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Off-Field Data Reference Architecture

Make the case for an off-field data reference architecture.

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  • A constant influx of new data solutions combined with closed or siloed source transactional applications create complexity and challenges when trying to integrate it together to create insights you can act on to improve fan experience, engagement, revenue, and sponsorship relations.
  • You are struggling to get buy-in from your business executives for investing in off-field data applications.

Our Advice

Critical Insight

  • An effective off-field data architecture can create data-driven digital opportunities and new insights. Equipped with the right data strategy, any sports entertainment organization can create a sustainable sports business, improve fan engagement and sponsorship relations, and achieve business goals.

Impact and Result

  • Establish a working group of key stakeholders from the organization to work on this endeavor together and determine what the risks and considerations may be.
  • Determine the state of your current data architecture and what data-driven digital opportunities can arise from Info-Tech's off-field reference architecture.
  • Build a business case for why an off-field data architecture is important to get buy-in from your business executives.
  • Once buy-in is secured, input this off-field architecture into your next data strategy to achieve your business goals.

Off-Field Data Reference Architecture Research & Tools

1. Off-Field Data Reference Architecture Guide – Discover the value in an off-field data architecture.

This blueprint is comprised of tools, templates, and a validated view of an off-field data reference architecture that can improve your business, identify what the challenges and opportunities may be, and help you get buy-in from business executives.

2. Business Case Presentation Template – A template that can be used alongside the guide to present the value to your business executives.

Use this template alongside Info-Tech’s Off-Field Data Reference Architecture Guide, then leverage that information to create an executive presentation about how it impacts the organization.

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Off-Field Data Reference Architecture Guide

Make the case for an off-field data reference architecture.

Table of Contents

4 Analyst Perspective 36 Step 2.1: Off-Field Data Reference Architecture
5 Executive Summary 53 Phase 3: Build your Case for an Off-Field Data Architecture
17 Phase 1: Define a Working Group and the Current State 55 Step 3.1: Construct the Business Case
18 Step 1.1: Build an Accurate Depiction of the Business 64 Step 3.2: Determine the Impact to the Organization
27 Step 1.2: Information Assessment 72 Summary of Accomplishment
35 Phase 2: Discover Data-Driven Digital Opportunities 76 Bibliography

Off-Field Data Reference Architecture Guide

Make the case for an off-field data reference architecture.

EXECUTIVE BRIEF

Analyst Perspective

Centralize your fan data with the right off-field data architecture.

Sports entertainment organizations are struggling to simplify their siloed data to create the 360-degree view of the fan and effectively use it to improve their business. Thirty percent of sports organizations report that only one person has the appropriate knowledge of data and 25% say that no one has any expertise in the area (Fan Engagement Consultancy, 2021).

Sports entertainment organizations are new to collecting fan data to meet business goals, where 61% of all sports entertainment organizations do not use data for their overall strategy. Additionally, only 51% of teams collect data in a centralized place, meaning that many teams still work with separate data sources, which limits their marketing abilities to understand fans (Fan Engagement Consultancy, 2021).

Through the right off-field data, architecture teams can enable their business strategies, where they can discover data-driven digital opportunities and relieve the pains of monetizing their fan data while maximizing revenues, sponsorships, enhancing fan engagement, and guiding fan behaviors.

Photo of Elizabeth Silva, Research Specialist, Sports Entertainment Industry, Info-Tech Research Group.

Elizabeth Silva
Research Specialist, Sports Entertainment Industry
Info-Tech Research Group

Executive Summary

Your Challenge
  • A constant influx of new data solutions combined with closed or siloed source transactional applications create complexity and challenges when trying to integrate it together to create insights you can act on to improve fan experience, engagement, revenue, and sponsorship relations.
  • You are struggling to get buy-in from your business executives for investing in off-field data applications.
Common Obstacles
  • Your IT team is not aware of the data-driven digital opportunities an off-field data architecture can provide to your organization, seeing no value in the initiative.
  • Your sports organization is solely focused on team performance to generate revenue; they do not see the value in shifting the focus to monetizing fan data.
  • Developing a convincing and impressive business case to get buy-in from your business executives is challenging.
Info-Tech’s Approach
  • Establish a working group of key stakeholders from the organization to work on this endeavor together and determine what the risks and considerations may be.
  • Determine the state of your current data architecture and what data-driven digital opportunities can arise from Info-Tech's off-field data reference architecture.
  • Build a business case for why an off-field data architecture is important to get buy-in from your business executives.
  • Once buy-in is secured, input this off-field architecture into your next data strategy to achieve your business goals.

Info-Tech Overarching Insight

An effective off-field data architecture can create data-driven digital opportunities and new insights. Equipped with the right data strategy, any sports entertainment organization can create a sustainable sports business and improve fan engagement and sponsorship relations, all while achieving business goals.

The meaning of off-field vs. on-field

Off-Field vs. On-Field Analytics

Off-Field Analytics

Deals with the business side of sports. Off-field analytics focus on helping a sports organization surface patterns and insight through data that would help increase ticket and merchandise sales, improve the fan experience, increase engagement, and more.

Off-field analytics is what creates an off-field data architecture and makes for a fan-driven data strategy.

On-Field Analytics

Deals with improving the on-field performance of teams and players by collecting player statistics and patterns.

On-field analytics is what makes for a player development or team development strategy.

“Off-field data and analytics is the ability to transform data into capabilities that transform the experience for fans.” (Rajiv Maheswaran, CEO, Second Spectrum)

An off-field data architecture and fan-driven data strategy is important

An off-field data architecture should be the core of any fan-driven data strategy. This type of strategy should be a vehicle for ensuring data is poised to support your organization’s strategic objectives and opportunities.
  • Having proper fan data and integration is vital to the success of other strategies, such as a digital fan engagement strategy, as they are reliant on accurate, maintained, useable, and integrated data to make effective decisions, and to understand the fan better to create practical initiatives.
  • The bottom line is that data is valuable to everyone when used right. Data empowers your organization to make better decisions, where an effective off-field data architecture and fan-driven data strategy will ensure you have the right data to make better decisions when it comes to marketing, hyper-personalization, sponsorships, and many more in the interests of your fans.
  • Making better decisions in the perspective of the fan allows you to reduce the friction within the fan journey while meeting fan needs and predicting what they want next to create retention and loyalty.

“Data-driven organizations are not only 23 times more likely to acquire customers, but they're also 6 times more likely to retain customers and 19 times more likely to be profitable." (McKinsey Global Institute via Tappit, n.d)

Other benefits that were found from using data are:
  • 8% increased profit
  • 10% reduced cost
  • 69% improved strategic decision making
  • 54% better control of operational processes
  • 52% improved understanding of their customers
      – BARC Research via Tappit, n.d.

“Data itself isn’t valuable, it’s the ability to use data to create content that fans want." (Rajiv Maheswaran, CEO, Second Spectrum)

Use fan data to differentiate and remain competitive in today’s digital economy

Off-field data in sports can create many benefits for organizations, such as sustainable revenue streams, robust fan intelligence to improve fan experience and engagement, and improved sponsorship relations. Having effective off-field data to understand fans better has so many more benefits than it may seem, as being different and remaining competitive in today's sports market is difficult to do without it.

Revenue Reliant on Team Performance Is Not Sustainable

Data and analytics have been used for improving team performance, although relying on team performance is not a sustainable choice as revenue is linked to performance. If a team is performing well, it generates revenue. If a team performs poorly, sources of revenue such as ticket and merchandise sales will drop.

Sports teams must create new revenue streams to be sustainable. By collecting data through multiple different sources, the 360-degree view of the fan will give sports teams a deeper understanding of their fans, to help them generate revenues and sustain relationships beyond team performance.

Understanding fans better is good for the long-term health of the business, as it allows for prediction of fan engagement behaviors and actions.

(Source: CIO, 2017)

Fan Intelligence Benefits

Understanding fans and their fan engagement behaviors is a key aspect of fan engagement, which every sports organization is looking to achieve.

Having the right data allows sports organizations to target and personalize offerings to maximize the potential of monetizing their assets.

Fan-driven data decision making is crucial when it comes to understanding your fan base, as understanding the data to further engage fans in order to refine and improve future activations is important.

(Source: Sportcal, 2020)

Sponsorship Benefits

Data and measurability can provide wins for sports team, fans, and sponsors. With data and analytics, it’s now possible for sports organizations to measure the impacts of the sponsorship opportunities placed within their venues in aggregated ways.

The three wins:

  • For the team/venue it allows for the ability to command higher rates by providing transparency and measurability to their sponsorship efforts.
  • It develops better fan relationships and more intimate discussions with brands.
  • For sponsors/brands it allows for better market targeting and understanding of the impact of the dollars they are spending.
If done right, all three parties should win.

(Source: Sportcal, 2020)

An off-field data architecture needs strategic drivers

Your fan-driven data strategy needs to align with your organizational strategy, such as a digital business strategy.

Privacy, Risk & Compliance

As a sports organization, you are more than likely operating as a privately held organization that is owned by a larger organization/holding company and mandated to meet certain regulatory requirements from the league your team belongs to and the area you are located in.

