Here is a list of our publications to date – it is kept current as we release more publications.
[Last updated: July 1st, 2021]
- Jitendra Subramanyam (LinkedIn)
- Ben Hertzberg (LinkedIn)
- Kevin Gabbard (LinkedIn)
- Ariel Silbert (LinkedIn)
- Shelly Thackston (LinkedIn)
- Eugene, Walton
Our Research Coverage Areas
- Business Value of Data & Analytics
- Data & Analytics Strategy and Planning
- Data & Analytics Quality and Ethics
- Data & Analytics Talent
- Case Study: Getting Value From D&A Innovation Failures (Brussels Intercommunal Transit Company): NEW!
Brussels Intercommunal Transit Company built a culture of D&A innovation and realized significant savings in maintenance costs with no innovation budget, mandate or dedicated talent. This case study shows data and analytics leaders how to set up projects so that failure is as valuable as success.
- 3 Case Studies of Data- and Analytics-Driven Business Innovation: NEW!
Data and analytics leaders, learn here how three progressive peers have built data- and analytics-driven innovation into their engagements to rapidly generate business value.
- Case Study: A Culture of Data Literacy and Data-Driven Decision-Making (Froedtert & the Medical College of Wisconsin)
Find out how the CDAO at Froedtert addresses the human challenges that prevent business partners from making data-driven decisions through targeted coaching and relationship building.
- Case Study: Monitoring the Business Value of AI Models in Production (Georgia Pacific)
Do your predictive models leak value in production? Do you *know* if they do? Learn how Georgia Pacific monitors their AI models to ensure they generate the most value possible.
- Case Study: Entity-Event Knowledge Graph for Powering AI Solutions (Montefiore)Do data and analytic silos prevent your organization from rapidly developing AI solutions? Find out how Montefiore uses an event-entity knowledge graph to organize its data and accelerate the development of AI models.
- Case Study: AI Innovation with Startups (Stora Enso)
Learn how Stora Enso partners with early-stage startups to rapidly develop innovative, bespoke solutions to their business problems.
- Build the Data & Analytics Core and Deliver Value Simultaneously
Don’t fall into the trap of building up your talent and D&A core before delivering value; learn how D&A organizations do both at the same time here!
- Ignition Guide to Creating, Measuring, and Improving the Performance of a Machine Learning Model
Measuring the performance of a machine learning model is sometimes more of an art than a science. Follow these steps to measure and improve your machine learning model!
- 3 Ways to Monetize Data and Analytics
Learn how three progressive organizations overcame common roadblocks to monetize data and analytics!
- 13 Ways Real-World Companies Deliver Measurable Value with Data and Analytics
D&A is a value-generating activity. Ensure your team generates business value by following these 13 strategies sourced from real-world companies!
- Case Study: Answering Critical Business Questions with Graph Analytics (Jaguar Land Rover)
D&A is supposed to solve business problems. All too often, these efforts run aground because business units and data teams lack a shared understanding of data. Learn how JLR uses Graph to connect business and data domains in order to ask and solve difficult business questions.
- Case Study: KPI-Led Data and Analytics Digital Transformation (St. Luke’s)
Data and analytics leaders struggle to get business buy-in for transformation efforts. St. Luke’s justified their transformation as crucial to the organization’s ability to calculate cross-functional KPIs at scale. This won them buy-in and enabled rapid response to the COVID-19 crisis.
- Case Study: Data and Analytics Monetization with Knowledge Graphs and AI (Turku City Data)
Learn how knowledge graphs allowed Turku city’s data team to quickly monetize their data and analytics assets.
- Advanced Data and Analytics; What Do Leading Organizations Do?
Learn the perspectives, practices, and strategies that have made leading D&A organizations so advanced.
- Case Study: Data Monetization Through Data Product Development (ZF Group) Successful data monetization requires much more than just selling your organization’s data! Learn how ZF Group monetized their data by building and selling powerful data products.
- Tool: D&A Use Cases to Improve Operational and Financial Performance
As your organization transitions from immediate COVID-19 response to adjusting to new economic realities, use this tool to find real-world D&A use cases that improve operational and financial performance.
- Tool: 6 Ways Data and Analytics Leaders Can Serve Their Organizations During the COVID-19 Crisis
How can data and analytics leaders serve their organizations during the COVID-19 crisis? This presentation showcases companies that use D&A to cut costs, optimize business processes, spend efficiently on talent, maximize the value generated from existing spending, and accelerate with new.
