Here is a list of our publications to date. Watch this space for updates every month as we release more publications.
Our research terrain covers:
- Business Value of Data & Analytics
- Data & Analytics Strategy and Planning
- Data & Analytics Quality and Ethics
- Data & Analytics Talent
- 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
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.
- 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.
- 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.
- Rationales for the Idealist Imperative in Business NEW!!
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.
- 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.
- 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.
- Metrics for Fair and Transparent Performance Narratives (Cafcass) NEW!!
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 NEW!!
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.
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