Risk mitigation is also another driver for formalizing or optimizing your current data architecture. Your current practices and environment may be outdated, leading to potential exposure to risk.

Table center-piece with icons corresponding to surrounding strategic drivers. Fan Engagement/Service Excellence

As a sports organization, your current focus is on improving fan/customer experience, engagement, and striving for service excellence, whether by offering highly tailored products or services, upselling, cross-selling, sending targeted communication, or building fan/customer loyalty.

Stakeholders within the sports industry are fans, internal customers, sport spectators, sponsors/partners, media participants, leagues/clubs, host community, and governing bodies.

Operational Excellence

As a sports 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 for the venue. 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.

Product & Service Innovations

To maintain or establish your competitive edge, your sports 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 fan and stakeholder preferences by understanding purchasing habits, consumption, behaviors, more varied and larger data sets, IoT, and other disruptive forces.

Consider a comprehensive fan-driven data strategy to unlock value

  • Off-field data should be at the foundation of your organization's evolution where transformational insights that executives are looking for can be unlocked with the right data strategy.
  • A data strategy should be high quality, well integrated, trustworthy, and have relevant data, to create a clear form of the 360-degree view of the fan, where it is readily available to the organization when needed.
  • Your organization should be able to gain a better understanding of the business, fans, and predict what fans are looking for, whether it is at home or in venue, resulting in better experiences, which results in higher engagement and revenues, through a comprehensive fan-driven data strategy.
  • However, you cannot have an effective fan-driven data strategy, without a comprehensive off-field data architecture first.

"Data can be considered to be one of the most valuable commodities in today’s world - more so than gold, oil and bitcoin. It underpins most business and performance operations; and the organizations that are able to maximize its use are the most successful.” (David Ingham, Client Partner of Media, Entertainment & Sport at Cognizant via Techradar, 2021)

"The awareness, collection and usage of data needs to be seeded and cultivated from the top of the organization as a priority for it to take root within sports entities so they can pave the way for inclusion as a source of insight in decision making that will future-proof the organization's operating model." (Global Sports Innovation Center, Powered by Microsoft, 2020)

In the major leagues, 75-85% of single game buyers don’t come back every single year, where teams need to be investing in effective tools to better understand fans through practical insights. (StellarAlgo, 2021)

"After the first lockdown, there was a short pause in CDP-projects within the sports industry. However, the need for a digital transformation became globally evident to most sports organizations, resulting in tremendous growth in CDP-projects. The CDP is finally perceived as a backbone to digital transformation & fan engagement.“ (Peter Kekesi, Data Talks, 2021 interview)

Many opportunities are revealed for sports entertainment organizations once they start using data

The opportunities data can help your sports entertainment organization leverage:

Icon of a hand giving a package.

HIGHER QUALITY SERVICES

The strategic use of data can enable sports entertainment organizations to provide higher quality services.

Icon of a head with brain circuits.

FAN INTELLIGENCE

The strategic use of data can provide sports entertainment organizations with a wealth of knowledge on fans and you will be able to not just know the fan behaviors but also guide and predict what they may be to enhance marketing, fan engagement, and revenues.

Icon of a magnifying glass over a heart monitor.

FAN-DRIVEN DATA DECISIONS

The insights that can be found through the strategic use of data can provide intuitive information around fans and sponsorships so fan-driven data decisions can be made.

Icon of people on an upward trend line.

IMPROVE SPONSORSHIP RELATIONS

Make better evidence-informed decisions and improve understanding around the impact of sponsorships and fan engagement, so funds and sponsorship can be directed where they are most likely to deliver the best results.

Icon of coins.

DATA MONETIZATION

Create actionable decisions around your fan data so you will be able to monetize it.

Icon of a thumbs-up in a speech bubble.

BECOME A PROVEN BUSINESS PARTNER

If you have any business strategy, such as a digital business strategy, in place where data and integration is highly important, you will be able to effectively align with the business and be a proven business partner rather than just a business enabler.

Align and enable your organizations goals cascade with an off-field data architecture

An off-field data architecture is what makes an organization’s goals actionable. Ensuring that your off-field data architecture will have the right components to support organizational goals will allow for better buy-in from business executives.

Example:

Example organization goals cascade. The first column on the left side is 'Organization Goals' listing four business goals, some of which are color-coded similarly. The second column on left side is 'Organization Initiatives' with three initiatives, each color-coded to match the business goal(s) they help to achieve. The third column on the left side is 'Organization Capabilities' with capabilities grouped by a category such as 'Fan Scoring', each of which are color-coded to business goals and the business initiatives that create or improve them. On the right side there is similarly 'IT Goals' with two goals listed, they each are achieved through many 'IT Initiatives' which are created or improved by 'IT Capabilities', of which 'Data Architecture' is highlighted.. The capabilities of either side support each other.

Info-Tech’s methodology for an off-field data architecture

1. Define a Working Group and the Current State 2. Discover Data-Driven Digital Opportunities 3. Build Your Case for an Off-Field Data Architecture
Phase Steps

1.1 Build an Accurate Depiction of the Business

1.2 Information Assessment

2.1 Off-Field Data Reference Architecture

3.1 Construct the Business Case

3.2 Determine the Business Case Impact to the Organization

Phase Outcomes Establish a working group to provide direction and clarity on an off-field data architecture, such as the associated risks with this initiative, and comprehend the current data management maturity. Understand what an off-field data architecture is and discover what data-driven digital opportunities exist for value creation.
Read through conceptual and logical data model examples for value creation.
Develop vision and mission statements and guiding principles. Identify business drivers and high-value use cases to then prioritize for making the case to business executives. Determine the impact of this initiative by enhancing the organization’s existing goals cascade/business strategy, calculating metrics, and designing a business case profile.

Blueprint Deliverable

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

Key deliverable:

Off-Field Data Reference Architecture Business Case Presentation Template
Input the activities and outputs of this blueprint into the Presentation Template to easily present your business case to executives.

Sample of the key deliverable 'Off-Field Data Reference Architecture Business Case Presentation Template'.

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

Guided Implementation

Workshop

Consulting

"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." "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." "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." "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 used throughout all four options

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 to 12 calls over the course of 4 to 6 months.

What does a typical GI on this topic look like?

Phase 1

Phase 2

Phase 3

Call #1: Develop working group; discuss and assess current data maturity. Call #2: Walk through the off-field data reference architecture, and exemplar data models for value creation. Call #4: Create vision and mission statements.

Call #5: Determine guiding principles.

Call #6: Build high-value use cases.

Call #7: Conduct a MoSCoW analysis.

Call #8: Map the data architecture components to the goals cascade.

Call #9: Determine metrics for measuring success.

Call #10: Build business case profile.

Off-Field Data Reference Architecture Guide

Phase 1

Define a Working Group and the Current State

Phase 1

1.1 Build an Accurate Depiction of the Business

1.2 Document Your Current Data Management Maturity

1.3 Assess How Well Information Supports Business Capabilities

Phase 2

2.1 Off-Field Data Reference Architecture

Phase 3

3.1 Construct the Business Case

3.2 Determine the Business Case Impact to the Organization

This phase will walk you through the following activities:

  • Identify and assemble key stakeholders
  • Document your current data management maturity
  • Assess how well information supports business capabilities

This phase involves the following participants:

  • CIO
  • CEO
  • CFO
  • CMO
  • CDO
  • Other stakeholders as appropriate

Step 1.1

Build an Accurate Depiction of the Business

Activities
  • 1.1.1 Identify and Assemble Key Stakeholders

This step will walk you through the following activities:

  • Identify and assemble key stakeholders

This step involves the following participants:

  • CIO
  • CEO
  • CFO
  • CMO
  • CDO
  • Other stakeholders as appropriate

Outcomes of this step

  • Establish a working group to provide direction and clarity on creating a business case for an off-field data architecture
  • Understand the risks associated with this initiative
Define a Working Group and the Current State
Step 1.1 Step 1.2

The challenges associated with fan data

  • The level of data maturity in the sports industry is surprisingly low; however, sports organizations have realized that they need to collect data and use it effectively to succeed. Many sports organizations have invested in using their fan data to personalize fan experiences, but many basic processes are still lacking.
  • Major pieces of information are missing from the game day experience, where sports teams understand who bought tickets, but not who attends the games. There are big gaps around what fans buy, when they buy it, brand preferences, and habits.
  • It’s not only important to collect fan data but also to have a 360-degree view of the fan and their truth, which means connecting all data source partners too.
  • “The US sports sponsorship market alone predicted to be worth $20 billion dollars in 2022, it is incredible that organizations do not understand their fans and how to maximize this revenue through data. Yet fan behavior in-stadium is still a huge data black hole." (Statista, 2021, via Tappit, n.d.)
  • “Many organizations still work with a lot of separate data sources which limits marketing as well as a general understanding of the fan, where only 51% of organizations collect it in a centralized place.” (Geoff Wilson, Data Maturity Model, via Tappit, n.d.)

“Big data is exploding. But many companies are still struggling to simplify how to make their data actionable.” (CPA Canada, 2019)

Signals of Concern

37%
of sports organizations have a one-year data strategy.

32%
of sports organizations never review their data objectives.

