Thought leadership

March 10, 2026

The House of Data Series: Enablement

13 min read

This paper focuses on how data programs get adopted — training, knowledge management, coaching, and the change management work required to turn built capabilities into used ones. It does not cover the technical design of those capabilities or governance policy — those are addressed in the Data Architecture, DataOps, and Compliance whitepapers.

Jim Barker

Get Data Insights Delivered

Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.

Join The AI Trust Summit on April 16

A one-day virtual summit on the controls enterprise leaders need to scale AI where it counts.

Get the Best of Data Leadership

Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.

Get the Best of Data Leadership

Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.

Stay Informed

Sign up for the Data Leaders Digest and get the latest trends, insights, and strategies in data management delivered straight to your inbox.

Get Data Insights Delivered

Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.

House of Data Series

Every strong data program is built like a house. Data Architecture forms the foundation — the platforms, pipelines, and operating model that everything else depends on. Seven domain pillars rise from that foundation, each one essential to a complete data program: Data Quality, Privacy, Data Security, DataOps, Compliance, Data Enablement, and Data Consumption. Data Literacy runs across all seven as a connecting beam, ensuring people at every level can read, interpret, and act on data. At the top, People & Leadership sets the direction, accountability, and culture that holds the whole structure together.

This series of whitepapers covers each component of the House of Data in depth. Each paper was written by a practitioner with direct experience in that domain. Together, they form a practical guide to building data programs that earn — and keep — trust.

This paper covers Data Enablement — the function that ensures people can actually use the data capabilities the organization has built. Without structured enablement, even the best data products fail to achieve adoption.

Data enablement

Data enablement is a term that is often misused. Some corporations who have struggled with data governance and looked for a new term have used data enablement as a title for their data governance program. Other debates have broken out and resumed the data governance/data enablement debate outright. This paper takes a different view: data enablement is the helpful part of the data governance program and is used to propel AI, analytics, and digital modernization efforts forward.

In this paper we will:

Define data enablement
Align coaching, mentoring, and knowledge management to data enablement
Cover top-of-mind data enablement capabilities
Introduce telemetry to continue data enablement for continuous improvement
Describe the role of data enablement in data trust

A quick scan of data enablement in a search engine — or what AI provides — returns a varied set of definitions that are self-serving, or aligned to a re-organization of data enablement to be the whole term for a hybrid view of data governance and data management. This is not correct.

Data enablement defined

Data enablement should be defined as: the practice of empowering people to use data confidently and effectively by providing accessible data, intuitive tools, and the knowledge needed to make data-driven decisions.

Data enablement should be the cross-functional set of actions that always introduce, support, and promote data capabilities. It should at a minimum provide an introduction of new data capabilities, which includes roll-out materials, job aids, support documents for solutions, and include telemetry and follow-up to promote the new and existing capabilities.

Leverage the idea from Davenport of "Learn Before," "Learn While," and "Learn After Doing." Use this enablement of capability to expand business value and increase return on investment (ROI) of these new data capabilities. Further, by putting in systems that support the use of data, capabilities will grow. To be sure, continue to serve the data audience to find out what is needed to increase users' satisfaction and make a difference.

This area of enablement is one area in the rollout of new data capabilities. As we invest in new capabilities, this is the set of activities as we begin to use new data capabilities.

To be successful at enabling, knowledge work needs to be done across the development lifecycle. Business purpose, business and technical metadata needs to be compiled, job aids and training docs need to be built, and the consumers of this capability need to be thought of front, center, and always.

In the overall set of activities, enabling staff is the most important activity for success. Think of this idea when considering Data Enablement 101 below.

This diagram shows how the enablement outcomes will be achieved through a set of actions from ideation to business value. To be successful, the DataOps team will execute on a run basis and address any issues proactively, and address any and all issues that occur. The governance team will continue to support the business through building job aids, training materials, business support offerings, and encouraging data coaching. Data coaching will assist the growth of user adoption of these new solutions to drive business benefits.

Enablement tools and assets

The key to enablement is the assets and training materials. The following tasks list off some key tools to consider using. They are broken down in three categories: (1) Training Tools; (2) Knowledge Management Tools; (3) Six Sigma Tools.