- Tool: Practical Cost Optimization Techniques for Data and Analytics Leaders Responding to COVID-19
Data and analytics leaders are well placed to assist their organizations during the COVID-19 pandemic. This compendium provides critical resources and guides to quickly ramp up cost optimization plays with D&A.
- 6 Lenses for Discovering D&A Value Generation Opportunities
Click here to learn six lenses D&A leaders can use to identify new D&A-based value generation opportunities across their organization. Each lens comes from a real-world practitioner profiled in Gartner research.
- Tool: A Living Library of Real-World Data and Analytics Use Cases
This spreadsheet contains over 300 real-word D&A use cases. Filter use cases by organization size, industry vertical, and solution type. Updated quarterly.
- Library: Examples of How Data and Analytics is Used Across the Enterprise (Domain Data and Analytics)
D&A leaders can use this library to learn how their business partners are using data and analytics. The library collects and categorizes over 150 different case studies, tools, templates, and notes from across Gartner research.
- Infographic: Six Traps on the Road from Data to Value
Gartner research reveals six traps organizations fall into when they try to generate value with data. Avoid them by learning from the case studies referenced here!
- Case Study: Realizing the Promise of Analytics and BI Platforms (Dow)
If your organization is like most, you have an underused BI platform. Dow’s D&A leadership analyzes BI consumption patterns, intervenes strategically, and evangelizes successes. Their approach led to 25% more platform use and a 4-fold gain in revenue.
- Communicating through Data Visualization
Does your team struggle to get business partners to act on the insights you generate? This research helps D&A teams understand how to use visualizations to better communicate their conclusions.
- Performance Measurement for Data & Analytics Leaders Template (Utah Governor’s Office of Management and Budget)
The C-Suite expects D&A leaders to generate value, but many D&A leaders struggle to quantify the performance of their teams. This downloadable and editable template offers D&A leaders an easily implementable approach to performance measurement that follows Utah GOMB’s approach.
- Performance Measurement for Data & Analytics Leaders Tool (Utah Governor’s Office of Management and Budget)
Utah GOMB combines multiple performance indicators into a single, easily understood ratio. D&A leaders can follow Utah GOMB’s approach to quantify the performance of their teams. This downloadable presentation explains Utah GOMB’s approach and complements the template above.
- 5 Steps to Get Started with Machine Learning
Eager to get started with ML but afraid it will be too technically difficult, expensive, or time consuming? Click here to learn the 5 steps D&A teams from Micron, Iron Mountain, and Avon used to get started with ML.
- How to Reveal the Business Value of Imperfect Data with AI (Avon)
Imperfect data is worthless for business intelligence. But it can create business value, if organizations switch from BI to advanced analytics. Find out how Avon did so here.
- Data and Analytics Value Creation: Key Obstacles and How to Overcome Them
Learn what Chief Data & Analytics Officers polled during the 2019 D&A Summits in London and Orlando believe enables their organizations to create business value with data & analytics.
- Peer-Based Analytics Learning (ABB)
Frustrated with low analytics use in your organization? Take a lesson from ABB’s audit function: they use peer-led case studies to give auditors hands on experience in how analytics can improve audits.
- Machine Learning Literacy for Business Partners (Micron)
Do you have data scientists mired in dashboard creation? Or do they develop cool products that don’t meet business needs? Find out how Micron improves communication between data science teams and business partners with a simple ML literacy course.
- Machine Learning Literacy for Business Partners (Micron) Implementation Tool
Download Micron’s internal ML literacy syllabus here. It includes two case studies business partners can use to experience developing an ML solution on their own.
- Analytics Presentation Engagement Framework (NGA)
Too often, analysis falls on deaf ears, and excellent insights fail to drive value. See how analytics leaders at the National Geospatial-Intelligence Agency create analytics presentations that motivate business users to action.
- Opt-Out Decision Engineering to Increase Analytics Use
Business users often have powerful analytics tools available—but they rarely use them. Data and Analytics leaders can use an opt-out technique to shape the behavior of business users by “nudging” them into using of analytic tools.