10%
of sports organizations have communicated their data objectives across their entire organization, with 25% communicating it only to senior staff.

29%
of sports organizations do not use any form of segmentation within email campaigns.

(Source: Tappit, n.d.)

Leverage your working group

Your working group should:

Icon of justice scales.
Govern

Be the accountable group that oversees the initiative and actively makes decisions concerning it.

Icon of an 'i'.
Inform

Identify best practices and make suggestions around what will or will not be successful as the initiative moves forward.

Icon of a compass.
Direct

Make recommendations on how the property should move forward with the initiative and highlight potential obstacles.
A working group should be comprised of:
  • CEO
  • CDO
  • CFO
  • CMO
  • CIO
  • And the appropriate directors reporting to the chiefs.

Info-Tech Insight

Your head of marketing should be a key stakeholder within your working group as the marketing team are those who will execute any of the insights found through data and analytics with their creative marketing abilities.

1.1.1 Identify and assemble key stakeholders

1-3 hours

Input: List of potential stakeholders, Level of influence and support the various stakeholders may have on this initiative

Output: Key stakeholders for this initiative

Materials: Whiteboard, Pen and paper, Presentation Template

Participants: CIO, CEO, COO, CFO, CMO, CDO

  1. As a group, create a working group of stakeholders that should be involved in creating the business case for an off-field data architecture.
  2. Aim to create a working group that can help inform your organization’s decision on whether to pursue this initiative now or wait.
  3. Evaluate and discuss each potential stakeholder on the list based on:
    1. Influence: To what degree can this stakeholder impact the progress of this initiative?
    2. Involvement: How involved is the stakeholder in this initiative already?
    3. Support: How supportive is this person of this initiative?
  4. Once the group has decided who the working group will be, document the key stakeholders for this initiative in the appendix of the Presentation Template.

Download the Presentation Template

The importance of fan data has shifted upwards

  • The COVID-19 pandemic has shifted the sports entertainment industry’s focus to look at damage control and the potential losses that can be faced in another sports shutdown; analyzing fans and other means of engagement is crucial.
  • Sports entertainment organizations that have invested in and developed digital platforms have fared much better in keeping close to their fans and riding out the COVID-19 crisis better than average.
  • "COVID-19 could lead to a 60% increase in the amount of content fans watch, where we are seeing higher engagement on social media, meaning people need and want to consume more content.” – Ramón Amich, Managing Director Spain & Portugal, Nielsen Sports, via N3xt Sports, 2020.
  • Going digital and satisfying fans is only one part of the strategy. Data is another part of the strategy, where without the right data, you will not be able to understand your fans correctly. Having the right data will carry your organization’s decision making through any global crisis that your organization may be affected by, to ensure success and valuable relationships with fans.
  • Effective data can be used as the oil to a sports entertainment organization by fueling its innovation, transformation, and strategic opportunities.

Info-Tech Insight

Using data to understand the current market is critical for determining who your at-home and live game watchers are. Marketing correctly to the right market is highly important for success.

Case Study

Canada West TV and Canada West Universities Athletic Association’s Sporting Events Data Strategy
Logo for CanadaWest.tv.
INDUSTRY
Sports Entertainment
SOURCE
Global Sports Innovation Center, Powered by Microsoft, 2020

Challenge

Canada West TV is a video streaming platform service launched on YAREtv to showcase Canada West Universities Athletic Association’s sporting events. An affiliate of U Sports, Canada West consists of 17 universities across Alberta, Manitoba, British Columbia, and Saskatchewan. Over the last three seasons (2016-2017 to 2019-2020), the platform was struggling to achieve higher YOY growth rates and they were looking to increase those numbers by understanding their fans better through data.

Solution

On assuming charge from the incumbent in 2017, a three-pronged strategy was adopted: Reach, Engage, and Monetize. Using data as its guiding light enabled Canada West TV to identify users in the target segment, convert them into subscriptions, retain them as registered users in the off-season, and steadily acquire a critical mass for what was a fledgling service three years ago.

Results

The service performed extremely well, gaining 12,000+ registered users and selling 15,000+ passes from a universe of 150,000, giving them a 10% conversion rate, which is extremely high relative to comparable organizations. The successful identification of fan segments and creation of a registered data base within the YAREtv platform were crucial factors in securing a title sponsor for the property who was able to accurately calculate the ROI for the brand association with Canada West TV.

  • Total hours view: 90% increase
  • Per customer spend: 54% increase
  • Total active users: 69% increase

Developments in business and technology are changing how sports organizations use and manage data

The main objective of BI and advanced analytics is for the organization’s decision makers to have improved value to cost and identify new business opportunities from data-driven decision making.

Operate in Real Time

Businesses of today operate in real time; to maintain a competitive edge, businesses must identify and respond quickly to opportunities and events. To effectively do this the business must have accurate and up-to-date data at their fingertips.

Business Intelligence and Analytics

Business intelligence: analytical capabilities centered around metrics and measures that gauge past performance and guide business planning.
Examples:

  • Reports and dashboards
  • Data discovery
  • Questions asked in BI scenarios
    • What happened?
    • How many?
    • How often did it happen?

Advanced analytics: uses sophisticated techniques to predict events, uncover trends and patterns, and garner additional insights from data that could not be uncovered from traditional BI practices.
Examples:

  • Descriptive modeling
  • Predictive analytics
  • Text analytics
  • Multimedia analytics
  • Optimization and simulation
Questions asked in advanced analytic scenarios:
  • Why is this happening?
  • What if…
  • What will happen next?
  • What is the best outcome?

Consider the various IT risks that may come with an off-field data architecture

Overarching IT Risks
  • Business intelligence is high in importance but low in effectiveness within the sports entertainment sector, showcasing the importance but struggle of success in this business function.
  • Data architecture is low in importance but high in effectiveness within the sports entertainment sector, demonstrating the lack of value the industry sees for this business function.

Legend for the adjacent Management & Governance Framework with red background indicating functions to 'IMPROVE', yellow 'EVALUATE', light green 'MAINTAIN', and dark green 'LEVERAGE'.

Off-Field Management & Governance Framework with two functions highlighted 'Business Intelligence & Reporting' and 'Data Architecture'.
(Source: Info-Tech’s Management and Governance Benchmarking Report for Sports Entertainment.)

Consider the various IT risks that may come with an off-field data architecture

Overarching IT Risks Continued
  • Data quality is two ranks below average in the sports entertainment industry when benchmarked to all other industries.
  • This indicates that IT stakeholders do not find data quality to be as important. IT stakeholders should be thinking differently about this core service to meet market demand and be competitive, as relying on team performance is not sustainable.
  • The importance of analytical capability and reports is also low, even though analytics is typically the main driver of business growth, actionable insights, and success. IT stakeholders need to be thinking differently about this space.
'Core Service Importance Rankings' comparing 'All Segments' to 'SAL Segment'. 'Data Quality' is #4 in All and #6 in SAL, 'Analytical Capability' is #8 in All and #9 in SAL.
(Source: Info-Tech’s IT Stakeholder Satisfaction Benchmarking Report for Sports Entertainment.)

Step 1.2

Information Assessment

Activities
  • 1.2.1 Document Your Current Data Management Maturity
  • 1.2.2 Assess How Well Information Supports Business Capabilities

This step will walk you through the following activities:

  • Assess how well information supports capabilities
  • Evaluate accessibility to data for key capabilities
  • Determine what areas are not being supported by data
  • Document your current data management maturity

This step involves the following participants:

  • Stakeholder working group
  • Other key business stakeholders as appropriate

Outcomes of this step

  • Identification of capability data support
  • Comprehend your current data management maturity

Define a Working Group and the Current State

Step 1.1 Step 1.2

Determine and document the business context for an off-field data architecture

Develop an understanding of the strategic plans, goals, and current state of the business; you may have already completed a goals cascade, business strategy, or data maturity assessment that you can leverage.

If you do not already have supporting documentation, leverage Info-Tech resources to get this done:

Discover Business Goals & Capabilities

The business goals and capabilities blueprint helps you uncover what IT knows and needs to know about the business context. This is a necessary first step to begin each of Info-Tech’s strategic IT initiatives and to uncover business context gaps.

Data Quality Diagnostic

The data quality diagnostic assesses what the data quality satisfaction is within your organization, compared to satisfaction across different departments, and helps you understand data issues at an individual respondent level. Additionally, you can uncover which data is most important and more error-prone.

Data Culture Diagnostic

The data culture diagnostic is used to understand how your organization scores across ten different areas relating to data culture to determine whether the organization perceives data to be an asset or not. Contact your rep for this diagnostic.

The data quality and culture diagnostics help identify information gaps

Assessing how well information supports capabilities is nearly impossible to perform without an honest and thorough understanding of end-user sentiment toward data, reporting, and analytics.

Develop data-driven insights to help you decide which business capabilities require new or improved reporting and analytics and find opportunities to improve business processes, and by extension, enable the capabilities of the business.

The Data Quality and Culture Diagnostics will help you:
  • Assess data quality and reporting satisfaction at a glance.
  • Evaluate data quality across nine dimensions of quality.
  • Evaluate reporting across ten dimensions of satisfaction.
  • Determine which areas are the most critical.
  • Determine effectiveness of analytics tools.
  • Determine whether the organization perceives data to be an asset.

