- The volume and variety of data that organizations have been collecting and producing have been growing exponentially and show no sign of slowing down.
- At the same time, business landscapes and models are evolving, and users and stakeholders are becoming more and more data centric, with maturing expectations and demands.
Our Advice
Critical Insight
- As the CDO or equivalent data leader in your organization, a robust and comprehensive data strategy is the number one tool in your toolkit for delivering on your mandate of creating measurable business value from data.
- A data strategy should never be formulated disjointed from the business. Ensure the data strategy aligns with the business strategy and supports the business architecture.
- Building and fostering a data-driven culture will accelerate and sustain adoption of, appetite for, and appreciation for data and hence drive the ROI on your various data investments.
Impact and Result
- Formulate a data strategy that stitches all of the pieces together to better position you to unlock the value in your data:
- Establish the business context and value: Identify key business drivers for executing on an optimized data strategy, build compelling and relevant use cases, understand your organization’s culture and appetite for data, and ensure you have well-articulated vision, principles, and goals for your data strategy
- Ensure you have a solid data foundation: Understand your current data environment, data management enablers, people, skill sets, roles, and structure. Know your strengths and weakness so you can optimize appropriately.
- Formulate a sustainable data strategy: Round off your strategy with effective change management and communication for building and fostering a data-driven culture.
Member Testimonials
After each Info-Tech experience, we ask our members to quantify the real-time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this blueprint and what our clients have to say.
9.3/10
Overall Impact
$46,033
Average $ Saved
28
Average Days Saved
Client
Experience
Impact
$ Saved
Days Saved
Société des Mines d’ITY S.A.
Guided Implementation
9/10
$31,499
10
University of Prince Edward Island
Guided Implementation
8/10
$5,000
6
USAF AETC/A3/6FS
Workshop
10/10
$94,499
60
The Pittsburgh Water and Sewer Authority
Guided Implementation
10/10
N/A
N/A
Northeast Credit Union
Workshop
10/10
$12,599
9
De Novo Energy Limited
Workshop
9/10
$69,299
20
BC Energy Regulator
Workshop
8/10
$50,000
20
Heartland Food Products Group
Workshop
10/10
N/A
N/A
MTA Metropolitan Transportation Authority
Workshop
10/10
$94,499
65
Hawaii Gas
Guided Implementation
7/10
$11,339
5
Starkey Hearing Technologies
Guided Implementation
10/10
N/A
12
Twin Disc
Guided Implementation
10/10
N/A
N/A
Wolf & Company, P.C.
Workshop
8/10
N/A
N/A
Fayetteville State University
Workshop
10/10
$125K
120
GS1 Australia Ltd
Workshop
9/10
$87,999
20
Nieuport Aviation
Guided Implementation
10/10
$9,000
4
State of New Mexico Early Childhood & Care Department
Guided Implementation
10/10
$37,799
50
New South Wales Department of Education
Guided Implementation
10/10
$87,999
5
Kansas City Chiefs Football Club
Workshop
10/10
N/A
N/A
Washington Department of Fish and Wildlife
Workshop
9/10
$31,499
50
Codex LTD
Guided Implementation
10/10
$14,489
32
LiveBetter
Guided Implementation
8/10
$8,799
5
SWCA Environmental Consultants
Guided Implementation
9/10
$12,599
5
Faegre Drinker Biddle & Reath LLP
Workshop
10/10
$251K
50
Utah Transit Authority
Workshop
9/10
$125K
120
Goodwill Industries of Middle Tennessee
Guided Implementation
8/10
$31,499
44
Government of Northwest Territories – Health and Social Services Authority
Workshop
10/10
$400K
120
Ballard Spahr
Guided Implementation
10/10
N/A
N/A
Accord Financial Corp.
Guided Implementation
9/10
$125K
95
Central California Alliance for Health
Guided Implementation
8/10
$34,649
55
Workshop: Build a Robust and Comprehensive Data Strategy
Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
Module 1: Establish Business Context and Value: Understand the Current Business Environment
The Purpose
- Establish the business context for the business strategy.
Key Benefits Achieved
- Substantiates the “why” of the data strategy.
- Highlights the organization’s goals, objectives, and strategic direction the data must align with.
Activities
Outputs
Data Strategy 101
Intro to Tech’s Data Strategy Framework
Data Strategy Value Proposition: Understand stakeholder’s strategic priorities and the alignment with data
- Business context; strategic drivers
Discuss the importance of vision, mission, and guiding principles of the organization’s data strategy
- Data strategy guiding principles
- Sample vision and mission statements
Understand the organization’s data culture – discuss Data Culture Survey results
- Data Culture Diagnostic Results Analysis
Examine Core Value Streams of Business Architecture
Module 2: Business-Data Needs Discovery: Key Business Stakeholder Interviews
The Purpose
- Build use cases of demonstrable value and understand the current environment.
Key Benefits Achieved
- An understanding of the current maturity level of key capabilities.
- Use cases that represent areas of concern and/or high value and therefore need to be addressed.
Activities
Outputs
Conduct key business stakeholder interviews to initiate the build of high-value business-data cases
- Initialized high-value business-data cases
Module 3: Understand the Current Data Environment & Practice: Analyze Data Capability and Practice Gaps and Develop Alignment Strategies
The Purpose
- Build out a future state plan that is aimed at filling prioritized gaps and that informs a scalable roadmap for moving forward on treating data as an asset.
Key Benefits Achieved
- A target state plan, formulated with input from key stakeholders, for addressing gaps and for maturing capabilities necessary to strategically manage data.
Activities
Outputs
Understand the current data environment: data capability assessment
- Data capability assessment and roadmapping tool
Understand the current data practice: key data roles, skill sets; operating model, organization structure
Plan target state data environment and data practice
Module 4: Align Business Needs with Data Implications: Initiate Roadmap Planning and Strategy Formulation
The Purpose
- Consolidate business and data needs with consideration of external factors as well as internal barriers and enablers to the success of the data strategy. Bring all the outputs together for crafting a robust and comprehensive data strategy.
Key Benefits Achieved
- A consolidated view of business and data needs and the environment in which the data strategy will be operationalized.
- An analysis of the feasibility and potential risks to the success of the data strategy.
Activities
Outputs
Analyze gaps between current- and target-state
- Data Strategy Next Steps Action Plan
Initiate initiative, milestone and RACI planning
Working session with Data Strategy Owner
- Relevant data strategy related templates (example: data practice patterns, data role patterns)
- Initialized Data Strategy on-a-Page
Build a Robust and Comprehensive Data Strategy
Key to building and fostering a data-driven culture.
ANALYST PERSPECTIVE
Data Strategy: Key to helping drive organizational innovation and transformation
"In the dynamic environment in which we operate today, where we are constantly juggling disruptive forces, a well-formulated data strategy will prove to be a key asset in supporting business growth and sustainability, innovation, and transformation.
Your data strategy must align with the organization’s business strategy, and it is foundational to building and fostering an enterprise-wide data-driven culture."
Crystal Singh,
Director – Research and Advisory
Info-Tech Research Group
Our understanding of the problem
This Research is Designed For:
- Chief data officers (CDOs), chief architects, VPs, and digital transformation directors and CIOs who are accountable for ensuring data can be leveraged as a strategic asset of the organization.
This Research Will Help You:
- Put a strategy in place to ensure data is available, accessible, well integrated, secured, of acceptable quality, and suitably visualized to fuel decision making by the organizations’ executives.
- Align data management plans and investments with business requirements and the organization’s strategic plans.
- Define the relevant roles for operationalizing your data strategy.
This Research Will Also Assist:
- Data architects and enterprise architects who have been tasked with supporting the formulation or optimization of the organization’s data strategy.
- Business leaders creating plans for leveraging data in their strategic planning and business processes.
- IT professionals looking to improve the environment that manages and delivers data.
This Research Will Help Them:
- Get a handle on the current situation of data within the organization.
- Understand how the data strategy and its resulting initiatives will affect the operations, integration, and provisioning of data within the enterprise.
Executive Summary
Situation
- The volume and variety of data that organizations have been collecting and producing have been growing exponentially and show no sign of slowing down. At the same time, business landscapes and models are evolving, and users and stakeholders are becoming more and more data centric, with maturing and demanding expectations.
Complication
- As organizations pivot in response to industry disruptions and changing landscapes, a reactive and piecemeal approach leads to data architectures and designs that fail to deliver real and measurable value to the business.
- Despite the growing focus on data, many organizations struggle to develop a cohesive business-driven strategy for effectively managing and leveraging their data assets.
Resolution
Formulate a data strategy that stitches all of the pieces together to better position you to unlock the value in your data:
- Establish the business context and value: Identify key business drivers for executing on an optimized data strategy, build compelling and relevant use cases, understand your organization’s culture and appetite for data, and ensure you have well-articulated vision, principles, and goals for your data strategy.
- Ensure you have a solid data foundation: Understand your current data environment, data management enablers, people, skill sets, roles, and structure. Know your strengths and weakness so you can optimize appropriately.
- Formulate a sustainable data strategy: Round off your strategy with effective change management and communication for building and fostering a data-driven culture.
Info-Tech Insight
- As the CDO or equivalent data leader in your organization, a robust and comprehensive data strategy is the number one tool in your toolkit for delivering on your mandate of creating measurable business value from data.
- A data strategy should never be formulated disjointed from the business. Ensure the data strategy aligns with the business strategy and supports the business architecture.
- Building and fostering a data-driven culture will accelerate and sustain adoption of, appetite for, and appreciation for data and hence drive the ROI on your various data investments.
Why do you need a data strategy?
Your data strategy is the vehicle for ensuring data is poised to support your organization’s strategic objectives.
The dynamic marketplace of today requires organizations to be responsive in order to gain or maintain their competitive edge and place in their industry.
Organizations need to have that 360-degree view of what’s going on and what’s likely to happen.
Disruptive forces often lead to changes in business models and require organizations to have a level of adaptability to remain relevant.
To respond, organizations need to make decisions and should be able to turn to their data to gain insights for informing their decisions.
A well-formulated and robust data strategy will ensure that your data investments bring you the returns by meeting your organization’s strategic objectives.
Organizations need to be in a position where they know what’s going on with their stakeholders and anticipate what their stakeholders’ needs are going to be.
A data strategy is needed and is relevant across industries
Data and its associated data strategy is not only relevant for profit-generating industries.
The bottom line: data is valuable to everyone.
Regardless of the industry in which you operate, data is still the key to informed decision making. So whether you’re a nonprofit, a government entity, or in any other industry, data still empowers you to make better decisions.
The data strategy will serve as the mechanism for making good-quality and well-governed data readily available and accessible to deliver on your organizational mandate.
Whether your key stakeholders are customers, students, patients, donors, residents, or citizens, an effective data strategy will ensure you have the right data to make the right decisions in the interests of your stakeholder groups.
“Data have the power to enable the government to make better decisions, design better programs and deliver more effective services.”
“But for this to occur— and for us to share data in a way that allows other governments, businesses, researchers and the not-for-profit sector to also extract value from data—we need to refresh our approach.”– Privy Council Office, Government of Canada
Data cannot be fully leveraged without a cohesive strategy
Most organizations today will likely have some form of data management in place, supported by some of the common roles such as DBAs and data analysts.
Most will likely have a data architecture that supports some form of reporting.
Some may even have a chief data officer (CDO), a senior executive who has a seat at the C-suite table.
These are all great assets as a starting point BUT without a cohesive data strategy that stitches the pieces together and:
- Effectively leverages these existing assets
- Augments them with additional and relevant key roles and skills sets
- Optimizes and fills in the gaps around your current data management enablers and capabilities for the growing volume and variety of data you’re collecting
- Fully caters to real, high-value strategic organizational business needs
you’re missing the mark – you are not fully leveraging the incredible value of your data.
Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions

Organizational drivers for a data strategy
Your data strategy needs to align with your organizational strategy.
Main Organizational Strategic Drivers:
- Stakeholder Engagement/Service Excellence
- Product and Service Innovations
- Operational Excellence
- Privacy, Risk, and Compliance Management
“The companies who will survive and thrive in the future are the ones who will outlearn and out-innovate everyone else. It is no longer ‘survival of the fittest’ but ‘survival of the smartest.’ Data is the element that both inspires and enables this new form of rapid innovation.” – Joel Semeniuk, 2016
A sound data strategy is the key to unlocking the value in your organization’s data.
Data should be at the foundation of your organization’s evolution.
The transformational insights that executives are constantly seeking to leverage can be unlocked with a data strategy that makes high-quality, well-integrated, trustworthy, relevant data readily available to the business users who need it.
Whether hoping to gain a better understanding of your business, trying to become an innovator in your industry, or having a compliance and regulatory mandate that needs to be met, any organization can get value from its data through a well-formulated, robust, and cohesive data strategy.
According to a leading North American bank, “More than one petabyte of new data, equivalent to about 1 million gigabytes” is entering the bank’s systems every month. – The Wall Street Journal, 2019
“Although businesses are at many different stages in unlocking the power of data, they share a common conviction that it can make or break an enterprise.”– Jim Love, ITWC CIO and Chief Digital Officer, IT World Canada, 2018
Data is a strategic organizational asset and should be treated as such
The expression “Data is an asset” or any other similar sentiment has long been heard.
With such hype, you would have expected data to have gotten more attention in the boardrooms. You would have expected to see its value reflected on financial statements as a result of its impact in driving things like acquisition, retention, product and service development and innovation, market growth, stakeholder satisfaction, relationships with partners, and overall strategic success of the organization.
The time has surely come for data to be treated as the asset it is.
“Paradoxically, “data” appear everywhere but on the balance sheet and income statement.”– HBR, 2018
“… data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset.”– “5 Essential Components of a Data Strategy,” SAS
According to Anil Chakravarthy, who is the CEO of Informatica and has a strong vantage point on how companies across industries leverage data for better business decisions, “what distinguishes the most successful businesses … is that they have developed the ability to manage data as an asset across the whole enterprise.”– McKinsey & Company, 2019
How data is perceived in today’s marketplace
Data is being touted as the oil of the digital era…
But just like oil, if left unrefined, it cannot really be used.
"Data is the new oil." – Clive Humby, Chief Data Scientist
Source: Joel Semeniuk, 2016
Enter your data strategy.
Data is being perceived as that key strategic asset in your organization for fueling innovation and transformation.
Your data strategy is what allows you to effectively mine, refine, and use this resource.
“The world’s most valuable resource is no longer oil, but data.”– The Economist, 2017
“Modern innovation is now dependent upon this data.”– Joel Semeniuk, 2016
“The better the data, the better the resulting innovation and impact.”– Joel Semeniuk, 2016
What is it in it for you? What opportunities can data help you leverage?
GOVERNMENT
Leveraging data as a strategic asset for the benefit of citizens.
- The strategic use of data can enable governments to provide higher-quality services.
- Direct resources appropriately and harness opportunities to improve impact.
- Make better evidence-informed decisions and better understand the impact of programs so that funds can be directed to where they are most likely to deliver the best results.
- Maintain legitimacy and credibility in an increasingly complex society.
- Help workers adapt and be competitive in a changing labor market.
- A data strategy would help protect citizens from the misuse of their data.
Source: Privy Council Office, Government of Canada, 2018
What is it in it for you? What opportunities can data help you leverage?
FINANCIAL
Leveraging data to boost traditional profit and loss levers, find new sources of growth, and deliver the digital bank.
- One bank used credit card transactional data (from its own terminals and those of other banks) to develop offers that gave customers incentives to make regular purchases from one of the bank’s merchants. This boosted the bank’s commissions, added revenue for its merchants, and provided more value to the customer (McKinsey & Company, 2017).
- In terms of enhancing productivity, a bank used “new algorithms to predict the cash required at each of its ATMs across the country and then combined this with route-optimization techniques to save money” (McKinsey & Company, 2017).
A European bank “turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the bank.” The resulting understanding “gave rise to a targeted campaign that reduced churn by 15 percent” (McKinsey & Company, 2017).
A leading Canadian bank has built a marketplace around their data – they have launched a data marketplace where they have productized the bank’s data. They are providing data – as a product – to other units within the bank. These other business units essentially represent internal customers who are leveraging the product, which is data.
Through the use of data and advanced analytics, “a top bank in Asia discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.” Several sets of big data were explored, including “customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data” (McKinsey & Company, 2017).
What is it in it for you? What opportunities can data help you leverage?
HEALTHCARE
Leveraging data and analytics to prevent deadly infections
The fifth-largest health system in the US and the largest hospital provider in California uses a big data and advanced analytics platform to predict potential sepsis cases at the earliest stages, when intervention is most helpful.
Using the Sepsis Bio-Surveillance Program, this hospital provider monitors 120,000 lives per month in 34 hospitals and manages 7,500 patients with potential sepsis per month.
Collecting data from the electronic medical records of all patients in its facilities, the solution uses natural language processing (NLP) and a rules engine to continually monitor factors that could indicate a sepsis infection. In high-probability cases, the system sends an alarm to the primary nurse or physician.
Since implementing the big data and predictive analytics system, this hospital provider has seen a significant improvement in the mortality and the length of stay in ICU for sepsis patients.
At 28 of the hospitals which have been on the program, sepsis mortality rates have dropped an average of 5%.
With patients spending less time in the ICU, cost savings were also realized. This is significant, as sepsis is the costliest condition billed to Medicare, the second costliest billed to Medicaid and the uninsured, and the fourth costliest billed to private insurance.
Source: SAS, 2019
What is it in it for you? What opportunities can data help you leverage?
RETAIL
Leveraging data to better understand customer preferences, predict purchasing, drive customer experience, and optimize supply and demand planning.
Netflix is an example of a big brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects large amounts of data. If you are a subscriber, you are likely familiar with their suggestions messages of the next series or movie you should catch up on. These suggestions are based on your past search data and watch data. This data provides Netflix with insights into your interests and preferences for viewing (Mentionlytics, 2018).
“For the retail industry, big data means a greater understanding of consumer shopping habits and how to attract new customers.”– Ron Barasch, Envestnet | Yodlee, 2019
The business case for data – moving from platitudes to practicality
When building your business case, consider the following:
- What is the most effective way to communicate the business case to executives?
- How can CDOs and other data leaders use data to advance their organizations’ corporate strategy?
- What does your data estate look like? Are you looking to leverage and drive value from your semi-structured and unstructured data assets?
- Does your current organizational culture support a data-driven one? Does the organization have a history of managing change effectively?
- How do changing privacy and security expectations alter the way businesses harvest, save, use, and exchange data?
“We’re the converted … We see the value in data. The battle is getting executive teams to see it our way.”– Ted Maulucci, President of SmartONE Solutions Inc. IT World Canada, 2018
Where do you stack up? What is your current data management maturity?
Info-Tech’s IT Maturity Ladder denotes the different levels of maturity for an IT department and its different functions. What is the current state of your data management capability?
Info-Tech Insight
You are best positioned to successfully execute on a data strategy if you are currently at or above the Trusted Operator level. If you find yourself still at the Unstable or Firefighter stage, your efforts are best spent on ensuring you can fulfill your day-to-day data and data management demands. Improving this capability will help build a strong data management foundation.
Guiding principles of a data strategy
Value of Clearly Defined Data Principles
- Guiding principles help define the culture and characteristics of your practice by describing your beliefs and philosophy.
- Guiding principles act as the heart of your data strategy, helping to shape initiative plans and day-to-day behaviors related to the use and treatment of the organization’s data assets.
“Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes.”– McKinsey, 2018
Build a Robust and Comprehensive Data Strategy

