- Mahmoud Azimaee, Data Quality Manager, Institute for Clinical Evaluative Sciences
- Peter Benson, Founder and Executive Director, ECCMA
- Mario Cantin, Chief Data Strategist at Prodago
- Nancy Couture, Delivery Enablement Lead, Datasource Consulting
- Adrian Dokmecian, Data Quality Lead, Bank of Montreal
- Kirti Shetty, Independent Subject Matter Expert,
- Dr. John R. Talburt, Director of the Information Quality Graduate Program, University of Arkansas at Little Rock
- Jay Zaidi, Founder and Managing Partner, AlyData
- Henrik Liliendahl Sorensen, MDM Specialist, Liliendahl.com
- Data users do not trust the data quality in your company. Business users bypass IT and establish their own data silos to manage quality. Without governance or consistency, data quality issues are propagated further within data silos.
- Data is not usable or useful to the business due to deficiencies in quality and processes, resulting in decisions being made based on intuition and weak analysis. Worse, these decisions can be based on the wrong data.
- Modern regulatory environments are becoming increasingly strict and demanding. Not knowing the level of data quality or an inability to control data quality leads to significant exposure to risks around data compliance and security.
- With suboptimal data quality, companies are unable to launch related data initiatives such as business intelligence, master data management, and big data. They need a clear understanding of business requirements and the implementation of data quality processes first.
- A business-driven and strategic approach to getting data quality right enables the implementation of best practices to ensure it stays that way.
- Hone in on key data subject areas first and employ an incremental approach to build a data quality practice; once successful, tackle the remaining data that is important to your business.
- Integrating data quality provisions in every stage of the data life cycle will provide a modern, effortless, and self-governed framework for data quality sustainability.
Impact and Result
- Implement a set of data quality initiatives that are aligned with overall business objectives and aimed at addressing data practices and the data itself.
- Develop a prioritized data quality roadmap and long-term improvement strategy.
- Build related practices such as business intelligence and analytics with more confidence and less risk after achieving a fit-for-use level of data quality.
Start here – read the Executive Brief
Read our concise Executive Brief to understand the importance of data quality, Info-Tech’s methodology for conquering data quality challenges, and how we will support you in completing this mission.
1. Develop a business-driven data quality improvement plan
Define business context and subject area focus to launch a data quality improvement strategy.
2. Assess data quality
Perform an inventory of a critical data subject area and assess its quality.
3. Assess data quality practice capabilities
Evaluate current data quality practices and identify areas of improvement to reach a target state.
4. Develop a data quality roadmap for continual improvement
Develop initiatives and build a fit-for-purpose data quality improvement roadmap.
This guided implementation is an eight call advisory process.
Guided Implementation #1 - Develop a data quality improvement plan
Call #1 - Create your business and data quality vision
Call #2 - Plan your data quality project
Guided Implementation #2 - Assess data assets
Call #1 - Review results of the data inventory and assessment
Call #2 - Review results of the root cause analysis and impact assessment
Guided Implementation #3 - Assess data quality practice
Call #1 - Review results of the data quality practice assessment
Call #2 - Define the future state of the data quality practice
Guided Implementation #4 - Create a data quality improvement roadmap
Call #1 - Review planned initiatives
Call #2 - Discuss roadmap findings and next steps
- Title: Data Quality Course
- Number of Course Modules: 5
- Estimated Time to Complete: 2-2.5 hours
- Crystal Singh, Research Director, Applications
- David Piazza, VP of Research & Advisory, Applications Practice
- Now Playing: Executive Brief
Book Your Workshop
Onsite 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 onsite 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: Understand the Business Context
- Understand the corporate strategy and goals.
- Identify the most valuable subject areas to work on.
- Define data quality principles to guide the project.
- Scope, structure, and staff the project.
Key Benefits Achieved
- Understanding of the business’s needs for data quality.
- Knowledge of which subject areas are the most valuable to the business and how data quality interacts with those subject areas.
- Establishment of overarching data quality principles to provide guidance.
- The data quality initiative is tackled in chunks, with a defined and manageable scope.
- Roles and responsibilities for the project team are defined. Agility best practices are used in the project.
Establish business context
- Business vision, mission, and goals clarified
- Business drivers and differentiators identified
Review and identify the most valuable subject areas
- Key subject areas are identified based on business values
Define data quality principles
- Data quality principles are defined
Define data quality roles and responsibilities
- Project organization structure is defined
Establish project governance and ensure agility in data quality
- Project governance is designed with built-in agility
Module 2: Data Inventory and Data Quality Assessment
- Inventory data assets for the subject area(s) of interest.
- Perform a detailed technical assessment of the data's quality.
- Interview data users and owners to uncover issues around data.
- Identify root causes and impact of the data issues.
Key Benefits Achieved
- An understanding of data assets.
- Data quality is quantified through technical assessment of the data.
- Gathered insights on the data from interviews.
- Root causes of the data issues are identified.
- The business impact of the data issues are quantified.
Perform data inventory
Conduct user interviews
- Data quality profiles for data assets
Assess data quality via technical assessment
- Insights on the data quality issues
Perform root cause analysis
- Root causes of the data issues
Quantify business impact
- Quantified business impacts
Module 3: Data Quality Practice Assessment
- Evaluate the maturity of the existing data quality practice and activities.
- Assess how data quality is embedded into related data management practices.
- Envision a target state for the data quality practice.
Key Benefits Achieved
- Understanding of the current data quality landscape.
- Gaps, inefficiencies, and opportunities in the data quality practice are identified.
- Target state for the data quality practice is defined.
Evaluate data quality maturity
- Current maturity of the data quality practice
Assess how data quality is integrated with other data management practices
- Data quality maturity in the other related data management practices
Define the target state for the data quality practice
- Future state maturity
Module 4: Initiatives Roadmap for Continual Improvement
- Gather and rationalize issues and opportunities from the earlier phases.
- Define improvement initiatives.
- Define a data quality improvement strategy and roadmap.
- Plan for continuous improvement in data quality.
Key Benefits Achieved
- Improvement initiatives are defined.
- Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
- A roadmap is defined to depict when and how to tackle the improvement initiatives.
Take inputs in terms of data issues, gaps, and opportunities from the earlier phases
- Data improvement initiatives are defined
Define and develop specifications for the improvement initiatives
- Data quality improvement strategy is defined
Develop a strategy and roadmap for data quality
- Roadmap to achieve the strategy is developed