1.2.1 Document your data business context

1-2 hours

Input: Business context, Data Quality diagnostic, Data Culture diagnostic

Output: The current data management maturity of the organization

Materials: Completed diagnostics, Presentation Template

Participants: Stakeholder working group

  1. Gather your working group to discuss the results of the Data Quality and Data Culture diagnostics.
  2. Discuss and determine what your current data management maturity is, based off business context and the diagnostics completed.
  3. Document the results of the data management maturity in the Presentation Template.

Info-Tech Insight

The path to "one view" of the fan is a systematic journey aligned with the level of data maturity and fluency.

Download the Presentation Template

The sports entertainment business capability map

Business Capability Map Defined…

In business architecture, the primary view of an organization is known as a business capability map.

A business capability defines what a business does to enable value creation, rather than how. Business capabilities:

  • Represent stable business functions.
  • Are unique and independent of each other.
  • Typically will have a defined business outcome.

A business capability map provides details that help the business architecture practitioner direct attention to a specific area of the business for further assessment.

Sports entertainment business capability map with the sports entertainment value chain as column headers, 'Scouting & Recruiting', 'Player Development', 'Team/Venue Operations', 'Fan Engagement', and row headers 'Defining Capabilities', 'Shared Capabilities', and 'Enabling Capabilities'.

Download the Full Business Capability Map for Sports Entertainment

Information Assessment

Assess the availability and quality of data in providing information as a business asset.

  • Information is central to every sport entertainment organization’s success and ability to realize its goals. Too often organizations experience the following pains:
    • Duplicated or conflicting data residing in disparate databases.
    • Inadequate controls or edits on data.
    • Manual rekeying of data into multiple systems.
    • Inability to provide executives with reliable and easily accessible information for decision making.
    • Inability to provide sponsors with reliable data for transparency and measurability to their sponsorship efforts.
    • Unable to use data to create better market targeting.
    • Inability of business units to assume “ownership” of data.
    • Inability to develop insights through data and analytics to improve fan hyper-personalization, experience, and engagement.
  • These organizations are driven by the desire to effectively manage existing business processes while recognizing the need for a faster ability to share data, information, and insight across multiple systems and business units to support increasing demands for more rapid response.
  • A primary goal of a strategy is to provide a framework that enables information to be viewed as a critical business asset, across organizational boundaries, and accessed as seamlessly as possible.
  • Through effective strategy design, IT can provide integration of data across business units by performing an analysis of how well the organizational capabilities are supported by information. Specifically, IT should analyze and assess data on the basis of quality, integrity, and ownership and on the presence of an effective data governance framework.
Assess how well existing information supports capabilities
Color-coding legend item for 'NONE' is red. NONE: Data is unavailable, unreliable, duplicated, or not of sufficient detail
Color-coding legend item for 'LOW' is yellow. LOW: Data is available but not subject to adequate integrity or quality controls. Data ownership is undefined.
Color-coding legend item for 'MEDIUM' is yellow-green. MEDIUM: LOW + Data is available but not fully automated. Data ownership is mostly defined.
Color-coding legend item for 'HIGH' is green. HIGH: MEDIUM + Data is available, of high quality, fully automated, and has clear ownership.

Figure above: Information Assessment Legend

Information support of key capabilities

The Business Capability Map for Sports Entertainment with information support level for key capabilities color-coded to a legend. i.e.. Level 1 capability 'Contract Management' is coded 'MEDIUM', but Level 2 capabilities within it 'Partner/Sponsor Management' and 'Trademark & Licensing' have 'HIGH' information support.
Note: Illustrative Example. To edit and customize this visual go to the appendix of the Presentation Template.

1.2.2 Assess how well information supports business capabilities

1-2 hours

Input: Business context, Sports entertainment business capability map

Output: Clear understanding on how well the existing information and data supports business capabilities

Materials: Sports entertainment business capability map, Presentation Template

Participants: Stakeholder working group, Other key business stakeholders as appropriate

  1. Gather your working group to discuss the availability and quality of current data to provide information as a business asset.
  2. Analyze and assess data on the basis of quality, integrity, and ownership and on the presence of an effective data governance framework.
  3. Use the following color scheme in assessment of your data:
    • None = Red: Data is unavailable, unreliable, duplicated, or not of sufficient detail.
    • Low = Yellow: Data is available but not subject to adequate integrity or quality controls. Data ownership is undefined.
    • Medium = Light Green: Low + data is available but not fully automated. Data ownership is mostly defined.
    • High = Dark Green: Medium + data is available, of high quality, and fully automated with clear ownership.
      • Use the eyedropper tool for easy completion of this activity.
  4. Use the output of this activity to build your case on why you need an off-field data architecture by providing a visual of where data lacks in supporting the business.

Download the Presentation Template

Off-Field Data Reference Architecture Guide

Phase 2

Discover Data-Driven Digital Opportunities

Phase 1

1.1 Build an Accurate Depiction of the Business

1.2 Document Your Current Data Management Maturity

1.3 Assess How Well Information Supports Business Capabilities

Phase 2

2.1 Off-Field Data Reference Architecture

Phase 3

3.1 Construct the Business Case

3.2 Determine the Business Case Impact to the Organization

This phase will walk you through the following activities:

  • Understand what an off-field data architecture is.
  • Discover what data-driven digital opportunities exist for value creation.
  • Read through conceptual and logical data model examples for value creation.

This phase involves the following participants:

  • CIO
  • CEO
  • CFO
  • CMO
  • CDO
  • Other stakeholders as appropriate

Step 2.1

Off-Field Data Reference Architecture

This step involves the following participants:

  • CIO
  • CEO
  • CFO
  • CMO
  • CDO
  • Other stakeholders as appropriate

Outcomes of this step

  • Understand what an off-field data architecture is.
  • Discover what data-driven digital opportunities exist for value creation.
  • Read through conceptual and logical data model examples for value creation.

Discover Data-Driven Digital Opportunities

Step 2.1

Together, data architecture and data modeling fill in the gaps between business goals and technology

Data Architecture vs. Data Modeling

Data Architecture

  • Data architecture is 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.

Data Modeling

  • Data modeling is a fast-paced activity responsible for the logical and physical data models as part of sprints.
  • Conceptual data models are high-level models that present abstract business concepts in an easy, consumable way that tells the story of the organization.
  • Logical data models are models that describe the data with as much detail as possible.
  • (Source: Pediaa, 2019)
Info-Tech’s Traditional Data Architecture Framework
Sample of Info-Tech's Traditional Data Architecture Framework.

Visit the Big Data Architecture and Implementation Plan blueprint to learn more

Info-Tech’s Data Modeling Framework
Sample of Info-Tech’s Data Modeling Framework.

Visit the Create and Manage Enterprise Data Models blueprint to learn more

Use transactional & analytics data to support the business

The data reference architecture must support both forms, based on the profile of users and applications.

Data Architecture

Supports applications that drive the ongoing operations of an organization. Activities could include sales, billing, reservations, venue management, and administration. Transactional databases are typically designed for high responsiveness and integrity under all operational conditions.
Arrow pointing right.

Architecture

Analytics Data

Is accumulated from transactional data sources to provide business intelligence and decision support capabilities. Analytical data repositories can take the form of a data warehouse, data lakes, data marts, and other specialized forms, optimized for efficiency across massive volumes of accumulated data.

To maximize insights, key stakeholders must better understand who their fans are with a 360-degree view to their demographics, the brands they like, the products they buy, and the media they consume.

When developing strategies for sponsorship, advertising, amenities, event format, venue design, or loyalty programs, results can be strengthened by considering your target audience and what’s most important to them.

Through industry-leading products such as the PRIZM segmentation and MobileScapes, Environics Analytics is uniquely positioned to take fan data further, leading to an enhanced and positive fan experience.” (Environics Analytics, 2022 interview)

Unification of data from multiple disparate sources yields powerful and potentially unexpected insights

Creating “one view” of the fan for enhanced insights requires expanded data and integration capabilities.

  • Data sources are vastly larger today.
  • Sources include traditional & cloud repositories, IOT sensors including cameras, etc.
  • High bandwidth pictures, videos, AR, VR, and MR technologies are now mainstream within a digital-first world, especially in the sports entertainment industry.
  • "Headless" application architectures with efficient integration capabilities allow true best-of-breed solutions to be deployed and replaced as necessary or as new technology becomes available.
  • Connectivity and 360-degree data access is necessary for many digital use cases.
(Source: Forbes, 2019)

“The use of analytics in the sports entertainment industry will only increase as the deluge of data increases. For example, teams can now optimize sponsorship negotiations and save millions through data analytics.

“The market for sports analytics is expected to reach almost $4 billion by 2022, as it helps a variety of sports organizations in a range of areas.

“Data from customer engagement within the stadium can be captured through electronic tickets - and even fingerprint or retinal scans - to understand fan movements.

“Sports entertainment organizations can mine sentiment from social media streams to understand what fans are thinking and can use analytics to engage those fans via social channels.