Follow Info-Tech’s methodology for effectively leveraging the value out of your data
Some say it’s the new oil. Or the currency of the new business landscape. Others describe it as the fuel of the digital economy. But we don’t need platitudes — we need real ways to extract the value from our data. – Jim Love, CIO and Chief Digital Officer, IT World Canada, 2018

Our practical step-by-step approach helps you to formulate a data strategy that delivers business value.
- Establish Business Context and Value: In this phase, you will determine and substantiate the business drivers for optimizing the data strategy. You will identify the business drivers that necessitate the data strategy optimization and examine your current organizational data culture. This will be key to ensuring the fruits of your optimization efforts are being used. You will also define the vision, mission, and guiding principles and build high-value use cases for the data strategy.
- Ensure You Have a Solid Data and Resources Foundation: This phase will help you ensure you have a solid data and resources foundation for operationalizing your data strategy. You will gain an understanding of your current environment in terms of data management enablers and the required resources portfolio of key people, roles, and skill sets.
- Formulate a Sustainable Data Strategy: In this phase, you will bring the pieces together for formulating an effective data strategy. You will evaluate and prioritize the use cases built in Phase 1, which summarize the alignment of organizational goals with data needs. You will also create your strategic plan, considering change management and communication.
Info-Tech offers various levels of support to best suit your needs
DIY Toolkit
“Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful.”
Guided Implementation
“Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track.”
Workshop
“We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place.”
Consulting
“Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project.”
Diagnostics and consistent frameworks are used throughout all four options.
Build a robust and comprehensive data strategy – project overview

Phase 1
Establish Business Context and Data Value
Phase 1 will help you to establish the business context and value of your data strategy
In this phase, you will determine and substantiate the business drivers for optimizing the data strategy.
- Identify the business drivers that necessitate or support the case for data strategy optimization.
- Understand your current organizational data culture, as this will be key to ensuring the fruits of your optimization efforts are being used.
- Establish the vision and mission and define the principles and goals of your organization’s data strategy.
- Build high-value use cases for informing the business case for the data strategy.
"To stay competitive, we need to become more data driven. Compliance pressures are becoming more demanding. We need to add a new functionality."
Leverage the Data Strategy Stakeholder Interview Guide Template for gathering business context findings from key stakeholders.
Phase 1, Step 1: Identify Your Business Drivers for the Data Strategy
This step will walk you through the following activities:
- Understand common organizational business drivers
- Identify the business drivers, goals, and objectives that necessitate optimizing data in your organization
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
- An understanding of the business’ use of data in day-to-day functions
- An understanding of the business’ vision, goals, objectives, KPIs, and strategic priorities as well as the business drivers that will necessitate optimizing the data strategy
Data strategy optimization should be driven by business goals
Data is an enabler of the business. Data strategy optimization therefore needs to be driven by business goals and objectives.
Common Business Drivers
- Stakeholder Experience/Service Excellence
- Product and Service Innovations
- Operational Excellence and Efficiency
- Risk and Compliance Management
Deconstructing common business drivers
In the face of continuously changing business models, organizations of today are looking at their data as a source of competitive advantage.
1. Stakeholder Experience/Service Excellence
As an organization, your current focus is on improving your stakeholder experience and striving for service excellence, whether by offering highly tailored products or services, upselling, cross-selling, sending targeted communication, or building customer/client loyalty levels.
For every organization, stakeholders are going to be different.
For instance, in retail, key stakeholders include customers, suppliers, partners, and employees.
In healthcare, they include patients, physicians, nurses, staff, suppliers, pharmaceutical firms, insurance providers, and the government.
In education, they include students, faculty, staff, researchers, the community, and boards.
In government, they include residents, citizens, the community, unions, and governing bodies.
2. Product and Service Innovations
In order to maintain or establish your competitive edge, your organization is looking to become innovative in the product(s) and service(s) that you offer.
As an organization, you’re seeking to differentiate through product or service innovation. You’re inventing and adapting to keep pace and/or get ahead of changing customer and stakeholder preferences by understanding purchasing habits, consumption, behaviors, more varied and larger data sets, IoT, and other disruptive forces.
3. Operational Excellence and Efficiency
As an organization, you’re focused on optimizing your operational excellence and efficiency to ensure you are delivering high-quality products or services in the most cost-effective manner.
This may mean your focus is on optimizing your ordering, production, and fulfillment processes. Or you may be working on the efficiency of your operations, making them leaner, reducing waste, and optimizing resource utilization, all of which can contribute to lower costs and higher profit margins.
4. Risk and Compliance Management
As an organization, you’re operating in a highly regulated industry or you may be a government entity (federal, state, provincial, municipal) and mandated to meet certain regulatory requirements. Your data strategy optimization may therefore be in response to changes in the existing regulatory/compliance landscape.
Risk mitigation is also another driver for formalizing or optimizing a data strategy. Your current practices and environment may be outdated, leading to potential exposure to risk.
Begin your data strategy planning by first ensuring you have a deep understanding of the business
Develop a deep understanding of the business, its goals, and its plans. This understanding will ensure that the data strategy planning and the resulting outcomes around data directly align with business needs and value and are in support of key strategic priorities.
Key Items to Consider:
What are the driving forces behind changes and decisions within the business?
- Stakeholder Experience/Service Excellence
- Product and Service Innovations
- Operational Excellence and Efficiency
- Risk and Compliance Management
Questions for your business stakeholders:
- Is the organization’s business model changing? (Are we an organization that grows by acquisitions, are we expanding operations in to a new jurisdiction?)
- Are business operations evolving and changing?
- Are regulations causing the organization to re-evaluate how data is used and managed by the business?
Determine and document the business context of the data strategy
1.1
Estimated Time: Variable time commitment based on the number of interviews performed
Instructions:
- Develop a firm understanding of the strategic plans and goals of the business. Before proceeding to understand the data environment, ensure you understand the business’ vision, goals, objectives, KPIs, strategies, priorities, and key drivers. (Leverage your project or executive sponsor to streamline this information-gathering process.)
- Use the techniques outlined below to support you in establishing a clear business context and alignment as you embark on your data strategy initiative.
- Record findings in the Data Strategy Stakeholder Interview Guide and Findings document.
OUTPUT
Use the findings of your analysis of the business to identify:
- Organizational vision, goals, objectives, KPIs
- Strategic priorities
- Business drivers
Recommended Techniques
Interview business stakeholders. (See Activity 1.1a for support.)
Review key business artifacts. Typical review inputs include:
- Business vision and mandate
- Three-to-five-year roadmaps, department-level business plans, audit reports
Info-Tech Insight
Prevent the interviews from becoming a platform for the business to complain about data. Open the discussion by having them share about their current use of data, then switch gears to finding out what they would like to do with data and how they see data supporting their strategic plans.
Interview business stakeholders
1.1a
Estimated Time: Average of 1 hour per interview session
Objective
Increase the team’s understanding of organizational strategic plans and how the organization would benefit from data as a strategic enabler.
Activity: Individual and small-group interviews with business stakeholders
Instructions:
- Identify members of the business to interview to understand their current and desired data usage. (Try to interview as many lines of business as possible to create a more comprehensive picture.)
- Leverage the interview questions in the Data Strategy Stakeholder Interview Guide and Findings template as a starting point.
- Interview the identified members of the business.
- Debrief and document results in the Data Strategy Stakeholder Interview Guide and Findings document.
Data strategy optimization must be driven by business need.
Data Strategy Stakeholder Interview Guide and Findings
INPUT
- Data Strategy Stakeholder Interview Guide, business stakeholders
OUTPUT
- Identified business drivers that apply to your organization.
Materials
- Data Strategy Stakeholder Interview Guide and Findings Template
Participants
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Phase 1, Step 2: Understand Your Organization’s Data Culture
This step will walk you through the following activities:
- Understand the organizational data culture
- Understand users’ appetite for data
- Understand appreciation of data in terms of governance, quality, accessibility, ownership, and stewardship
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders (data owners, data stewards)
Outcomes of this step:
- An understanding of the current culture as it relates to the use and consumption of data
- An understanding of whether data is currently perceived to be an asset at the organization
Conduct Data Culture Survey
1.2
Estimated Time: Average of 1 hour per respondent
Objective
- Increase the team’s understanding of the organizational data culture, users’ appetite for data, and their appreciation of data in terms of governance, quality, accessibility, ownership and stewardship
Activity: Stakeholder survey
Instructions:
- Identify members of the data user base, data consumers, and other key stakeholders for surveying.
- Conduct an information session to introduce the Data Culture Survey diagnostic. Explain the objective and importance of the survey and its role in helping to understand the current data culture and inform the improvement of that culture.
- Roll out the Info-Tech Data Culture Survey diagnostic to the identified users and stakeholders.
- Debrief and document results/scorecard in the Data Strategy Stakeholder Interview Guide and Findings document.
A data-driven culture is an indicator of data being treated as an asset.

“Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes.” – McKinsey, 2018
INPUT
- Data Culture Survey diagnostic, business stakeholders
OUTPUT
- Understanding of current data culture
Materials
- Data Culture Survey
- Data Strategy Stakeholder Interview Guide and Findings Template
Participants
- Data architect
- Enterprise architect
- Business analyst
- Senior leaders and business stakeholders
Phase 1, Step 3: Vision, Mission, and Guiding Principles of Your Organization’s Data Strategy
This step will walk you through the following activities:
- Establish the vision, mission, and guiding principles of your organization’s data strategy
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
- Vision and mission of your organization’s data strategy
- Defined principles and goals of your organization’s data strategy
Create compelling vision and mission statements for the organization’s data strategy
A vision represents the way your organization intends to be in the future.
A clear vision statement helps align the entire organization to the same end goal.
Your vision should be brief, concise, and inspirational. It is attempting to say a lot in a few words, so be very thoughtful with the words you choose. Consider your IT department’s strengths, the customers of your IT services, and your current/future commitments to service quality.
Remember that a vision statement is internally facing for other members of your company throughout the process.
"A vision is a picture of the future you seek to create, described in the present tense, as if it were happening. A statement of our vision shows where we want to go and what we will be like when we get there." – Senge et al. 1994
A mission expresses why you exist.
While your vision is a declaration of where your organization aspires to be in the future, your mission statement should communicate the fundamental purpose of the data management practice.
It identifies the function of IT, what it produces, and its high-level goals that are linked to delivering timely, high-quality, relevant, and valuable data to business processes and end users. Consider if the practice is responsible for providing data for analytical and/or operational use cases.
A mission statement should be concise and provide a clear statement of purpose for both internal and external stakeholders.
"The mission statement provides a valuable starting point for establishing, afterwards, more specific objectives and strategies" – Hannagan, 2002
Craft your vision and mission statements
1.3.1
Estimated Time: 2 hours
Overview
Gather a collection of project stakeholders and project team members together to create consensus on a singular vision and mission for the organization’s data strategy.
Instructions
Get the conversation started:
Ask everyone to complete the following sentence:
- Five years from now, our data landscape will include ______________.
- Have each participant create a statement of purpose (1-5 lines) describing the future data management practice. Have them consider the following:
- Vision:
- What does an organization with an effective high-value data strategy look like?
- How will our organization benefit and grow from an improved data strategy?
- What are our customers saying, feeling, and doing? Reflect on current state data collection.
- Mission:
- Why does this program exist?
- What problems are we trying to solve?
- Who will benefit from this program?
- How will we reach our target?
- Vision:
- Ask each participant to present their vision and mission. Discuss common themes and then develop a concise vision statement that incorporates the group’s ideas.
- Consolidate the findings and document the results.
INPUT
- Business context as derived from stakeholder interview session and input
OUTPUT
- The vision and mission statements for the data strategy
Materials
- Whiteboard
Participants
- Data architect
- Enterprise architect
- Business analyst
- Senior leaders and business stakeholders
Guiding principles for your organization’s data strategy
The Value of Clearly Defined Data Principles
- Guiding principles help define the culture and characteristics of your practice by describing your beliefs and philosophy.
- Guiding principles act as the heart of your data management – helping to shape initiative plans and day-to-day behaviors related to data management and treatment of the organization’s data assets.
Examples:
Principle #1
The organization’s data supports fact-based decision making.
Principle #2
Data is comprehensively integrated.
Principle #3
Data is appropriately accessible and available to support timely consumption and insight generation.
Principle #4
Quality of data will be measured, maintained, and managed.
Principle #5
Data definitions are consistent and are maintained and managed to support data users.
Principle #6
Data owners and data stewards are accountable and responsible for their domains.
Principle #7
Data is managed (curated, retained, archived, disposed) across its lifecycle.
Principle #8
Data is appropriately secured across its lifecycle.
Principle #9
Data is governed.
Info-Tech Insight
Take the time to craft your guiding principles. They are shared, long-lasting beliefs that will guide your treatment of, investment in, and decisions related to data and data management. Devise a set of guiding principles that speak to your organization’s data strategy and your current or desired culture.
Create guiding principles for your organization’s data strategy
1.3.1
Estimated Time: 2 hours
Instructions:
- As a group, brainstorm a list of principles and values related to key business objectives of data and the goals of the data strategy and the data management practice. These will help you develop the practice’s ultimate principles. Attempt to brainstorm between five and ten values.
- Divide into small groups, with each group taking one of the values. For each value, determine:
- Why is it important to the data strategy?
- How will it help to improve the delivery, use, quality, or management of data?
- Complete one of the following sentences:
- Data is…
- The business is able to…
- The data strategy…
- Get back together as a group to discuss the principles:
- How can these principles help to guide the practice’s planning?
- How will these principles help to correct and guide staff behavior?
- Finalize and document your guiding principles.
What is the business looking to get from its data? | How is the data strategy going to deliver the business needs? |
---|---|
Quality data? Accessible data? Timely data? |
Integrate varied data sets for improved decision making? Create a scalable environment to accommodate growth? Create a culture of accountability and stewardship? |
INPUT
- Business context as derived from stakeholder interview session and input
OUTPUT
- Guiding principles
Materials
- Whiteboard
Participants
- Data architect
- Enterprise architect
- Business analyst
- Senior leaders and business stakeholders
Phase 1, Step 4: Build High-Value Use Cases for Informing the Business Case for Data Strategy
This step will walk you through the following activities:
- Brainstorm top data challenges, risks, and opportunities
- Formulate problem statements that articulate business needs
- Examine how the need is filled today, if at all, and what the future state looks like
- Identify relevant metrics and KPIs for measuring success
- Build data flow of the current state
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders (data owners and data stewards)
Outcomes of this step
- Use cases that highlight business needs and/or opportunities
Build high-value use cases for informing the business case for data strategy
Rounding out the business context for the data strategy is the very important exercise of creating high-value use cases.
Hold focused conversations and conduct deep-dive sessions with business stakeholders to identify high-priority initiatives for your organization’s data strategy. Leverage Info-Tech’s data requirements and mapping methodology for creating use cases.
Bring data owners, data stewards, business subject matter experts, and their IT partners or data custodians together to discuss and create use cases that represent the top business needs and priorities. If addressed, these will deliver value and support the strategic direction of the organization.
Include in these conversations current challenges, risks, and opportunities associated with the use of data across lines of business. Also explore which other stakeholder groups/lines of business will be impacted and how you will measure success.
Info-Tech Insight
Coming out of these sessions with use cases identified by your data owners, data stewards, and business SMEs will highlight the areas or processes top-of-mind for these stakeholders and their units. If addressed, these will deliver value to the organization as a whole.
Info-Tech’s framework on developing use cases for the data strategy
Objective: Business-needs gathering activity to highlight and create relevant use cases around data-related problems or opportunities that are clear and contained and if addressed will deliver value to the organization.
Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases
Breakout Session #1
- What is a number-one risk you need to alleviate?
- What is a number-one opportunity you wish to see happen?
- What is a number-one pain you have when working with data?
Once you identify a data-related business activity/process, define a problem statement.
Breakout Session #2
- What are your challenges in performing the activity today?
- What does “amazing” look like if we solve this perfectly?
- What other business unit activities/processes will be impacted/improved if we solve this?
- What compliance/regulatory/policy concerns do we need to consider in any solution?
- What measures of success/change should we use to prove the value of the effort (KPIs/ROI)?
Breakout #3
- What are the steps in the process/activity today?
- What are the applications/systems used at each step?
- What data elements (domains) are involved, created, used, or transformed at each step
The resulting use cases are to be prioritized and leveraged for informing the business case for the data strategy.
Build high-value use cases for informing the business case for data strategy
1.4
Estimated Time: Average of 2 hours per use case
Objective
Business-needs gathering activity to highlight and create relevant use cases around data-related problems or opportunities that are clear and contained and if addressed will deliver value to the organization.
Activity: Use-case activity with business stakeholders and data custodian (IT partner)
Instructions:
- Bring together key business stakeholders (data owner, stewards, SMEs) from a particular line of business as well the data custodian to build cases for their units.
- Leverage Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases.
- Have the stakeholders move through each breakout session, using flip charts to brainstorm and to document thoughts.
- Debrief and document results in the Data Strategy Use Case Template.
Repeat this exercise with as many lines of business as possible.
"Organizational appetite for having access to data has intensified, with specific departments having high demands and extensive use cases for data."
– Andy Woyzbun, Executive Advisor, Info-Tech Research Group
Data Strategy Use Case Template
INPUT
- Business stakeholders’ subject area expertise; data custodian systems, integration, and data knowledge
OUTPUT
- Identified business drivers that apply to your organization.
Materials
- Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases
Participants
- Data architect
- Enterprise architect
- Business analyst
- Data owners, data stewards, data custodians
Phase 2
Ensure You Have a Solid Data and Resources Foundation
Phase 2 will help you ensure you have a solid data and resources foundation for your data strategy
In this phase, you will focus on understanding and evaluating the foundational components of the data environment, resources and skills.
- Understand your current data environment: Data management enablers – data governance, data architecture, data operations, data quality management, data risk management
- Understand the required resources portfolio: Key people and skill sets
- Put things in perspective: determine your strengths, weaknesses, opportunities, and threats through a SWOT analysis.
"To stay competitive, we need to become more data-driven. Compliance pressures are becoming more demanding. We need to add a new functionality."
Phase 2, Step 1: Understand Your Current Data Environment
This step will walk you through the following activities:
- Understanding your current data environment: Data management enablers including data governance, data architecture, data operations, data quality management and data risk management
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step:
- An understanding of the foundational data management enablers that are key to operationalizing your data strategy
Optimized data management enablers means a strong foundation for the data strategy
Data Management Enablers:
Info-Tech categorizes data management enablers as the processes that guide the management of the organization’s data assets and support the delivery.
Data Management Enablers:
- Data Governance
- Data Architecture Management
- Data Operations Management
- Data Risk Management
- Data Quality Management
- Practice Evolution

Ensure you have a data management practice with strong process capabilities
What is data management?
Data management is the planning, execution, and oversight of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets (DAMA, 2009).