- Decision-Focused Data Maps (General Mills)
Do people in your organization spend more time looking for the right data than using it to inform decisions? Find out how General Mills developed an easy-to-understand visual that connects crucial business questions to available data sources.
- Simple, Powerful Machine Learning Pilot (Iron Mountain)
Do worries about expertise and expense keep your organization from piloting ML projects? Find out how two FTEs in Iron Mountain’s A/R team developed a Machine Learning pilot off the side of their desks that decreased time to payment by 40%.
- From Data to Prediction (Iron Mountain): Further Details
Find out the specific steps Iron Mountain used to develop their Accounts Receivable late payment prediction pilot here.
- How to Build Momentum for Machine Learning (ML) Initiatives (Iron Mountain)
D&A leaders need to build on their successes with Machine Learning. Find out how Iron Mountain did so here.
- Case Study: Demand Management for Self-Service Data and Analytics Tools (SureSparkle*): NEW!
SureSparkle* proactively manages the demand for self-service data and analytics tools by targeting high-value users and then building sustainable partnerships with business stakeholders. D&A leaders, learn a new approach to prioritizing and delivering self-service here.
- Data and Analytics Org model Benchmarks: A Survey of D&A Functions:
How do D&A functions organize themselves? Based on nearly 90 conversations with D&A leaders, Gartner synthesized nine common organization models. These org models reflect the complex reality in which D&A leaders operate and offer D&A leaders a way to benchmark their own organizations.
- Three Case Studies of Data and Analytics-Driven Business Innovation:
D&A’s contribution to business value depends on its innovative application to business problems. Learn how three progressive data and analytics leaders have built innovation into the core activities of their teams to rapidly generate business value.
- Infographic: IT Score for D&A Benchmarks for Transportation:
Gartner’s IT Score for Data & Analytics benchmarks the maturity level and importance of 25 activities across seven objectives that are top of mind for data and analytics leaders worldwide.
- IT Score for D&A Benchmarks for Manufacturing
Learn how your manufacturing organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics!
- IT Score for D&A Benchmarks for Banking, Finance, and Insurance
Learn how your banking, finance, or insurance organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics!
- IT Score for D&A Benchmarks for Energy and Utilities
Learn how your energy or utility organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics!
- IT Score for D&A Benchmarks for Healthcare
Learn how your healthcare organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics!
- IT Score for D&A Benchmarks for Government
Learn how your government organization compares to your peers’ results on Gartner’s IT Score for Data & Analytics!
- Tool: Data and Analytics Strategy Template This tool can help clients in mid-size enterprises develop and organize their D&A strategy!
- Ignition Guide to Scoping a Machine Learning Project Are you eager to use machine learning in your organization but unsure how to begin? This detailed guide gives step-by-step instructions for identifying a business problem that is ready to be solved with machine learning.
- Infographic: Establish a Repeatable Process to Discover Analytics Insights Are there features that every analytics project ought to have? Yes! Use this infographic to learn how to standardized analytics projects so they reliably generate business value for your organization.
- As Demand Soars for Pandemic Analytics, D&A Needs a Business Leader Mindset Learn how three data and analytics leaders’ business mindset allowed them to responded to the COVID-19 pandemic quickly and generate value for their organizations.
- Inside View: Attributes of a Good Data & Analytics Strategy
This framework illustrates how to create a progressive data strategy by connecting it directly to business outcomes.
- Tool: Data Literacy Playbook
A step-by-step guide for expanding data literacy across an organization drawn from real-world examples
- Data Pitfalls, Part 1: That Wasn’t Supposed to Happen!
Every organization wants to be “data driven.” But few recognize that there are better and worse ways to make decisions with data. Find three pitfalls to avoid in your data-driven decision making here.
- Case Study: Data-Driven Decision Making Using the Assumption-to-Knowledge Ratio (Bose)
Data and analytics leaders need to teach business partners to make good data-driven decisions. Bose developed an assumption-to-knowledge ratio that forces teams to weigh new information against key assumptions. D&A leaders can use this ratio to help their business partners.
- Pitfalls on the Path from Data to Decision
Every organization wants to be “data-driven.” But few distinguish between good and bad ways to use data in business decisions. This publication is the first in a series dedicated to helping D&A leaders avoid data pitfalls: common mistakes business leaders make when using data.