Info-Tech Insight

Traditional architecture typically features monolithic data repositories and data pre-integrated by ETL or other means. Whereas evolved digital-capable architectures support multiple real-time federated data sources and flexible real-time integration.

New data sources and demands for real-time information, require expanded integration capabilities for both transactional and analytical applications

  • Rapidly growing portfolio of digital data sources (social media content, streaming data, multi-media) demands new approaches.
  • Advanced real-time integration methods, including enterprise service bus (ESB) technology, provide multi-directional distribution of data, as necessary.
  • Master data management (MDM) enables filtering and analyzing of big data for the people, places, and things the organization cares about.
  • The variety and unstructured nature of some data requires more sophisticated data repositories.
  • Analytics capabilities need to be expanded to handle the variety, volume, and velocity of available data.
  • New applications leverage reporting and visualization in new ways to integrate information and generate new insights.

Off-Field Data Reference Architecture for the Sports Entertainment Industry

Examples of external data points that may be required from the organization to generate new and intuitive insights through the data architecture.
Reference architecture with notes on the top external data points and the bottom channels.
Examples of applications and channels that an organization may want to integrate to gather information to reach fans.

Leverage industry roundtables and trend reports to understand what’s possible

Refer to the organization’s business goals and initiatives and the Fan Experience Strategic Foresight Trend Report to identify data-driven digital opportunities.

Uncover important business goals, initiatives, and industry trends that can inform technology innovation and create data-driven digital opportunities. Through the Fan Experience Strategic Foresight Trends Report you will understand what drives these trends to be weak, medium, strong, or superior within the sports entertainment industry and how data plays a part in enabling them.

Explore trends in areas such as:

  • Advanced wireless
  • Digital ecosystem
  • Digital sustainability
  • Vision recognition
  • Immersive and gamified sports
While you ensure that your organization’s business goals and initiatives are being evaluated for data-driven digital opportunities, it is also critical to do your market research. Identifying factors external to your organization and identifying technology innovation that will provide a competitive edge is crucial to competing in a data-driven and digital-first world.
Sample of the 'Discover High-Value Fan Experiences' blueprint.
Sample of the '2022 Tech Trends' blueprint.

Visit the Fan Experience Strategic Foresight Trends Report

Visit Info-Tech’s Trends & Priorities Research Center

Sample of the organization goals cascade from before.

Trends powered by the right data architecture will create data-driven digital opportunities

1. TRENDS

    Digital Ecosystem
  • Mobile integration & automation platforms
  • Stadium/team apps
  • Mobile ticket platforms
  • Public engagement through digital and social media
  • Contactless POS systems
  • Digital signage, IPTV
    Vision Recognition
  • Smart access control systems
  • Real-time crowd intelligence
  • Touchless access using facial recognition
  • Facial recognition for COVID-19 screening
  • Palm reading for payments & access control
  • Biometric security

2. FUNNELED THROUGH AN OFF-FIELD DATA ARCHITECTURE

3. ENABLE NEW DATA-DRIVEN DIGITAL OPPORTUNITIES

  1. Food & Beverage Pre-Ordering by Mobile App
  2. Fitbit Challenge
  3. Fast Pass Premium Ticketing

Download Build a Data Integration Strategy

Download Build an Application Integration Strategy

Digital opportunities for value creation

Its important to understand how the data-enabled digital opportunities will impact your organization.

Example:

Data-Enabled Digital Opportunities

New Customer

New Market

New Business Model

New Product or Service

Incremental Value

What opportunities can have a high or immediate impact on the organization? Target new customer segments for existing products or services Enter or create new markets by applying existing products or services to different problems Adjust the business model to capture a change in how the organization delivers value Introduce new products or services to the existing market What value is the organization receiving from this opportunity?

A. Food & Beverage Pre-Ordering

Online mobile app for F&B pre-ordering + concession order handling New/enhanced mobile app with hyper-personalization Improved service delivery

B. Premium Fast Pass Ticketing

New entitlement to premium ticket holders New premium ticket for faster access to venue New ticket option available through mobile app, (potentially exclusively via mobile app) Increased revenue

C. Fitbit Challenge

Corporate partnership with Fitbit Use externally streamed data from corporate partner to drive fan engagement New/enhanced mobile app and aggregation data service Strategic alliances

Info-Tech Insight

Incremental value that an organization can receive from different data-enabled digital opportunities can vary. Some others to consider are cost savings and improved risk management.

Digital opportunity value chain analysis

A. Food & Beverage Pre-Ordering Scenario

Analysis of the value chain capabilities impacted in the Food & Beverage Pre-Ordering scenario. In Value Stream 'Team/Venue Operations' the Value Chain 'Food & Beverage Management' has all three capabilities impacted.

Greatest Risks
  • Synchronization or harmonization of mobile app functionality and POS system at concession
  • Timely order fulfillment and delivery
  • Internal technical expertise level
Complexity
  • Integrating the needed features in mobile app can be challenging
  • Integrating the app with current technology and projects at the venue
  • Cost
Key Benefits
  • Allows fans to have a personalized experience that maximizes their enjoyment of the game
  • Creates efficient operations through modernization of processes and procedures
Dependencies
  • App usability and UX
  • Strong in-venue fulfillment processes
  • Software creation
Financial Revenue Impact
  • Increased F&B revenue
  • Increased ticket sales
  • Increased app usage leads to more cross-selling opportunities

Exemplar conceptual data model

A. Food & Beverage Pre-Ordering Scenario

A high-level model that presents abstract business concepts in an easy, consumable way that tells a story.

Survey data shows a desire among fans to pre-order food and drinks before a game or other events.

  • 50% of respondents indicated they were excited about the possibility to pre-order food and drink.
  • 67% said they would buy more if they could pre-order and avoid the line.
  • (Source: Oracle, 2021)

Exemplar conceptual model for scenario A.

Metrics:

  • % mobile app fans engaged by offer
  • $ spend, increase, and impact on loyalty
  • % of fans who pick deliver to seat vs. over counter
  • Time to order
  • # of retained season ticket holders

Exemplar logical data model

A. Food & Beverage Pre-Ordering Scenario

A model that describes the data with as much detail as possible.

Exemplar logical model for scenario A.

Digital opportunity value chain analysis

B. Fast Pass Premium Ticketing

Analysis of the value chain capabilities impacted in the Fast Pass Premium Ticketing scenario. In Value Stream 'Engage Fan' the Value Chain 'Fan Success Management' has all five of eight capabilities impacted.

Greatest Risks
  • Maintaining effective business and technical interfaces with external ticketing partners
  • Synchronization of mobile app data with external systems
Complexity
  • Providing necessary features in mobile app
  • Implementing the app alongside current technology and projects at the venue.
  • Handling of streaming data sources (ticket scanners)
  • Cost
Key Benefits
  • Gives fans the VIP experience they desire
  • Potentially resolves traffic flow problems at venue entrances.
Dependencies
  • Fan response to premium pricing of new ticket class
  • Software development
Financial Revenue Impact
  • Increased revenue from premium ticket sales
  • Fans who opt for "Fast Pass" tickets despite elevated price are prime candidates for additional VIP cross-promotions

Exemplar conceptual data model

B. Fast Pass Premium Ticketing

A high-level model that presents abstract business concepts in an easy, consumable way that tells a story.

Survey data from Oracle determines that 57% of people would love a fast pass system to gain priority access to food and shops.

This was mostly popular with families as 62% highly rated the option. (Source: Oracle, 2021)

Exemplar conceptual model for scenario B.

Metrics:
  • % of fans opting for fast pass
  • Incremental revenue
  • Time to process fast pass
  • # of retained season ticket holders

Exemplar logical data model

B. Fast Pass Premium Ticketing

A model that describes the data with as much detail as possible.

Exemplar logical model for scenario B.

Digital opportunity value chain analysis

C. Fitbit Challenge

Analysis of the value chain capabilities impacted in the Fitbit Challenge scenario. In Value Stream 'Engage Fan' the Value Chain 'Fan Engagement Management' has four of five capabilities impacted.

Greatest Risks
  • Security of data streams from external source carrying personal information
  • HIPAA legislation
  • Synchronization of data stream data with mobile app
Complexity
  • Creating a new interface to display exercise data – not typically seen in COTS sports apps
  • Implementing the app alongside current technology and projects at the venue
  • Cost
Key Benefits
  • Creates a unique and engaging fan experience, with a compelling reason to use the mobile app more frequently
  • Extends the team brand across the domain of corporate partner for joint marketing and promotion
Dependencies
  • Fan interest in detailed player behaviors
  • Fan use of wearable technology
  • Software development
Financial Revenue Impact
  • Potential for cross-marketing and sales with another well-recognized brand
  • Opt-in data can identify prime candidates for premium follow-up promotions

Exemplar conceptual data model

C. “Fitbit” Challenge

The Minnesota Timberwolves have partnered with Fitbit to develop programs that engage players and fans, on and off the court.

The partnership facilitates many targeted opportunities, including challenges that reward participants with discounted tickets if they can keep up with their favorite player's daily workouts or the team's average weekly steps! (Source: NBA, 2017)

Exemplar conceptual model for scenario C.