As you enable your staff, consider asking these questions to understand the mindset of the staff you are about to enable on the new capabilities:

What are the available skills and training needs of your organization?
What resources are available, most notably personnel, time, and budget?
What is the organization's readiness for this change to support it?
What were the data collection and analysis capabilities before this new offering?

Once those questions are answered, take some time to consider your current state. Ask these questions to determine how to move forward:

What type of waste is most highly possible?
Are quality defects the primary concern?
How complex were your processes before and after this new capability?
What data is currently available vs. what is being added?

The answers to these questions will help to determine what tools are needed and build your enablement plan. The following is a list of commonly used tools that firms use to achieve successful enablement and successful use of data capabilities. No firm uses all of these. Use this as a guide to possible tools you could build out. The tools to use should be heavily dependent on the answers to the questions above.

No data enablement program uses every tool in this list. Use it as a menu, not a checklist. The right selection depends on where your current gaps are — whether the problem is knowledge transfer, process documentation, or continuous improvement of how data work gets done.

Training tools	Knowledge management tools	Six Sigma tools
Training decks Solution documents How-to documents Analysis examples Technical data examples Training recordings	Cookbooks Playbooks Cheat sheets Content lists/examples Domain models Glossary of terms Data dictionary Policies & standards Data quality rules Lineage charts Telemetry reports Data access process defined	Process models Control charts SIPOC diagrams Cause & effect diagrams DMAIC 5 S's Seven wastes Value stream mapping Voice of the customer 5 Why's FMEA (Failure Modes and Effects Analysis) Two-sample T-tests Mood's median analysis Kaizen

Data coaching

The idea of data coaching is to develop a stable of "official" coaches that are publicized to be helping folks move forward in particular areas of expertise. People that can help and be viewed as not being judged, there to help. Additionally, encouraging coaching or "helpers" inside business teams can be very helpful. That friend-of-a-friend concept makes a big difference.

When providing enablement, having some "official" data coaches identified helps. Additionally, the evolution of coaches or helpers happens normally. Some kudos to people helping is a nice touch that makes a big difference, encouraging supporting your data peers as a positive influence on your organization.

Six Sigma tools for data programs

In the list of possible assets there is a column for Six Sigma tools. Many people in the data profession have limited experience or skills in the Six Sigma space, a space normally thought of as continuous improvement for manufacturing organizations, so many of these tools can generate improvement for data professionals. This notion is based on DMAIC. DMAIC stands for Define, Measure, Analyze, Improve, and Control. Just reading each of those pieces, data professionals immediately offer up a "wait a minute, that is what I do all the time." Due to this we incorporate many aspects of Six Sigma in this series of white papers. Here is a table of what each of those tools mean:

Six Sigma is most associated with manufacturing, but the underlying toolkit maps directly to how data teams define problems, measure performance, and drive improvement. This table covers each tool, what it does, and where to find reference material.

Name	Description	Reference material
Process models	A visual representation of a business process, often a workflow, showing the sequence of activities required to achieve the desired outcome.	Process Models — 6SigmaUS
Control charts	Graphs used in statistical process control (SPC) to monitor and manage processes over time, distinguishing normal variation from signals worth investigating.	Beginner's Guide to Control Charts — Deming Institute
SIPOC diagrams	Illustrates a process across five dimensions: Supplier, Inputs, Process, Outputs, and Consumer. Useful for scoping and communicating how a process works at a comprehensible level of detail.	Get Up to Speed on SIPOC — iSixSigma
Cause & effect diagrams	A visual tool (often called a fishbone diagram) used to identify and organize the potential causes of a problem, grouping them by category.	Cause & Effect Diagrams — Juran
DMAIC	A five-phase, data-driven methodology for process improvement: Define, Measure, Analyze, Improve, and Control. The core framework of Six Sigma.	DMAIC — ASQ
5 S's	An organizational methodology for improving efficiency: Sort, Set in Order, Shine, Standardize, and Sustain. Applies to digital workspaces and data environments as readily as physical ones.	5 S's — Lean Construction Institute
Seven wastes	A Lean framework for identifying non-value-add activities: Transportation, Inventory, Motion, Wait, Overproduction, Overprocessing, and Defects.	Seven Wastes — Lean Enterprise Institute
Value stream mapping	A technique for visualizing and analyzing every step required to deliver a product or service, surfacing delays and handoff inefficiencies.	Value Stream Mapping — ASQ
Voice of the customer	A structured process for capturing the needs, expectations, and experiences of the people you're building for — to ensure decisions are grounded in actual use, not assumptions.	VOC — International Six Sigma Institute
5 Why's	A root cause technique: ask "why?" five times in sequence, each time building on the previous answer, to get past surface symptoms to the underlying cause of a problem.	Five Whys and Five Hows — ASQ
FMEA	Failure Modes and Effects Analysis — a systematic approach to identifying potential failures in a process, product, or system before they occur, with the goal of prevention.	FMEA — ASQ
Two-sample T-tests	A statistical test for comparing the mean of a sample to a known or anticipated population mean — useful for validating whether a process change produced a meaningful difference.	T-Tests — JMP Statistical Discovery
Mood's median analysis	A non-parametric test for comparing the medians of two or more population groups, useful when data doesn't meet the assumptions required for a standard T-test.	Mood's Median — Lean Six Sigma Corp
Kaizen	A business philosophy centered on continuous, incremental improvement across the organization — small changes made consistently over time rather than periodic large overhauls.	Kaizen Institute