Govern and Direct
- Ensures data management practices and processes follow the standards and policies outlined for them
- Manages the executive oversight of the broader practice
Align and Plan
- Aligns data management plans to the business’ data requirements
- Creates the plans to guide the design and execution of data management components
Build, Acquire, Operate, Deliver, and Support
- Executes the operations that manage data as it flows through the business environment
- Manages the business’ risks in relation to its data assets and the level of security and access required
Monitor and Improve
- Analyzes the performance of data management components and the quality of business data
- Creates and executes plans to improve performance of the practice and quality and use of data assets
Overview of data management
What Is Data Management?
Data management is the planning, execution, and oversight of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets (DAMA, 2009).
Mission of a Data Management Practice
- Data services add direct value to business functions and processes.
- Data is accessible and used to improve the competitive position of the organization.
- Data usage and management does not expose the business to undue risk.
Performing effective data management requires a multifaceted approach that includes investments in people, processes, and technology.
Many components are included under the umbrella of data management, all working together to:
- Deliver data and allow it to support the data appetites of the business.
- Successfully support data through its lifecycle.
- Ensure it is appropriately treated as it flows through the organization’s environment.
Use the proceeding slides to build your understanding of the purpose, value, and capabilities associated with each component.
Data management enabler: Data governance
Governance is the structure for making decisions. (CIO Magazine)
Data governance is:
- An enabling framework of decision rights and accountabilities for information-related processes.
- Agreed-upon models that describe who can take what actions with what information, when, and using what methods. (The Data Governance Institute, 2014)
- True business-IT collaboration that will lead to increased consistency and confidence in decision making, which in turn increases innovation and growth.
If done correctly, data governance is not:
- An annoying, finger-waving roadblock in the way of getting things done.
- Meant to solve all data-related business or IT problems in an organization.
- A sweeping project that will clean up your data “once and for all.” (Accenture, 2011)
- A multiyear process that will cost a lot and have uncertain benefits.
Data governance is the central component of the data management framework
This research is created with reference to the Data Asset Management Association’s Data Management Book of Knowledge, Version 2 (DAMA DMBOK2).

Data management is the planning, execution, and oversight of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets (DAMA, 2017).
In other words, getting the right information to the right people at the right time.
The research in this blueprint will focus on data governance, the central idea of data management, without which the surrounding data management initiatives would have no structure.
Data governance directly complements all ten data management initiatives.
See Info-Tech’s Enable Shared Insights With an Effective Data Governance Engine blueprint for more information on data governance.
Data governance enables the business and IT to climb the data value chain together
After starting the data governance engine with the organization’s fuel – its data, information, and needs – the real power from the engine emerges when the program is implemented. Using Info-Tech’s methodology and extensive resources will help you get the power that you need out of your data governance engine. Together, this will help drive your organization up the data value chain to gain knowledge and shared insight from the business.

Data management enabler: Data architecture is an integral aspect of data management
The set of rules, policies, standards, and models that govern and define the type of data collected and how it is used, stored, managed, and integrated within the organization and its database systems.
In general, the primary objective of data architecture is the standardization of data for the benefit of the organization.
Myth
Data architecture is purely a model of the technical requirements of your data systems.
Reality
Data architecture is largely dependent on a human element. It can be viewed as “the bridge between defining strategy and its implementation.” - Erwin, 2016
Functions
A strong data architecture should:
- Define, visualize, and communicate data strategy to various stakeholders.
- Craft a data delivery environment.
- Ensure high data quality.
- Provide a roadmap for continuous improvement.
Business Value
A strong data architecture will help you:
- Align data processes with business strategy and the overall holistic enterprise architecture.
- Enable efficient flow of data with a stronger focus on quality and accessibility.
- Reduce the total cost of data ownership.
Data architecture is not a standalone concept; it fits into the more holistic design of enterprise architecture
Data Architecture in Alignment
Data architecture cannot be designed to simply address the focus of data specialists or even the IT department.
It must act as a key component in the all-encompassing enterprise architecture and reflect the strategy and design of the entire business.
Data architecture collaborates with application architecture in the delivery of effective information systems and informs technology architecture on data-related infrastructure requirements/considerations.
Please refer to the following blueprints to see the full picture of enterprise architecture:

Adapted from TOGAF
Refer to Phase C of TOGAF and Bizbok for references to the components of business architecture that are used in data architecture
Data architecture involves planning, communicating, and understanding technology
Data Architecture
A description of the structure and interaction of the enterprise’s major types and sources of data, logical data assets, physical data assets, and data management resources (TOGAF 9, 2011).
The subject area of data management that defines the data needs of the enterprise and designs the master blueprints to meet those needs (DAMA DMBOK, 2009).
IBM (2007) defines data architecture as the design of systems and applications that facilitate data availability and distribution across the enterprise.
Definitions vary slightly across major architecture and management frameworks.
However, there is a general consensus that data architecture provides organizations with:
- Alignment
- Planning
- Road Mapping
- Change Management
- A guide for the organization’s data management program
Data architecture must be based on business goals and objectives and developed within the technical strategies, constraints, and opportunities of the organization in support of providing a foundation for data management.
Info-Tech Insight
Data architecture is not just data models. Data architects must understand the needs of the business, as well as the existing people and processes that already exist in the organization, to effectively perform their job.
Capitalize on trends in data architecture before you determine the tactics that apply to you
Stop here. Before you begin to plan for optimization of the organization’s data environment, get a sense of the sustainability and scalability of the direction of the organization’s data architecture evolution.
Practically any trend in data architecture is driven by an attempt to solve one or more or the common challenges of today’s tumultuous data landscape, otherwise known as “big data.” Data is being produced in vast amounts, at very high speeds, and in a growing number of types and structures.
To meet these demands, which are not slowing down, you must keep ahead of the curve. Consider the internal and external catalysts that might fuel your organization’s need to modernize its data architecture:
Big Data | Data Storage | Advanced Analytics | Unstructured Data | Integration |
---|---|---|---|---|
Hadoop ecosystem The discussion about big data is no longer about what it is but about how businesses of all types can operationalize it. Is your organization currently capturing and leveraging big data? Are you looking to do so in the near future? |
The cloud The cloud offers economical solutions to many aspects of data architecture. Have you dealt with issues of lack of storage space or difficulties with scalability? Do you need remote access to data and tools? |
Real-time architecture Advanced analytics (machine learning, natural language processing) often require data in real-time. Consider Lambda and Kappa architectures. Has your data flow prevented you from automation, advanced analytics, or embracing the world of IoT? |
Graph databases Self-service data access allows more than just technical users to participate in analytics. NoSQL can uncover buried relationships in your data. Has your organization struggled to make sense of different types of unstructured data? |
Is ETL enough? What SQL is to NoSQL, ETL is to NoETL. Integration techniques are being created to address the high variety and velocity of data. Have your data scientists wasted too much time and resources in the ETL stage? |
The Five-Tier Data Architecture Model helps to understand your data and unlock true business insight
The framework below is a reference of a typical corporate data architecture, adapted from the DAMA Data Management Body of Knowledge (DMBOK), highlighting the role of business intelligence.
Use this as a guide moving forward.

Tiers 4 and 5 are where the true magic happens. This is where BI tools are essential for analyzing, visualizing, and making sense of your data. Without BI tools, the knowledge and insight locked in your data would remain hidden.
Data sources are any data structures that support the line of business applications. They can reside on many different platforms and can contain structured as well as unstructured data.

Data from siloed sources needs to come together in one place. Every database shares its information with a centralized location called a data warehouse. This is where different information from different departments gets linked together.
Data management enabler: Data operations management
Guiding Principles and Value
Operate, deliver, and support data to the organization’s data consumers and business areas.
Overview
The planning, control, and support for data assets across the data lifecycle, from creation and acquisition to archiving and purging (Data Management Book of Knowledge, 2009).
Objectives of Data Operations Management
- Implement and follow policies and procedures to manage data at each stage of its lifecycle.
- Maintain the technology supporting the flow and delivery of data (applications, databases, systems, etc.).
- Control the delivery of data within the system environment.
Indicators of Successful Data Operations Management
- Effective delivery of data assets to end users.
- Successful maintenance and performance of the technical environment that collects, stores, delivers, and purges organizational data.

This data management enabler has a heavy focus on the management and performance of data systems and applications.
This component works closely with the organization’s technical architecture in order to support successful data delivery and lifecycle management (e.g. data warehouses, repositories, databases, networks, etc.).
Data management enabler: Data risk management
Guiding Principles and Value
Operate, deliver, and support data to the organization’s data consumers and business areas.
Data risk management is responsible for ensuring data is protected and secure during its management, delivery, and disposition within the IT and business environments it resides in.
Overview
Data risk management ensures data assets are sufficiently protected and able to be accessed in secure and controlled manners throughout their lifecycle and as they sit at rest or in flight within the organization’s IT infrastructure and business environment.
Objectives of Data Risk Management
- Data is managed with consideration and adherence to the confidentiality, integrity, and availability needs of the business.
- A balance exists between managing risk from data usage and making data widely visible and available to the business.
- Data assets within the organization’s environment adhere to regulatory standards.
- The IT environment storing the data is proactively addressing data concerns and evolving its protection policies and management practices to address evolving technologies and new threats/concerns.
Critical Success Factors for Data Risk Management
- Sensitive data assets (e.g. PII data) and areas with high vulnerabilities and downside risks are classified and have controlled access and tight management.
- Security awareness training is present across the organization.
- Data is protected throughout its lifecycle.
- Decisions on technology usage and data access take into account security principles and incorporate security controls during their planning and implementation.
- The organization proactively tests its environment for security threats/weaknesses.
Data management enabler: Data quality management
Overview
Data quality encompasses planning, implementation, and control activities that apply quality management techniques to measure, assess, improve, and ensure the fitness of data for use (DAMA, 2009).
Consider
- What does data quality mean for your organization?
- How does your business determine data is of the necessary quality?
Value of Data Quality Management
Data quality and its management are integral to the performance and perception of the entire data management program:
- Aligns integrity and fitness of data to the business’s requirements
- Gauges the quality of data and the performance of the data management practices that support its delivery
Objectives of Data Quality Management
- Ensure usable, trustworthy data is available to the business and its processes.
- Analyze and recommend improvements to data management components to support improved data quality.
- Coordinate efforts between IT and the business that manage and maintain the quality of data assets on an ongoing basis.
- Investigate data quality issues, determining root cause and designing corrective plans that enable the maintained integrity of data assets.
Critical Success Factors for Effective Data Quality Management
- Activities of data quality are recognized as a business function that must be supported by executives and executed tactically by business and IT staff.
- Continual cleansing of data assets (automated and manual).
- Periodical reviews and improvements of the management practices for data.
Poor data quality develops due to multiple root causes
After you get to know the properties of good-quality data, understand the underlying causes of why a lack of those indicators can point to poor-quality data.
If you notice that the relevance, accuracy, timeliness, or usability of the organization’s data is suffering, one or more of the following root causes are likely plaguing your data:

Common root causes of poor data quality, through the lens of Info-Tech’s Five-Tier Data Architecture:
These root causes of poor data quality are difficult to avoid, not only because they are often generated at an organization’s beginning stages but also because change can be difficult. This means that the root causes are often propagated through stale or outdated business processes.
Consider the core IT capabilities and competencies of an effective data quality practice
Before addressing data quality issues in the organization, make sure that the competencies and capabilities necessary for properly fixing those issues are present in the organization’s IT department.
Quick Tip: Competencies vs. Capabilities
Being capable is different than being competent, and it is worth differentiating the two despite them often being used interchangeably.
A competency is a skill that you are functionally adequate at. When we evaluate competency, we are asking, “Who knows how to do [action], and how well do they know how to do it?”
A capability is a feature or faculty that can be developed and improved. When we evaluate capability, we are asking “Can we access and apply the competencies we need to get [action] done?”
– Innovation Management Services, 2008

Competencies and Capabilities of IT
The core competencies and capabilities of the IT department must be assessed and, if necessary, improved before addressing specific business data quality improvements. These include:
Competencies: IT competencies related to data quality include the ability of IT to functionally carry out data quality improvement techniques in databases and systems. These include data cleansing and data validation.
Capabilities: Related to the ability of IT to fulfill the needs of the business. This includes having business-informed data-quality-related policies and procedures, related roles and structure, the appropriate oversight and communication, and the appropriate technologies to measure and fix data quality issues.
Data quality management must be sustained for ongoing improvements to the organization’s data
Data quality management is a long-term commitment that shifts how an organization views, manages, and uses its corporate data assets. Long-term buy-in from all involved is critical.

- Data quality is never truly complete; it is a set of ongoing processes and disciplines that requires a permanent plan for monitoring practices, reviewing processes, and maintaining consistent data standards.
- Set the expectation for stakeholders that a long-term commitment is required to maintain quality data within the organization. This is critical to the success of the program.
- A data quality maintenance program will continually revise and fine-tune ongoing practices, processes, and procedures employed for organizational data management.
Data management enabler: Practice evolution
Monitor and improve the components and capacities of the practice over time.
Overview
The organization’s data management practice is continuously improved in order to better support the delivery of existing data assets and enable the business to respond to new opportunities and requirements related to the capture, management, and consumption of data.
Value of Practice Evolution
- Data is never static. Therefore, the practices managing and supporting it need to be dynamic and evolve.
- A continuous improvement mentality allows natural updates to occur and enables the organization to take advantage of new opportunities related to data.
Mission of Practice Evolution
- Ensure improvements to the capabilities of the data management practice are made over time.
- Evolve the capabilities of data management practices on both the sides of IT and the business.
- Ensure new data sources/types and business requirements trigger re-evaluations and improvements to practice capabilities.
Objectives and Methods to Incorporating Continuous Improvement Into Data Management
Short-Term and Long-Term Practice Planning
- Create and manage a data management roadmap.
- Conduct performance tuning on practice operations.
Improving Business and IT Processes
- Leverage business process transformations and process engineering to create more effective management and use of data in the business environment.
- Formalize and mature IT procedures and processes.
Respond to Evolving Data Usage and Data Types
- Re-evaluate capability requirements and practice performance against the organization’s data strategies and evolving business strategies.
- Identify how to effectively respond to the addition of new data sources and types.
Phase 2, Step 2: Key Resources – People and Skill Sets
This step will walk you through the following activities:
- Understand the typical roles and responsibilities for effective management of data
- Understand the key roles and responsibilities as they relate to the governance of data
This step involves the following participants:
- Data architect
- Enterprise architect
- Business stakeholders
Outcomes of this step
- An understanding of the key resources – people and skill sets – critical for operationalizing the data strategy and its associated initiatives
Key resources for your data strategy: People and skill sets
The right people and skill sets are another key component for an effective data strategy.
Some of the key roles for executing on your data strategy: The Management of Data
- Data Architects
- Database Administrators
- Data Engineers
- Data Integrators – ETL, ESB
- Data Analysts – Data quality tools, data rules
- Data Scientists
- Data Risk Officers
- Data Privacy Officers
- Data Security Officers
- Chief Data Officer
Some of the key roles for executing on your data strategy: The Governance of Data
- Data Steward
- Data Custodian
- Data Owner
- Data Governance Working Group
- Data Governance Steering Committee
- Data Governance Council
- Executive Sponsor
Key roles for operationalizing the data strategy
Data Engineer
Works with and analyzes data to generate reports to support business decisions.
- An individual or group of individuals who work with data to integrate, optimize, and support data with deep technical knowledge of underlying technologies.
- Could also be called database administrator (DBA), big data engineer, big data architect, etc., depending on the technology and platforms.
- Typically the first to experience data integration pains through slow or inaccurate data.
Business Analyst
Communicates with business to identify requirements and satisfaction. Demonstrates competencies for stakeholder management, analytical techniques, and the ability to “speak the language” of both the business and IT.
- Role that functions as the crucial link between the business and the IT roles responsible for designing, developing, and implementing data changes.
- The designated business analyst(s) for the project has responsibility for end-to-end requirements management.
- Works collaboratively with their counterparts in the business and IT (e.g. developer teams or procurement professionals) to ensure that the approved requirements are met in a timely and cost-effective manner.
Data Architect
Understands the data environment in a holistic manner and designs solutions. Has a greater knowledge of operational and analytical data use cases.
- Reviews project solution architectures and identifies cross impacts across the data lifecycle.
- Is a hands-on expert in data management and warehousing technologies.
- Facilitates the creation of the data strategy.
- Manages the enterprise data model.
- Has a greater knowledge of operational and analytical data use cases.
The data architect is one of the most important roles for operationalizing the data strategy
The data architect:
- Acts as a “translator” between the business and data workers to communicate data and technology requirements.
- Facilitates the creation of the data strategy.
- Manages the enterprise data model.
- Has a greater knowledge of operational and analytical data use cases.
- Recommends data management policies and standards and maintains data management artifacts.
- Reviews project solution architectures and identifies cross impacts across the data lifecycle.
- Is a hands-on expert in data management and warehousing technologies.
- Is not necessarily its own designated position but a role that can be completed by a variety of IT professionals.
Data architects bridge the gap between strategic and technical requirements.

The data architect must maintain a comprehensive view of the organization’s rapidly proliferating data.
As a data architect, you must maintain balance between the technical and the business requirements
The data architect role is integral to connecting the long-term goals of the business with how the organization plans to manage its data for optimal use.
Data architects need to have a deep experience in data management, data warehousing, and analytics technologies. At a high level, the data architect plans and implements an organization’s data, reporting, and analytics roadmap.
Some of the role’s primary duties and responsibilities include:
- Data modeling
- Reviewing existing data architecture
- Benchmarking and improving database performance
- Fine-tuning database and SQL queries
- Leading on ETL activities
- Validating data integrity across all platforms
- Managing underlying framework for data presentation layer
- Ensuring compliance with proper reporting to bureaus and partners
- Advising management on data solutions
Info-Tech Insight
The data architect role is not always clear cut. Many organizations do not have a dedicated data architect resource and may not need one. However, the duties and responsibilities of the data architect must be carried out to some degree by a combination of resources as appropriate to the organization’s size and environment.
Critical roles and responsibilities for data governance
Data Governance (DG) Council
Generally:
- Senior executive representatives operating at the organization’s strategic level
- Aligns organizational strategies and goals to data strategies and data management plans
- Supports top-down approach to DG, helps to champion/socialize DG, and supports adoption
Data Owners
Traditionally, data owners:
- Are ultimately accountable for all issues related to the data assets under their purview
- Are organizational leaders whose teams are heavy users of the data assets
- Review the permissions of user groups for different data sets
- Accountable for the quality of the data and whether it enables employees to perform their jobs efficiently
- Determine the institutional impact of changing permission statuses
- Understand the lifecycle of the data
Data Governance Steering Committee
Generally:
- Cross-functional body responsible for creating tactical plans
- Manages data- and practice-related issues
- Monitors and guides data and data governance initiatives
- Oversees performance and management of working groups
Critical roles and responsibilities for data governance
Data Governance Working Groups
- Working groups are the cross-functional teams that deliver on data governance projects, initiatives, and ad hoc review committees
Data Custodians:
Traditionally, data custodians:
- Serve on an operational level addressing issues related to data and database administration
- Support the management of access, quality, escalating issues, etc.
- Are subject matter experts from IT and database administration
Data Stewards
Traditionally, data stewards:
- Serve on an operational level addressing issues related to adherence to standards/procedures, monitoring data quality, raising issues identified, etc.
- Responsible for managing access, quality, escalating issues, etc.
Traditional data governance organizational structure: committees and roles that span across strategic, tactical, and operational duties
There is no one-size-fits-all data governance structure. However, most organizations follow a similar pattern when establishing committees, councils, and cross-functional groups. Most organizations strive to identify roles and responsibilities at a strategic, tactical, and operational level. Several factors will influence the structure of the program, such as the focus of the data governance project as well as the maturity and size of the organization.