Metrics:

  • % fans participating in challenge
  • Average activity for fans
  • Average activity for players
  • # of long-term engaged fans in challenge
  • # of retained season ticket holders

Exemplar logical data model

C. “Fitbit” Challenge

A model that describes the data with as much detail as possible.

Exemplar logical model for scenario C.

Off-Field Data Reference Architecture Guide

Phase 3

Build Your Case for an Off-Field Data Architecture

Phase 1

1.1 Build an Accurate Depiction of the Business

1.2 Document Your Current Data Management Maturity

1.3 Assess How Well Information Supports Business Capabilities

Phase 2

2.1 Off-Field Data Reference Architecture

Phase 3

3.1 Construct the Business Case

3.2 Determine the Business Case Impact to the Organization

This phase will walk you through the following activities:

  • Create vision and mission statements
  • Determine guiding principles
  • Build high-value use cases
  • Conduct a MoSCoW analysis
  • Map off-field data architecture components to the goals cascade
  • Measure your organization with metrics
  • Build your business case profile

This phase involves the following participants:

  • CIO
  • CEO
  • CFO
  • CMO
  • CDO
  • Other stakeholders as appropriate

Ensure you bring a compelling case to the table to receive buy-in from the rest of the organization

The activities within this phase should create a compelling business case to get buy-in from your business executives.
Use our Off-Field Data Reference Architecture Business Case Presentation Template to present to your business executives.

What is needed in a business case for buy-in:
  • Vision & Mission Statement

  • Guiding Principles

  • High-Value Use Cases

  • Prioritization of Use Cases

  • Metrics

Step 3.1

Off-Field Data Reference Architecture

Activities
  • 3.1.1 Create Vision and Mission Statements
  • 3.1.2 Determine Guiding Principles
  • 3.1.3 Build High-Value Use Cases
  • 3.1.4 Conduct a MoSCoW Analysis

This step will walk you through the following activities:

  • Create vision and mission statements
  • Determine guiding principles
  • Build high-value use cases
  • Conduct a MoSCoW analysis

This step involves the following participants:

  • Stakeholder working group
  • Other key business stakeholders as appropriate

Outcomes of this step

  • The vision and mission statement for an off-field data architecture
  • The guiding principles for an off-field data architecture
  • Identified business drivers that apply to the organization
  • High-value use cases
  • Prioritized use cases for making the case to business executives

Build Your Case for an Off-Field Data Architecture

Step 3.1 Step 3.2

Construct compelling vision and mission statements for your organizations off-field data architecture

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. 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.

Vision

Example: Unify our data to become the world's best fan-centric football club, where fans can have the utmost personalized experience, whether they are at home or in the venue.

(Source: Heller Search, 2020)

Mission

Example: We strive to offer all our fans a personalized experienced, whether they are at home or in the venue, through trusted data that is integrated across all products, services, and platforms.

(Source: Heller Search, 2020)
A mission expresses why the off-field data architecture exists.
  • 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.
Handshake.

3.1.1 Create vision and mission statements

1-2 hours

Input: Business context

Output: The vision and mission statement for an off-field data architecture

Materials: Whiteboard or paper and pen, Presentation Template

Participants: Stakeholder working group

  1. Gather your working group to create vision and mission statements for an off-field data architecture, to get buy-in from business executives.
  2. Each participant should create a statement of purpose (1-5 lines) describing the future of an off-field data architecture. Have them consider the following:
    • Vision:
      • What does an organization with an effective off-field data architecture look like?
      • How will our organization benefit and grow from an improved off-field data architecture?
      • What are our fans saying, feeling, and doing? Reflect on current-state data collection.
    • Mission:
      • Why does this off-field data architecture exist?
      • What problems are we trying to solve?
      • Who will benefit from this off-field data architecture?
      • How will we reach our target?
  3. Each participant should present their vision and mission. Discuss the common themes and then develop a concise vision and mission statement that incorporates the group’s ideas.
  4. Consolidate the findings and document the results in the appendix of the Presentation Template.

Download the Presentation Template

Design guiding principles for your organization’s off-field data architecture

Define the underlying general rules and guidelines (across the data architecture environment) that the organization will apply to use and deploy all business and IT resources and assets. Principles are intended to:
Provide an effective framework that will help the business make decisions about how it uses or implements data architecture technology. Idea lightbulb with icons referring to the surrounding text. Provide a guide to establish evaluation criteria and influence the selection of suppliers, business partners, components, and products or product architectures used for data.
Drive the functional requirements of a data solution’s architecture. Assist in the assessment of the existing data architecture environment to provide insights into the transition activities needed to strategically implement the principles in support of the business goals and priorities.
Use the Big Data Architecture Principles & Guidelines Template for examples and to start developing your guiding principles.

Download the Big Data Architecture Principles & Guidelines Template

Image of a person sitting at a desk with a laptop. To be effective, all principles, policies, and guidelines should be defined and agreed upon by both business and IT executives. Once they have been agreed to and published, the principles and policies provide a framework that can be used to explain and justify why certain business and IT decisions are being made or need to be made.

3.1.2 Determine guiding principles

1-2 hours

Input: Business context

Output: The guiding principles for an off-field data architecture

Materials: Whiteboard or paper and pen, Big Data Architecture Principles & Guidelines Template, Presentation Template

Participants: Stakeholder working group

  1. In your working group, brainstorm guiding principles related to key business objectives and goals of data. These will help you develop the practice's ultimate principles. Use the Big Data Architecture Principles and Guidelines Template to assist in this. Try to create five to ten principles.
  2. In smaller groups, each group will take one of the values and determine:
    • Off-field data is…
    • The business can…
    • The off-field data architecture…
  3. Get back together a group to discuss the principles:
    • How can these principles help guide the practice's planning?
    • How will these principles help to correct and guide fan behavior?
  4. Finalize and document the guiding principles in the appendix of the Presentation Template.

Download the Big Data Architecture Principles & Guidelines Template

Download the Presentation Template

Determine high-value use cases for informing the business case

  • During business context interviews your organization should've been able to identify the high-priority initiatives and needs for your organization that an off-field data architecture can support.
  • 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 or lines of business will be impacted and how you will measure success.

Leverage Info-Tech’s data requirements and mapping methodology for creating use cases

  • 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.
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?
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 or processes will be impacted or improved if we solve this?
  • What compliance/regulatory/policy concerns do we need to consider in any solution?
  • What measures of success or change should we use to prove the value of the effort (KPIs/ROI)?
Breakout session #3
  • What are the steps in the process or activity today?
  • What are the applications or systems used at each step?
  • What data elements (domains) are involved, created, used, or transformed at each step?

3.1.3 Build high-value use cases

2-4 hours

Input: Business context, Subject area expertise

Output: Identified business drivers that apply to the organization, High-value use cases

Materials: Whiteboard or paper and pen, Data requirements and mapping methodology, Presentation Template

Participants: Key business stakeholders, Stakeholder working group

  1. Bring together key business stakeholders (data owner, stewards, SMEs) from a particular line of business as well as the data custodian to build cases for their units. Data-driven digital opportunities were already identified in phase 2; leverage them or create new ones if you would like to.
  2. Leverage Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases.
      Document the following items:
    • Risks
    • Opportunities
    • Pains
    • Challenges
    • If perfectly solved scenario
    • Impacted/improved capabilities
    • Concerns
    • KPIs
    • Steps in the current process
    • Applications used at each step
    • Data elements involved, created, used or transformed
  3. Have the stakeholders move through each breakout session, using flip charts to brainstorm and to document thoughts.
  4. Debrief and document results in the appendix of the Presentation Template. Repeat this exercise with as many lines of business as possible.

Download the Presentation Template

Conduct a MoSCoW analysis

Direct strategic IT investments based on the collective output of the use-case assessments.

When combined with a solid understanding of business priorities and the vision and mission, a use-case assessment can be the driving force that informs a unified perspective on the sequencing of an organization’s strategic IT initiatives.

Assessments based on how well a use case is supported by people (via organizational analysis), process (via process review), and technology (via application, infrastructure, data, and security improvements) will inform the overall health of a use case, or in other words, the size of a gap. This information, when contrasted with the concept of a MoSCoW-based effort to value, forms an enhanced decision-making framework that can be used to determine initiative sequencing on a strategic roadmap.

If a use case has a large gap (is poorly supported by people, process, data, or technology), it should be considered as high effort, or difficulty, to address. When the use case is well aligned with business priorities and the vision and mission, the use case gap should be considered as high value, low effort to address.

See the figure on the right: IT leaders should focus their efforts on the lower-right quadrant (high value, low effort). In the top-right quadrant (high value, high effort), IT should seek business support to drive the initiative. Use case gaps on the right side of the quadrant overall are good candidates for use case outsourcing.
Layout of a MoSCoW analysis with the 'Must Address LE/HV' quadrant highlighted.