Enabling: a guide

This section is meant to be a helpful guide to executing enablement. It was written based on feedback on a number of projects, and hopefully helps you as you establish your development standard for enablement. It is provided with the numbered lines being the enablement steps, and the check boxes being pre-requisite tasks for successful completion of a project.

Note: we use the term "data capability" but it could be a variety of other terms such as data product, dashboard, executive information system, AI analytical solution, etc.

Steps to enable on a new data capability

Build out pre-requisites:

List of original ideas by person/team/role
Define target audience
Determine use case
Establish desired outcomes
Listing of potential data objects
Detailed list of leveraged data objects
Populated metadata for each physical table, column, report, and dashboard
Developed data pipelines for each data asset (file or table)
Available lineage for each delivered object
Documented test cases with results
List of developed analytic output for alerts, AI analytics, dashboards, digital assets, and reports
Developed analytic output for alerts, AI analytics, dashboards, digital assets, and reports certified
Run book for daily/weekly/monthly processes
How-to for each outcome asset: developed analytic output for alerts, AI analytics, dashboards, digital assets, and reports
How-to for opening up outcome assets
Additional how-to's the user community will require (this list should grow over time based on lessons learned)
Write-up labs for training/rollout

Enablement steps:

Determine format and timing of onboard training
From the target audience list and requested user list
Identify the right window for training: weeks/days/times
Schedule the onboard training
Host the onboard training
Share the labs
Share the wide range of training and job aids to new users
Promote the new capability with core users and the broader community
Follow up on lab completion
Host follow-up training as necessary
Check on use of new capabilities
Follow up to build user adoption
Promote the capability further
Survey the user base or collect similar information with other methods
Report out adoption numbers, and work for help to increase adoption rate

Remember the steps following enablement, and take action to maximize use of the new capability: (1) Use solutions; (2) Monitor usage; (3) Package usage and feedback sentiment; (4) Detail new improvement; (5) Develop and implement improvements; (6) Promote improvements.

Coaching

To be successful, the governance and enabling team should recruit, train, and support a set of official data coaches to support all new users. It should also include providing a mechanism that simplifies the finding of coaches that can provide some help.

AI trust in enablement

Most AI capabilities have people involved, either "human involved" or "human managed," so they require enablement on how to use the AI capability, how to share concerns, and generally all questions regarding the use of data in AI.

During the rollout of new analytical capabilities, AI capabilities, or business processes, time should be spent to educate users on:

The process
Appropriate use of data for AI
Sharing of data from and into AI
Identifying data quality needs
Vision of data you can use and share, or need to be concerned about

It is only with training on data sensitivity and AI that we can truly achieve AI Trust.

Role of Bigeye for data enablement

Bigeye's technology is important in building, operating, and rolling out new data capabilities. There are three or four areas that are worth considering when enabling staff.

First, use lineage charts to show what data flows to what solutions. This can be a "show quick and move on" approach, but showing data flows will help some, while it may confuse others, so be careful with it.