Data stewards ensure controls exist to protect the data assets within your organization
Choose the right data stewards – they apply data governance on a daily basis.
- Organizations have begun to understand the significant function this role plays in the pursuit of dependable data and have begun to delegate responsibility to data stewards for the creation and management of the data.
- Data stewards are typically mid-level managers in the organization and should be relatively tech-savvy with a vested interest in improving data quality.
- The main thrust of the data steward role is to work with the data owners and steering committee to approve naming standards, develop consistent data definitions, determine data aliases, document rules, monitor data quality, and potentially define security requirements.
- Stewards are tasked with keeping a particular data domain/silo clean and free of errors as well as protecting the organization against data loss in the midst of changes, disruptions, mergers between on-premises and cloud applications, etc.
Use the Data Governance Implementation Plan Template to identify and document data stewards. Clearly define their role within the organization and their responsibilities in terms of data.
Data Stewards Are Found, Not Made
- Whenever possible, appoint people who are already interested and involved in quality of data.
- They should have some background experience familiarizing them with data management.
- Data stewards must be provided with the authority and accountability to make final decisions on which data definitions, formats, and standard processes are acceptable for their data set.
For a job description of your typical data steward, look here: Data Steward Job Description
Data owners bring accountability to the data of their departments
Organizations often have trouble determining and defining who owns the data. In order to create accountability, identify representatives from different departments to step into the data owner role.
One approach to creating effective data owners:
- Identify different data sets/domains within your organization and group them according to different departments.
- Assign an owner for each data set or domain.
- The data owner should be senior-level business personnel or department heads and come from the business rather than technical teams. This allows them to better represent the organization’s needs.
- Create formal job descriptions that highlight responsibilities and expectations. This will help data owners accept ownership and accountability. The data owner should ultimately decide who has access to enterprise data. Other responsibilities could include:
- Review the permissions of user groups to different data sets.
- Assess the quality of the data and whether it enables employees to perform their jobs efficiently.
- Determine the impact of changing permission statuses.
- Ensure the data owner role is clearly defined, repeatable, and effective.
Refer to the Data Governance Implementation Plan Template to identify and document data owners within the organization. Clearly define their roles and responsibilities.
Info-Tech Insight
When there are decision points that determine if someone should be granted permission to access data, a data owner must exist. If the enterprise fails to identify a data owner, this duty will get offloaded to members of IT who have limited understanding of the organizational or business context of the data they are assigned to manage.
Put a data governance steering committee in place to enforce policies and procedures
Data Governance Steering Committee
- The steering committee must consist of organization-wide representatives. Ensure that they understand the data needs of the organization and the technological constraints, if any, of the existing infrastructure and applications.
- The steering committee must be given the necessary political authority to make decisions on how the organization’s data is maintained, what the data contains, and how long the data is kept in the system according to regulatory constraints.
- The steering committee will be the governing body and final voice regarding organizational data processes, policies, and standards used throughout the organization. Any changes surrounding the manipulation of data must be authorized by the steering committee before implementation.
- An effective data governance steering committee depends on the size and complexity of the organization. The team needs to be big enough to represent key stakeholders but small enough to ensure that tasks can be carried out efficiently.
Core objectives of a data governance steering committee:
- Strengthen data-driven decisions by acting as the in-between for the data governance council and the working groups.
- Ensure information is consistently defined and well understood.
- Create trusted data as an enterprise asset.
- Improve the consistency of data use across an enterprise.
The following categories of key stakeholders should make up the group:
- Data owners
- Departmental subject matter experts
- Departmental process owners
- Data stewards
Refer to the Data Governance Implementation Plan Template. Document the members and responsibilities of the data steering committee. Determine how frequently the committee will meet.
A data governance council acts as the ultimate level of authority in the data governance program
The data governance council:
For smaller organizations, this function may only be one person, but for larger organizations, it can be several people. Ideally, the governance council should have a seat for an executive sponsor.
Responsibilities include:
- Assist in setting direction for future data initiative
- Provide guidelines around new data policies, procedures, and standards.
- Authorize changes to existing policies and approve the implementation of new policies.
- Resolve issues escalated by the steering committee.
A data governance council should include some of the following:
- CEO, CIO, CFO, CDO, and senior management.
Involving C-suite executives will help solidify their buy-in for data-related initiatives.
Refer to section 2.4 of the Data Governance Implementation Plan Template:
- Document council members.
- Determine the frequency of meetings.
- Identify their responsibilities.
Info-Tech Insight
Taking a cross-functional approach and including inputs from senior managers and C-suite executives will help ensure the data governance program is aligned with organizational needs.
Key resources for your data strategy: The CDO
The Dawn of the Chief Data Officer (CDO):
Where once in the past at your typical organization, a combination of executives such the chief marketing officer (CMO), chief financial officer (CFO), and chief information officer (CIO) had responsibility of the data, now we are seeing more and more a transition to a single executive role focused on data – the chief data officer.

“Digital transformation remains a central business initiative that relies on data. Initially, data functions were focused on regulatory compliance, however, many executive teams now want to see continued innovation and results from the Chief Data Officer that generate value and growth for the company.”– Guillaume Le Galiard, Collibra, 2019
Some key considerations for the CDO
- How much of the enterprise data has been given a "value" through a standardized process?
- What is the quality of data in your landscape?
- Relevance
- Accuracy
- Timeliness
- Availability
- How much data from the corporate landscape has been published as open data?
- How much of the data landscape has been classified appropriately?
Some measures of success for the CDO
Some measures of success for the CDO:
- How has your data capability landscape matured?
- How have you increased the volume and variety of data accessible to your data consumers and stakeholders?
- What does the enterprise data roadmap look like, and how is that tied to the data strategy?
- What is the mechanism in place to change the culture such that data is recognized as a corporate asset?
- What is the health of your data landscape?
- Has the business noticed a positive impact in data accessibility and availability?
A high-level sample RACI view CIO vs CDO vs CTO
CIO | CDO | CTO | |
---|---|---|---|
Data Ethics | Consulted | Accountable | Informed |
Data Security and Privacy | Consulted | Accountable | Consulted |
Data Collection | Consulted | Accountable | Consulted |
Data Exploitation | Consulted | Accountable | Consulted |
Application Development | Accountable | Consulted | Consulted |
Technology Training | Accountable | Informed | Informed |
Vendor Management | Accountable | Informed | Informed |
Technology Deployment | Consulted | Informed | Accountable |
Architecture Change | Consulted | Consulted | Accountable |
See Info-Tech’s Chief Data Officer job description for further context.
Phase 2, Step 3: Put Things Into Perspective – SWOT Analysis
This step will walk you through the following activities:
- Conduct a SWOT analysis, examining your data management enablers as well as key resources and skill sets
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
The SWOT analysis will provide an opportunity to self-assess your data foundation: the data management enablers and key resources and skill sets to help round out the data strategy in terms of risk and feasibility.
A SWOT analysis will help identify both the internal and external factors impacting the data environment
A SWOT analysis will provide an opportunity to self-assess your data foundation: the data management enablers and key resources and skill sets.
SWOT stands for strengths, weaknesses, opportunities, and threats. Each word is a category of internal and external factors that could impact the data strategy and must be taken into consideration.

Review these questions to help you conduct your SWOT analysis on your current data management
Strengths (Internal)
- How does the Data Management department succeed at supporting the business?
- Which data management services are known for being the most effectively delivered?
- Which data management capabilities and enablers are the most mature?
- Are the data management department’s people professional, knowledgeable, and talented?
- Is the Data Management department good at innovating?
Weaknesses (Internal)
- What areas of your Data Management department require improvement?
- If your end users were to provide you with constructive criticism, what would it be about?
- Is the IT/Data Management budget sufficient? (This can also be a strength.)
- Are the data management processes well documented and monitored? Are people trained on performing the processes?
- How strong is the IT-business communication and alignment? (This can also be a strength.)
Opportunities (External)
- Are there any vendors or external partners that can help the Data Management department deliver better solutions?
- Do cloud solutions provide any opportunities?
- Do we plan on taking advantage of technology trends such as the internet of things, AI, and ML?
- Are there any business trends in your organization’s industry that would need data management support?
- Are there any technology trends that your competitors are implementing or thinking of implementing?
Threats (External)
- Are there any obstacles external to the IT and Data Management department that will impact your ability to achieve success?
- Has there been an increase in the frequency of security breaches in your industry?
- Is shadow IT/shadow BI/rogue reporting prevalent at your organization?
- Have there been regulatory changes likely to drastically change the way data solutions/IT solutions are delivered (e.g. GDPR)?
Conduct a SWOT analysis for the data strategy
2.3
Estimated Time: 2 Hours
Instructions:
- Break the group into two teams:
- Assign team A internal strengths and weaknesses.
- Assign team B external opportunities and threats.
- Have the teams brainstorm items that fit in their assigned grids. Use the prompt questions on the previous slide as guidance.
- Pick someone from each group to fill in the SWOT grid.
- Conduct a group discussion about the items on the list; identify implications for the data strategy and opportunities to innovate.
- Input the results into the Data Strategy Document Template.
INPUT
- Data strategy creation team expertise
OUTPUT
- Analysis of internal and external factors impacting the data strategy
Materials
- Whiteboard and markers
Participants
- Enterprise architect
- Data architect
- Senior IT personnel
- Data governance representatives
Phase 3
Formulate a Sustainable Data Strategy
Phase 3 will help you to bring the pieces together for formulating an effective data strategy
In this phase, you will focus on putting the output pieces of the previous phases together to formulate an effective data strategy.
- Evaluate and prioritize the use cases built in Phase 1.
- Formulate your business-needs model, which summarizes the alignment of organizational goals with data needs, your internal enablers and barriers, factors in the external environment, and the technology trends landscape.
- Understand the role of change management and communication as you implement the various initiatives of your data strategy.
- Create your strategic plan
"To stay competitive, we need to become more data-driven. Compliance pressures are becoming more demanding. We need to add a new functionality."
Download the Data Strategy Document Template.
Phase 3, Step 1: Prioritize Your High-Value Use Cases
This step will walk you through the following activities:
- Use case evaluation and prioritization
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
- Evaluation and prioritization of the use cases built in Phase 1
Conduct a risk/value quadrant assessment to assess and prioritize your use cases
3.1
Instructions:
- Recall that in Phase 1, the business stakeholders worked on crafting use cases that represent high-value areas that need to be addressed for their teams or functions.
- Based on your answers to the following questions, place the use-case topics on the quadrant according to where they lie along the risk and value axes:
- What do you do?
- Challenges?
- Activities/Systems?
- Value?
- What is the risk associated with this data asset if mishandled, stolen, or lost?
- Use cases in the top right (value and risk driven), top left (value driven), and bottom right (risk driven) quadrants should be considered for building out into a business case.