- How to Select Attributes for a Bottom-Up Data Standard (GWC Implementation Guide)
Internal GWC documents illustrate how they selected attributes for inclusion in their bottom-up data standard. D&A Leaders can use this document to guide their data integration efforts.
- A Bottom-Up Data Standard (GWC Implementation Guide)
Internal GWC documents illustrating the final stage of their data integration standard. D&A Leaders can use this document to guide their data integration efforts.
- Case Study: Bottom-Up Data Integration Standard for Advanced Analytics (GWC)
Does fragmented, inaccessible data prevent your organization from taking full advantage of advanced analytics? Find out how GWC created a 500,000 record, water point dataset that integrated data from more than 50 countries in the global south with minimal burden on data providers.
- Implementation Guide: How to Create a Data Standard from the Bottom Up (GWC)
Use this straightforward, six-step guide to recreate GWC’s data integration standard in your own organization.
- Data Dimension Prioritization Process (Clorox)
D&A leaders often assume that more data is better. Clorox thought differently. Learn how they increased sales from targeted ads by limiting, rather than increasing, the variables included in their analysis. Clorox shows how to avoid the “more is always better” trap.
- Capability-Driven Data Use Expectations (Bunge)
Do your business partners bog down your D&A team with endless requests for reports and dashboards? Find out here how Bunge standardized its business partners’ requests to ensure its data specialists received high value requests.
- Data Analytics Capability Survey Tool (Bunge)
Click here to download the survey Bunge used to standardize business partners’ data requests.
- Continuously Market-Tested Data & Analytics Strategy (UrbanShopping*)
Creating value from enterprise data requires organizations to make a blinding array of choices. UrbanShopping’s D&A strategy led them to create a D&A sandbox that enabled the rapid market testing of D&A solutions and drove substantial ROI. (*Pseudonym)
- IT Score for Data and Analytics
Uncertain how to get started in D&A? Use Gartner’s IT Score for Data & Analytics to measure D&A maturity across seven objectives and 25 discrete functional activities!
- Ignition Guide to Strategic Planning for Data & Analytics
As a Data & Analytics leader, how do you develop a world class D&A strategy? Tap into the collective wisdom of hundreds of D&A leaders. Here is Gartner’s step-by-step guide, with tools and templates, to help you establish an actionable Data & Analytics strategy.
- Analytics Prioritization Principles (Gap Inc.)
How can Data and Analytics leaders sense, prioritize, and satisfy the critical data and analytics needs of their business users? Gap Inc. provides a model for engaging with business users to determine their data needs and priorities and develop the analytics they need.
- Data & Analytics Strategy Workbook
D&A leaders often struggle to navigate the complexities of the strategic planning process. This workbook outlines the steps involved and provides hands-on tools and templates to create a strategic plan document.
- Data & Analytics Strategy Presentation Template
This template provides D&A leaders with customizable recommended and optional slides to craft an effective D&A strategy presentation.
- Data & Analytics Sample Strategy Presentation
This sample strategy presentation is an illustrative example of how to tie D&A strategy to business strategy and improve organizational decision making through analytics investments.
- Planet Architecture: The Role of Data in Platform Strategy (A Conversation with Ian Reynolds and Jitendra Subramanyam)
Data is crucial to building the business case for new technology platforms. This episode explores how EAs and D&A leaders can maximize the business value of a platform strategy by effectively using available enterprise data.
- Deploying Effective Data and Analytics Governance: Three Companies That Got It Right
The three companies profiled here learned that D&A governance is more efficient and effective when it occurs as close to business decisions as possible.
- Three Progressive Approaches to Governing AI
Learn how three progressive organizations govern their AI projects by tailoring governance to the stages of the AI development process.
- Case Study: Ethical AI with an External Board (Axon)
Are concerns about potential ethical issues blocking you from implementing AI? Learn from Axon how to set up an external ethics board to keep your work ethically sound!
- How to Investigate the Operating Characteristics of Any Quantitative Model
Any quant model, even deterministic ones, can behave unpredictably. Learn how to systematically investigate the operating characteristics of your business-critical quant models here.
- Case Study: Data Ethics Decision-Making System (Highmark Health)
Most D&A leaders believe complying with rules ensures ethical use of D&A. It doesn’t. Ethical use of D&A demands reflection on use cases, which enables decisions on their appropriateness. Find out how Highmark Health established a system to do so here.