Second, showing the browser extension to the audience to see where they can see data quality, and that they can trust the data, is very helpful.

Third, showing data quality results to the user in other places, such as inside a catalog or in the Bigeye UI, can be helpful. The data that you make available in data quality dashboards or reports can also be very valuable.

Which of these to use is up to you. Remember the goal of enablement is to get people started with confidence, so find the right balance between educating on provenance of data and trust in data quality, and making it easy for the end user to use the data right away. That is a critical decision.

Summary

Data enablement is the set of activities to roll out new data capabilities and data products for business success. It includes building the assets necessary for successful knowledge transfer and help tools. It should also include establishing data coaches, and focusing on continuous improvement.

Explore the Series

Every great data program is built from the ground up.

The House of Data breaks down the ten pillars of a mature, trustworthy data organization. Click any section to explore that paper.

References

Adjeddine, K., & Lundqvist, M. (2016). Policy in the data age: Data enablement for the common good. McKinsey & Company.

Horvitz, E., & Mitchell, T. (2020). From data to knowledge to action: A global enabler for the 21st century. arXiv. https://arxiv.org/abs/2008.00045

IBM. (n.d.). What is data enablement? https://www.ibm.com/think/insights/data-enablement

Inside Automation. (n.d.). Data enablement: Optimize your business. https://insideautomation.net/data-enablement-optimize-your-business-aw-aug/

DataQG. (n.d.). The 5 pillars of data enablement. https://dataqg.com/articles/the-5-pillars-of-data-enablement/

GRC World Forums. (n.d.). Beyond protection: The rise of data enablement.

TDAN (The Data Administration Newsletter). (n.d.). Making the case for data enablement rather than data governance.

Thomson Reuters & Pitchly. (2023). The next big thing: Data enablement. https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2023/01/The-Next-Big-Thing_Data-Enablement-Pitchly.pdf

share with a colleague

Resource

Monthly cost ($)

Number of resources

Time (months)

Total cost ($)

Software/Data engineer

$15,000

$540,000

Data analyst

$12,000

$144,000

Business analyst

$10,000

$30,000

Data/product manager

$20,000

$240,000

Total cost

$954,000

Role

Goals

Common needs

Data engineers

Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.

Freshness + volume
Monitoring
Schema change detection
Lineage monitoring

Data scientists

Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.

Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing

Analytics engineers

Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.

Lineage monitoringETL blue/green testing

Business intelligence analysts

The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.

Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing

Other stakeholders

Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.

Integration with analytics toolsReporting and insights

about the author

Jim Barker

Director of Professional Services

Jim Barker is a lifelong data practitioner, industry thought leader, and passionate advocate for treating data as a strategic asset. With more than four decades of experience spanning data quality, governance, warehousing, migration, and architecture, Jim brings a rare blend of hands-on expertise and executive perspective to the evolving data landscape.

Jim’s journey in data began at just 14 years old. Since then, he has held leadership roles across organizations including Honeywell, Informatica, Thomson Reuters, Winshuttle (Precisely), Alation, nCloud Integrators, and Wavicle, contributing to advancements in data governance, migration methodologies, and enterprise data strategies. His work has included building global data quality programs, developing scalable governance frameworks, and driving innovation recognized across the industry.

His research and writing focus on lean data management, governance strategies, and the intersection of AI, data quality, and enterprise value creation.

Now at Bigeye as Director of Professional Services, Jim is energized by the company’s vision for data observability and its role in shaping the future of trusted data. He continues to share his perspectives through writing and speaking, aiming to elevate the conversation around data, cut through industry noise, and help organizations do data the right way.

Outside of work, Jim enjoys coaching and spending time with his family, often on the basketball court or soccer field, where many of the same lessons about teamwork, discipline, and leadership apply.

As Jim puts it: “Data matters.”

‍

about the author

His research and writing focus on lean data management, governance strategies, and the intersection of AI, data quality, and enterprise value creation.

Outside of work, Jim enjoys coaching and spending time with his family, often on the basketball court or soccer field, where many of the same lessons about teamwork, discipline, and leadership apply.

As Jim puts it: “Data matters.”

‍