INPUT
- Department answers to questions targeting key data needs
OUTPUT
- Assessed and prioritized use cases
Materials
- Note-taking materials
- Whiteboard or flip chart, markers, sticky notes
Phase 3, Step 2: Build Your Business- and Data-Needs Model
This step will walk you through the following activities:
- Business-data-needs modeling
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
- Business- and data-needs model with organizational goals, data needs, internal enablers and barriers, factors in the external environment, and the technology trends landscape
Business and data needs must align for your data strategy
Info-Tech’s Business- and Data-Needs Model
- Identifying the organizational goals and demonstrating how data supports those goals is key to a successful data strategy.
- Augmenting the business-needs model with your internal enablers (those key pieces or things that you do well or have in place internally that would help drive the strategy) and barriers (things you need to be cognizant of, as they could potentially derail or inhibit the initiative, and need to plan mitigations for) makes for a realistic and feasible representation.
- Rounding out the model with factors in the external environment and examining the technology market and trends landscape creates a holistic view of your data strategy.

Business- and Data-Needs Model (Template)
Identifying organizational goals and how data can support those goals is the first step to a successful data management strategy. Rounding out the business model with technology drivers, environmental factors, and internal barriers and enablers creates a holistic view of data strategy within the context of the organization as a whole. Through business interviews, the following holistic model can be created to articulate the alignment of the organizational goals and data needs in an all-encompassing context.

Sample business- and data-needs modeling for data strategy

Phase 3, Step 3: Understand the Role of Communication and Change Management
This step will walk you through the following activities:
- Understanding the role of communication and change management
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
- An understanding of the role of change management and communication in your data strategy
Optimizing data strategy relies on communication between the business and IT/data management
Communication across all levels of the organization is key for starting to build a data-driven culture.
You will have your technical audience, your business users, and data consumers, and you will have your senior leaders.
Communication at all stages is also going to be key. It is very important that you demonstrate and share your wins.
Remember: Roles such as data architects and business relationship managers bridge the gap between strategic and technical requirements of data.

Therefore, as you plan the data strategy optimization and its interactions and impact, it is imperative that you communicate the plan and its implications to the business and other stakeholders.
Create strong communication for the data strategy
Effective communication will aid in building and sustaining a healthy data culture.
Some key messages to convey to the business and the wider organization:
Business Value: How the data strategy aligns with the organization’s strategic priorities and business goals, whether those are around Client Intimacy, Service Excellence, Product and Service Innovation, Operational Excellence, and/or Risk Management
The Data Strategy Objectives, Guiding Principles, Vision and Mission
Building a Data Culture Through Learning and Collaboration: Data literacy and improving the organization’s data IQ
Data Roles and Responsibilities: Data is a corporate asset and hence requires a partnership across the business and IT to ensure it is treated as an asset.
Key characteristics of the communication:
Consistent, permeates all levels of the organization, frequent, open and honest, two-way, creative, and relevant
Expected outcomes:
Drives adoption, helps manage change, fosters relationships, reduces resistance, builds credibility, gives visibility, and helps drive support and buy-in across the enterprise
Vehicles for communicating the messages out to the organization and for maintaining the visibility of data:
Lunch and learns, information sessions, roadshows, show-and-tell events, and data IQ tournaments
Phase 3, Step 4: Create Your Strategic Plan
This step will walk you through the following activities:
- Pull all of the pieces together into your strategic plan
This step involves the following participants:
- Data architect
- Enterprise architect
- Business analyst
- Business stakeholders
Outcomes of this step
- Your strategic plan for going forward
- Leverage the Data Strategy Document Template to pull together the output of all preceding activities and start building a data strategy document.
- See the table of contents of the Data Strategy Document Template to get a firm understanding of the minimum components required for crafting a robust and comprehensive data strategy.
To implement your data strategy changes, you must plan to accommodate the issues that come with change
Once you have a plan in place, one the most challenging aspects of improving an organization is yet to come … overcoming change!

Change management improves core benefits to the business: the four Cs
Most organizations have at least some form of change control in place, but formalizing change management leads to the four Cs of business benefits:
- Control: Change management brings daily control over the data environment, allowing you to review every relatively new change, eliminate changes that would have likely failed, and review all changes to improve the data environment.
- Consistency: Request-for-change templates and a structured process shape implementation, test, and backout plans to be more consistent. Implementing processes for preapproved changes also ensures these frequent changes are executed consistently and efficiently.
- Collaboration: Change management planning brings increased communication and collaboration across groups by coordinating changes with business activities. The Change Advisory Board (CAB) also brings a more formalized and centralized communication method for IT and the data management group.
- Confidence: Change management processes will give your organization more confidence through more accurate planning, improved execution of changes with less failure, and control over the data environment. This also leads to greater protection against audits.
Info-Tech Best Practice
Some organizations will not be able to assign a dedicated change manager, but they must still task an individual with change review authority and with ownership over the risk assessment and other key parts of the process.
Avoid the pains of poor change management
An ineffective change management process will lead to lost productivity, service disruptions, and slow deployments.

“With no controls in place, IT gets the blame for embarrassing outages. Too much control, and IT is seen as a roadblock to innovation.” – VP IT, Federal Credit Union
There are several common misconceptions about change management
- It’s just a small change; this will only take five minutes to do.
- Ad hoc is faster; too many processes slow things down.
- Change management is all about speed.
- Change management will limit our capacity to change.
Reality: Even a small change can cause a business outage. That small fix could impact a large system connected to the one being fixed.
Reality: Ad hoc might be faster in some cases, but it carries far greater risk. Following defined processes keeps systems stable and risk-averse.
Reality: Change management is about managing risk. It gives the illusion of speed by reducing downtime and unplanned work.
Reality: Change management allows for a better alignment of process (release management) with governance (change management).
Leverage Info-Tech’s resources to smooth change management
As changes to the data environment occur, the data strategy team must stay ahead of the curve and plan the change management considerations that come with major architectural, systems, operations, role definition and assignment, and governance decisions.
See Info-Tech’s resources on change management to smooth changes:



If you want additional support, have our analysts guide you through this phase as part of an Info-Tech workshop
Book a workshop with our Info-Tech analysts:

- To accelerate this project, engage your team in an Info-Tech workshop with an Info-Tech analyst team.
- Info-Tech analysts will join you and your team onsite at your location or welcome you to Info-Tech’s historic Toronto office to participate in an innovative onsite workshop.
- Contact your account manager (www.infotech.com/account), or email Workshops@InfoTech.com for more information.
The following are sample activities that will be conducted by Info-Tech analysts with your team:

Build use cases that necessitate or support the case for data strategy optimization.
In this activity, the facilitator will guide the team of stakeholders through deep-dive sessions for identifying high-priority initiatives for your organization’s data strategy through Info-Tech’s data requirements and mapping methodology.

Understand the current data environment and build an initialized future state roadmap for operationalizing the data strategy.
In this activity, the facilitator will work with the team of stakeholders to understand the current environment across data management enablers, information dimensions, people, culture, and skill sets and will analyze gaps and initialize a corresponding roadmap for the data strategy. This artifact will be a key component of the resulting data strategy document.
Research contributors and experts
Internal Contributors
- David Wallace, Vice President, Research – Industry Practice
- Michael Fahey, Executive Counselor – Executive Services
- Valence Howden, Principal Research Director – CIO
- Dirk Coetsee, Research Director – Data and Analytics
- Reddy Doddipalli, Senior Workshop Director – Data and Analytics
- Igor Ikonnikov, Research Advisor – Data and Analytics
- Andrea Malick, Research Director – Data and Analytics
- Rajesh Parab, Research Director – Data and Analytics
External Contributors
- Lisa Bobo, CIO, City of Rochester
- Stephen Burt, Assistant Deputy Minister, Data, Innovation, and Analytics, and Chief Data Officer, Department of National Defence, Government of Canada
- Dr. Irshad Siddiqui, Chief Health Information Officer (CHIO), Blessing Health System
- Head of Enterprise Information Management at an African central bank
Related Info-Tech research

Ensure you have a strong data practice and foundation. Optimize your data management capabilities, guided by Info-Tech’s Create a Plan for Establishing a Business-Aligned Data Management Practice blueprint.

Define an effective and sustainable data governance program by leveraging Info-Tech’s Enable Shared Insights With an Effective Data Governance Engine blueprint.

The data architecture needs to evolve to keep pace with changing business, data and user landscapes. Info-Tech’s Build a Business-Aligned Data Architecture Optimization Strategy blueprint provides you with a phased approach to building a modernized data architecture for meeting changes landscapes.

The step-by-step approach outlined in Info-Tech’s Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach blueprint helps you to improve the organization’s data quality by finding the root causes, remediating and fixing the issues, and building a practice to ensure that improved quality is sustained.
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