3.1.4 Conduct a MoSCoW analysis

2-3 hours

Input: Business context, Use cases

Output: Prioritized use cases for making the case to business executives

Materials: Whiteboard or paper and pen, MoSCow analysis chart, Presentation Template

Participants: Stakeholder working group

  1. Bring together the working group and discuss the following questions to determine where each use case belongs on the MOSCOW analysis:
    • How well is this use case supported by internal and external customers? People?
    • What would the process for this use case look like? Is it achievable?
    • Do we have the technology to support this use case? If not, what kind of improvements would have to be done? Are they achievable (application, infrastructure, data, security)?
  2. If the use case is poorly supported by people, process, or technology it should be considered high effort, or difficulty, to address.
  3. If the use case is well aligned with business priorities and the vision and mission, it should be considered high value, low effort to address.
  4. Organizations should focus on the high value, low effort use cases, where they will have to seek business support to drive initiatives in the high value, high effort quadrant. Overall, use cases on the right side of the quadrant are good candidates to present to business executives.
  5. Document your findings within the appendix of the Presentation Template.

Download the Presentation Template

Step 3.2

Determine the Impact to the Organization

Activities
  • 3.2.1 Map Off-Field Data Architecture Components to the Goals Cascade
  • 3.2.2 Measure Your Organization With Metrics
  • 3.2.3 Build Your Business Case Profile

This step will walk you through the following activities:

  • Map off-field data architecture components to the goals cascade
  • Measure your organization with metrics
  • Build your business case profile

This step involves the following participants:

  • Stakeholder working group

Outcomes of this step

  • Enhanced goals cascade/business strategy
  • Metrics and measures that determine the impact that the off-field data architecture will have on the organization
  • Business case profile

Build Your Case for an Off-Field Data Architecture

Step 3.1 Step 3.2

An off-field data architecture should align with your goals cascade

The identified digital opportunities that are supported through off-field data architecture and modeling align with the following IT goals, which in the end are achieved through the various initiatives, improve the data architecture and other IT capabilities, and support the various organizational goals, initiatives, and capabilities.

Example organization goals cascade from before with a 'Data-Enable Digital Opportunities' inserted between the 'IT Goals' and 'IT Initiatives' columns on the right side. The first column on the left side is 'Organization Goals' listing four business goals, some of which are color-coded similarly. The second column on left side is 'Organization Initiatives' with three initiatives, each color-coded to match the business goal(s) they help to achieve. The third column on the left side is 'Organization Capabilities' with capabilities grouped by a category such as 'Fan Scoring', each of which are color-coded to business goals and the business initiatives that create or improve them. On the right side there is similarly 'IT Goals' with two goals listed, 'Data-Enabled Digital Opportunities' has three opportunities listed, they each are achieved through many 'IT Initiatives' which are created or improved by 'IT Capabilities', of which 'Data Architecture' is highlighted.. The capabilities of either side support each other.

3.2.1 Map off-field data architecture components to the goals cascade

1-3 hours

Input: Business context, Goals cascade/business strategy, Off-field data architecture

Output: Enhanced goals cascade/business strategy

Materials: Whiteboard or paper and pen, Presentation Template

Participants: Stakeholder working group

  1. Gather your working group and start to map out what components of your off-field data architecture enable the IT goals that support the organizational goals to better illustrate the value to business executives.
  2. Document your work in the appendix of the Presentation Template.

Info-Tech Insight

Strategic organizational goals rely on data to be achievable. Without data, the goals are simply statements; with data, the statements become a reality.

Download the Presentation Template

Use metrics to measure business success

Consider the following metrics to measure your current state and continue to measure these metrics year by year to see your organizations improvements

Outcomes Metrics Impacts Measures

Fan engagement

Fan engagement rate Increase fan engagement within a year
  • Fan engagement rate = (likes + comments + shares / total of followers) x 100
  • Annual fan survey

Improved back-end intelligence

Return on investment (ROI) Improve data intelligence and efficiencies within a year
  • Refined, automated, and centralized real-time data collection
  • ROI = (net return on investment / cost of investment) x 100%

Personalization

Conversion rate, click through rate Through data and analytics, personalization can be improved where conversion rates and click through rates will increase, resulting in actionable results
  • Conversion rate = (conversions / total visitors) x 100%
  • Click through rate = clicks / impressions

Actionable analytics

Return on engagement, monthly recurring revenue With the improved service of data reports, sports entertainment organizations can create actionable and valuable decisions to satisfy fans and improve engagement
  • Return on engagement = ((benefits – costs) / costs) x 100
  • Monthly recurring revenue = new fan subscription revenue + existing fan subscription revenue + add-on and license upgrade fees - lost revenue from churned fan accounts & license downgrades or removed add-ons

Superior fan relationship

Fan lifetime value, churn rate By understanding fans better with improve data intelligence, organization can create superior relationships with fans, resulting in high lifetime value and lower churn rate
  • Average fan value = average purchase x average purchase in one year
  • Average fan lifetime value = average fan value x average fan lifetime
  • Churn rate = (fans beginning of month – fans end of month) / fans beginning of month

Efficient venue management

Employee satisfaction, operating cash flow, operating profit margin Better data intelligence allows for improving operations efficiencies, making tasks easier for employees and more efficient
  • Employee satisfaction survey; Employee NPS = # of promoters - # of detractors
  • Operating cash flow = operating income + depreciation - taxes + change in working capital
  • Operating profit margin = operating income / net sales revenue

Effective fan service

Net promoter score With improved venue management, effective fan service should follow, improving the quality of the experience where net promoter scores are expected to increase
  • Net promoter score allows organizations to calculate the difference between fans who love and are satisfied with the organization and those who aren’t.
  • NPS = # of promoters - # of detractors
  • Annual fan survey
(Sources: CFI, n.d; Disruptive Advertising, 2020; Personify, 2019; Salesforce, n.d.; StriveCloud, 2020.; Salesforce, How to Calculate Customer Churn Rate and Revenue Churn Rate, n.d.; Hubstaff, 2020.)

3.2.2 Measure your organization with metrics

2-3 hours

Input: Business context, Metrics

Output: Metrics and measures that determine the impact that the off-field data architecture will have on the organization

Materials: Whiteboard or paper and pen, Presentation Template

Participants: Stakeholder working group

  1. Gather your working group and consider the given metrics to determine if they are good measures for what your organization is trying to accomplish. If not, brainstorm or research some metrics that you will be able to use to properly measure your organization.
  2. Document the outcome, metric, impact, and measure of the metrics your organization wants to use within the Presentation Template.
  3. Calculate any of the metrics that you can do.
  4. If there are metrics you cannot currently calculate, discuss with the working group when and who will be accountable for doing so. Record this in the Presentation Template.
  5. Discuss how frequently your organization will measure these metrics to determine the impact it is having on the organization (yearly, quarterly, etc.) and who will be responsible for what metrics. Record this in the appendix of the Presentation Template.

Download the Presentation Template

Business case profile for your off-field data architecture

Example:

Add the initiative name and its value statement.

Although it will be difficult to get an accurate estimate for time and dollars required for initiatives while building the strategy, a ballpark within +/- 100% of the actual will help to begin to budget and scope the roadmap.

Sample of the business case profile for Off-Field Data Architecture show on the next page.

Brainstorm risks and dependencies for this initiative. These could be external or internal risks, dependencies, or other initiatives or resources.

Outline the benefits achieved by completing this initiative. Think about the specific capabilities or strategic goals they will help support.

Brainstorm who would be the main stakeholders involved in the initiative.

Develop and implement an off-field data architecture

Develop and implement an off-field data architecture that unifies fan data through integration across all products, services, and platforms our organization provides, to become fan-centric and provide personalized experiences.

Incremental Cost:

$10,000 LABOR

$20,000 SYSTEMS

$10,000 CONTRACTS

$40,000 TOTAL

Initiative Description:
  • An off-field data architecture will support the existing organizational and IT goals that are set out, and it can also activate additional opportunities for fans. Hiring and training staff to maintain and assure data quality while also analyzing data to create insights will be required. The way we create experiences and engage with fans will become more personalized with off-field data, which is a key outcome of the goals cascade for the organization.
Project Timeline:

Example project timeline split into quarters, with the marker reading from 'Q1 2022' through 'Q3 2022'.

CIO comments:

“An off-field data architecture that allows for better insights on fan data and ease of creating personalized experiences will allow for us to create better relationships and experiences with fans to lead into higher engagement, which aligns with our digital fan engagement strategy.”

Primary Business Benefits:

Up Arrow.
Collect efficient off-field data on fans to develop more personalized experiences

Other Expected Business Benefits:

Up Arrow.
Enhance fan profiles to determine who is who
Down Arrow.
Decrease the room for error on marketing initiatives with data-driven decision making
Up Arrow.
More opportunities are showcased and developed (upselling)
Project Team:
  • Business Sponsor: Daniel
  • IT Sponsor: Jessica
  • PM: Alex
Risks:
  • Ongoing maintenance and data quality assurance
  • Data maturity and culture of the organization
Dependencies:
  • Development of data architecture
  • The potential software needed to collect data
  • Hiring staff to maintain and analyze the data
  • Training

(Note: This is an example; therefore, the information here is not accurate, and the cost, timeline, etc. do not reflect real life precisely.)