- Zen and the Art of Data Quality Improvement
Does low quality data prevent value creation in your organization? Reexamine your data quality improvement practices. This research profiles a Zen approach to data quality improvement adopted by several progressive D&A leaders who have created value with imperfect data.
- Data Governance Playbook
This toolkit collects client templates to aid D&A leaders in developing a data governance model. It includes templates for governance processes, stewardship roles, and information health metrics.
- Rationales for the Idealist Imperative in Business
Are you perplexed by the hype around business ethics? Click here to learn why ethics are crucial for branding and financial performance today. Find concrete recommendations D&A leaders can take to ensure their organizations’ data practices are ethical.
- Human Controls for AI Dangers (SignatureValue Bank*)
Rather than guarding against AI-based attacks, D&A leaders should collaborate with security leaders to guard against the threats internal AI applications cause. Find out how SignatureValue Bank did so here. (*Pseudonym)
- On Demand Problem Solving Teams (McDonald’s)
Are you frustrated with onerous data governance practices at your organization? Learn how McDonald’s uses rapid response “sand dune teams” to efficiently make data governance decisions.
- Exclusion-Based Data Sharing Rights (FirstHarbor*)
Do data sharing requests at your organization get bogged down because dozens of stakeholders have to approve them? Find out how FirstHarbor quickly determines which stakeholders should be excluded from reviewing data sharing requests. (*Pseudonym)
- Business-Need Driven Data Governance Objectives (FirstHarbor*)
Does your data governance enable value creation or constrain it? Find out how FirstHarbor narrows the scope of data governance, meeting business needs in data collection, use, and sharing while ensuring compliance and productivity.
- Value-Add Data Minimization (Northrop Grumman)
More data is not always better, because data comes with risk. Find out how Northrop Grumman selects data for minimization and sells business partners on value-adding alternatives.
- Ignition Guide to Building a Data and Analytics Governance Program
Don’t assume that traditional data governance will meet the demands of big data and digitization! Use this guide to establish a governance program that aligns with business priorities and divides strategic and tactical responsibilities.
- Data Quality Score (TE Connectivity)
Does your organization struggle to get buy-in for data quality improvements from your business users? Find out how TE Connectivity used an enterprise-public data quality score to hold business users accountable for data quality.
- Dangerous Data: Can’t Live Without It, Can’t Live With It
Many D&A leaders think data is good and more data is better. But some data can pose serious legal, ethical, and brand risks to organizations. Find out why here.
- “Show Don’t Tell” Data Quality Improvement (Citizens Bank)
Business users often struggle to see the relevance of data quality to their work. Citizens Bank creates business demand for increased data quality by contrasting the insights and reports that could be generated from higher quality data to the current state.
- Data & Analytics Operational, Data Quality, and Data Management Metrics
How do Data and Analytics leaders measure success? Find out here: our collection of real-world metrics spanning operations, business value, data quality, and data management maturity.
- The Chief Data Scientist Role is Key to Evolving Advanced Analytics and AI
The role of the chief data scientist is increasingly prevalent. Use this research to orient and guide the chief data scientist to strategically support, manage and scale the use and adoption of advanced analytics and AI within the organization.
- The Current State of Demand for the Chief Data Scientist Role: Q1 2021 Report
Click here to learn about talent availability, location, diversity, education and experience levels along with most-common personas and job titles for the trending chief data scientist role.
- The Current State of Demand for Data and Analytics Roles: Q4 2020 Report
Are you looking to keep your data and analytics operation at pace with the market? Use this guide to understand the market demand for D&A talent, including two role profiles.
- Building a Strong Data Science Team on a Tight Budget Using Data Scientist Adjacent Roles
Struggling to find the talent you need for data science? Learn how to use role adjacencies to identify skilled individuals already in your organization and use them to build high performing teams!
- The Modern Chief Data Officer: 3 Insights from Social Media Discussions (2018-2020)
Learn how the of the Chief Data Officer has changed over the past two years from an analysis of social media discussions on the topic.
- Leverage Role Adjacencies to Respond to the Shifting Talent Market Amid Disruption
The COVID-19 pandemic has thrown the talent market into turmoil. Learn how to leverage role adjacencies to find the skills and abilities you need.
- Tool: Data Ethics Interview Guide
If you’re committed to ethical data science, you need D&A professionals who can think critically about the ethical issues in their work. Click here to find questions your hiring managers can ask to determine if job candidates have this crucial capability.
- Toolkit: Critical Market Information for Hiring D&A Talent in the United States
This Toolkit presents data from Gartner TalentNeuron revealing the current candidate supply, demand, salary, time to fill, and hiring difficulty of key data and analytics roles in the U.S. labor market.
- Toolkit: Intelligently Sourcing Internal D&A Talent when Budgets are Tight
This toolkit provides essential information to find people in data-adjacent roles inside your organization. For each common D&A role, we provide the most frequent skills and the most adjacent roles. Use this info to look for internal data talent!
- Use Role Adjacencies to Find and Develop Data Science Talent
D&A leaders complain about the difficulty of finding high quality data science talent. How can they do better? By tapping sources the competition doesn’t know about! Click here to learn how to use role adjacencies to find data science talent others ignore.
- Case Study: Internal Data Science Team Development (Eastman)
Do you wish you could build a data science team but lack the resources to hire expensive external talent? Find out how Eastman built a business-value generating data science team beginning with talent they already had.
- Data and Analytics Talent Library
This library puts all of Gartner’s resources on sourcing, staffing, organizing, and developing high performing D&A teams in one regularly updated place.
- Capability-Based Data and Analytics Talent (Stats NZ)
D&A talent is expensive and hard to find. Learn how Stats NZ attracts the right talent by prioritizing core capabilities rather than technical skills in its recruiting. Find out how the same approach allows them to train staff that are flexible and laterally-mobile.
- Capability-Based Data and Analytics Talent Implementation Guide: Job Functions (Stats NZ)
Use this tool to see how Stats NZ organizes their D&A work into separate specializations so they can effectively recruit the right talent.
- Capability-Based Data and Analytics Talent Implementation Guide: Core Capabilities (Stats NZ)
What capabilities are necessary for a high performing D&A team? Stats NZ divides D&A talent into four core capabilities. Download their definitions and advancement scheme here.
- Capability-Based Data and Analytics Talent Implementation Guide: Job Descriptions (Stats NZ)
Job ads are your first chance to attract new talent. Yet many D&A job ads are filled with jargon and long lists of technical requirements. Stats NZ writes jargon-free, accessible job ads for their D&A positions. Download examples here.
- Capability-Based Data and Analytics Talent Implementation Guide: Development Planning (Stats NZ)
In a tight D&A talent market, it’s crucial to develop D&A talent internally. Stats NZ uses its for core capabilities to sequence internal D&A talent development. Download their guide to D&A talent development here.
- Metrics for Fair and Transparent Performance Narratives (Cafcass)
Are your employees frustrated by their performance evaluations? Do they complain about the metrics your organization tracks? Find out how Cafcass created a performance management dashboard that increased employee trust in performance metrics and made evaluations fair.
- Cafcass’s Implementation Guide to Metrics for Fair and Transparent Performance Narratives
Learn how Cafcass teaches its employees about the metrics it tracks and ensures that their performance metrics are comparable across employees with different workloads here.
- Workforcewide Analytics Capability Development (Intel)
Click here to learn how Intel’s Financial Shared Services Center used internal data science talent to increase data literacy across the organization through peer-led education and a community of practice.
- Data and Analytics Job Descriptions Library
From cutting-edge, emerging roles to those that are now standard in D&A teams, here’s where to find job descriptions sourced from peer organizations. Updated quarterly with contributions from Gartner’s entire D&A research community!
- Redefining Analysts as Decision Experts (Philips)
Find out how Philips grew revenues by more than 18 million by aligning its analysts to support specific decision areas rather than a myriad of stakeholder requests.
- Creating Business Value with Multidisciplinary Data and Analytics COEs (Omicron)
Does your organization use a Data and Analytics Center of Excellence (COE)? Are you thinking of setting one up? Learn how Omicron avoided silos and enabled cross-functional collaboration in their COE for Finance D&A.
- D&A Organizational Models, Roles, and Responsibilities
How do Data and Analytics leaders organize their function? What roles should a data and analytics office have? This deck collects practitioner examples of organizational, staffing, and stewardship models and analytics roles.
- Analytics Champions Recruitment Guide
Learn how companies identify internal evangelists for analytics and use them to increase analytics demand across the enterprise.