3.2.3 Build your business case profile

1-2 hours

Input: Business context, Previous activities conducted

Output: Business case profile

Materials: Whiteboard or paper and pen, Presentation Template

Participants: Stakeholder working group

  1. Gather your working group, and discuss the different components needed to fill in the business case profile.
  2. Complete the business case profile within the appendix of the Presentation Template to then present to business executives.

Download the Presentation Template

Summary of accomplishment

Get buy-in from business executives.
  • You have now completed the Presentation Template and the various activities it contains to present to your business executives.
  • This presentation should be able to tell the story of why an off-field data architecture is crucial for your sports entertainment organization.
  • Not only does it drive a sustainable future for your team, but it will also support IT and organizational goals, bringing more than one purpose to this data architecture.
  • Once you get buy-in from your business executives, it is important to build a strategy around your data architecture to ensure success.

Additional Support

If you would like additional support, have our analysts guide you through other phases as part of an Info-Tech Workshop.

Photo of Elizabeth Silva. Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

To accelerate this project even further, engage your IT team in an Info-Tech workshop with an Info-Tech analyst team.

Info-Tech analysts will join you and your team at your location or welcome you to Info-Tech’s historic Toronto office to participate in an innovative onsite workshop.

The following are sample activities that will be conducted by Info-Tech analysts with your team:

Sample of the MoSCow Analysis activity from this blueprint.
MoSCow Analysis
Conduct a MoSCow analysis to define which high-value use cases pose the greatest value and lowest effort to determine what use cases should or could be addressed.
Sample of the Business Case Profile activity from this blueprint.
Business Case Profile
Create a business case profile to present the project on one page. One-page profiles are adequate ways to showcase and explain an initiative from top to bottom.

Off-field data architecture considerations and next steps

The Off-Field Data Architecture Guide can assist with the following blueprints:

1

Data Strategy
Sports entertainment organizations are evolving and becoming more and more data centric, with maturing expectations and demands. A strategy needs to be in place for an off-field data architecture to work.

Go to this Blueprint

2

Data Management
As organizations increase their need for data to become more strategic, and with a growing demand for data to better support business processes and inform decision making, it is important for data to be accessible and trustworthy.

Go to this Blueprint

3

Big Data Architecture
An increasing number of businesses, especially within sports entertainment, are starting to use data to make decisions. If a data architecture has not already been built, chances are one will be needed soon.

Go to this Blueprint

4

Data Model
Data models tell the story of the organization and its data in pictures to be used by a business as a tool to evolve the business capabilities and processes.

Go to this Blueprint

5

Master Data Management
IT and business leaders are recognizing the need to implement master data management (MDM) processes and technology to better manage enterprise master data.

Go to this Blueprint

6

Data Integration
Any time an organization changes applications or the database ecosystem it requires the organization to solve a data integration problem. Poor integration holds back critical functions.

Go to this Blueprint

7

Data Quality Diagnostic
Get a report showing the business’ evaluation of data quality so you can focus your improvement efforts to meet their needs.

Go to this Diagnostic

8

Analytics, BI & Reporting
In respect to business intelligence (BI) matureness, you can’t expect the whole organization to be at the same place at the same time. Your BI strategy needs to recognize this and should strive to align rather than dictate.

Go to this Blueprint

9

Privacy, Risk, and Compliance
Depending on the geographical area of your organization you may have regulations that you need to follow and be aware of to stay in compliance when it comes to fetching data.

Go to this Blueprint

10

Infrastructure
Infrastructure is left in a reactive mode, overwhelmed with work, while the business fumes at the delay. Follow the roadmap process to better interface with the business, becoming true strategic partners.

Go to this Blueprint

Research Contributors and Experts

Ian Greenwood
Account Executive
SAS

Dan Axman
Business Development for Sports Teams
SAS

Adam Bernatt
VP of Business Development
Environics Analytics

Tania O’Brien
Chief Marketing Officer
Environics Analytics

Jordan Rutner
Research Marketing Manager
KORE Software

Peter Kekesi
Product Manager for Sports
Data Talks

Igor Ikonnikov
Research Advisor
Info-Tech Research Group

Sharon Foltz
Managing Partner
Info-Tech Research Group

Andy Neill
Associate Vice President, Research
Info-Tech Research Group

Bibliography

Andrus, Aden. “What is Conversion Rate? How to Calculate and Improve Your Conversion Rate.” Disruptive Advertising, 15 April 2020. Accessed 9 Feb 2022.

Berry, Andy. “How sports clubs can use data to secure their futures.” CIO, 6 Dec. 2017. Accessed 31 Jan. 2022.

CFI Team. “Engagement Rate The level of engagement generated from a created content or a brand campaign” CFI, 8 Nov. 2019. Accessed 9 Feb 2022.

Data and Sports: A Marriage Made in Heaven, and the Cloud. Global Sports Innovation Center Powered by Microsoft, 2020. Accessed 7 Feb. 2022.

“How Data Can Unlock Fan Engagement.” Sportradar, n.d. Accessed 7 Feb. 2022.

“How to Calculate Customer Churn Rate and Revenue Churn Rate.” Salesforce, n.d. Accessed 9 Feb. 2022.

“How to Calculate Recurring Revenue.” Salesforce, n.d. Accessed 9 Feb. 2022.

Ingham, David. “Data in sport: driving fan engagement and elite performance.” Techradar, 13 July 2021. Accessed 2 Feb. 2022.

Lithmee. “What is the Difference Between Conceptual and Logical Data Model.” Pediaa, 15 July 2019. Accessed 28 April 2022.

Maheswaran, Rajiv. “The Next Way of Seeing Sports.” Second Spectrum, n.d. Accessed 17 Jan. 2022.

Manges, Maneesha. “Calculating ROE – Putting a Number Against the Engagement Metric.” Personify, 19 Nov. 2019. Accessed 9 Feb. 2022.

McCullough, Micheal. “How big data is changing sports fandom.” CPA Canada, 31 Oct 2019. Accessed 7 Feb. 2022.

Mintimberwolvespr. “Minnesota Timberwolves and Fitbit Announce Multi-Year Partnership Naming Company ‘Official Wearable,’ ‘Official Sleep Tracker’ and Jersey Patch Sponsor.” NBA, 20 June 2017. Accessed 29 April 2022.

Nevogt, Dave. “How to Measure and Improve Employee Satisfaction [Survey Included].” Hubstaff, 16 Oct. 2020. Accessed 9 Feb 2022.

Olavsrud, Thor. “What is data architecture? A framework for managing data.” CIO, 24 Jan. 2022. Accessed 28 April 2022.

Peters, Rich. “How to Write Your Data Vision and Mission Statements.” Heller Search, 7 Oct. 2020. Accessed 10 Feb. 2022.

Ricky, Abhas. “How Data Analysis In Sports Is Changing The Game.” Forbes, 31 Jan. 2019. Accessed 27 April 2022.

Saleh, Tariq. “Fan engagement: A game beyond the game.” Sportcal, 10 Dec. 2020. Accessed 4 August 2021.

Schnater, Bas. “Data Maturity Model: 61% of all sports organisations do not use data for their overall strategy.” Fan Engagement Customer Experience Marketing, 6 Jan. 2021. Accessed 7 Feb. 2022.

“SPORTS DATA SOLUTIONS Major League.” Stellaralgo, 2021. Accessed 7 Feb. 2022.

“Sports Innovation: The Power of Data.” N3xt Sports, 14 May 2020. Accessed 9 Feb. 2022.

“The power of fan data for sports organisations – how mature is your strategy?” Tappit, n.d. Accessed 9 Feb. 2022.

Trendell, Amber. “New research from Oracle shows US consumers are eager to return to stadiums provided certain changes are implemented.” Oracle, 23 March 2021. Accessed 28 April 2022.

Vanhaesebroeck, Jente. “Learn your customer lifetime value first, if you want to improve loyalty.” StriveCloud, 29 July 2020. Accessed 9 Feb. 2022.

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Guided Implementation #1 - Define a Working Group and the Current State
  • Call #1 - Develop working group; discuss and assess current data maturity.

Guided Implementation #2 - Discover Data-Driven Digital Opportunities
  • Call #1 - Walk through the off-field data reference architecture, and exemplar data models for value creation.

Guided Implementation #3 - Build Your Case for an Off-Field Data Architecture
  • Call #1 - Create vision and mission statements.
  • Call #2 - Determine guiding principles.
  • Call #3 - Build high-value use cases.
  • Call #4 - Conduct a MoSCoW analysis.
  • Call #5 - Map the data architecture components to the goals cascade.
  • Call #6 - Determine metrics for measuring success.
  • Call #7 - Build business case profile.

Author

Elizabeth Silva

Contributors

  • Ian Greenwood, Account Executive, SAS
  • Dan Axman, Business Development for Sports Teams, SAS
  • Adam Bernatt, VP of Business Development, Environics Analytics
  • Tania O’Brien, Chief Marketing Officer, Environics Analytics
  • Jordan Rutner, Research Marketing Manager, KORE Software
  • Peter Kekesi, Product Manager for Sports, Data Talks
  • Sharon Foltz, Managing Partner, Info-Tech Research Group
  • Igor Ikonnikov, Research Advisor, Info-Tech Research Group
  • Andy Neill, Associate Vice President, Research, Info-Tech Research Group
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