Data Governance

Why Data Governance is Important for Organizations (With Examples)

Knowledge Shared By Gensquared Inc on March 2, 2022

CDOs and other data leaders often struggle to communicate the tangible business value of data governance to stakeholders. Viewed as an overly academic exercise with little practical, pragmatic value, it can be difficult to show why data governance is so important to an organization.

Another challenge is that while data governance focuses on creating the processes, policies, and roles that dictate how an organization uses data, it needs to be coupled with other capabilities to actually realize business value. Capabilities include metadata management (to organize and understand data) and data quality management (to improve data quality and the business decisions that come out of it).

It’s not easy. In fact, Gartner predicts that in 2022, “only 20% of organizations investing in information governance will succeed in scaling governance for digital business”.

But organizations that are willing to do the hard work (and implement an agile data governance framework for faster results) will be rewarded. Forrester says that data-driven businesses grow by 30% each year.

But how?

What is the benefit of data governance to organizations?

A successful data governance strategy ensures that data is consistent, trustworthy, and doesn’t get misused. It also ensures that business users have access to the right information at the right time — and understand how to interpret it.

While data governance used to be focused mostly on mitigating risk in heavily legislated industries like healthcare and finance, it has in recent years been adopted by organizations across industries as a key business driver. Organizations have discovered that data governance can increase revenue and market share by identifying new business opportunities. It can also decrease or manage costs by uncovering operational inefficiencies.

Sounds great — but where’s the proof?

Three examples of data governance that provided important business value to organizations

For CDOs and other data leaders seeking to convince leadership that data governance provides value, here are three examples of tangible business value driven by data governance programs.

Example 1: How an insurance company reduced risk by prioritizing data quality

CMO Magazine published a case study by Ram Kumar, Chief Data and Analytics Officer at CIGNA, about one of the fastest growing and more successful general insurance companies in India. The case study outlines how the organization significantly reduced risk with a company-wide data quality program.

Data quality is critically important in the insurance business. Kumar’s article explains how the anonymous organization not only mitigated risk across the data lifecycle, but also ensured that agents captured accurate data to help serve the customer better.

The organization used unique and creative tactics to ensure the program succeeded, including branding it with a parrot mascot named “DeeKew”, giving out data quality awards to individual employees and the best-performing branches, and offering discounts to customers and brokers who entered quality data.

Example 2: Building a data governance program that pays for itself

At Enterprise Data Governance Online 2022, Curtis Mischler (Vice President and Chief Data Officer) and Toby Hall (Senior Vice President) of Delta Dental of Michigan delivered the keynote “Building a Data Governance Program”. They shared how they successfully launched and maintained data governance at Dental Dental of Michigan.

The presentation’s key takeaway was that their data governance program was intentionally designed to deliver business value. In fact, if any aspect of data governance didn’t have a direct effect on one of their key business drivers – including revenue growth, cost reduction, security and compliance, business decision-making, and risk reduction – they chose not to pursue it.

They also ensured that the program would “pay for itself” by adding value to the organization and addressing real business issues. Data governance isn’t just an academic exercise at Dental Dental of Michigan – it’s a living, breathing program that generates real business value.

Curtis Mischler shared how his team tracked their success metrics:

We have a massive spreadsheet… as we accomplish different things to the program, you put it up on the list. And then at some point, we’ll go in there and say, do we think it’s worth trying to quantify this into some kind of savings metric? It could be time saved, it could be real dollars, that we reduced expenditures, it could be cost avoidance, opportunity cost… ”
And it’s kind of impressive. Sometimes you pull the whole spreadsheet out and people can see the whole list by itself, even if we haven’t quantified the item on the list, just to get an appreciation of all the different things that we’ve done. And we’ve been surprised ourselves, sometimes [by] how many different things we’ve accomplished

He also shared several creative examples of benefits achieved through the data governance program, including optimized sales reports:

We have over 1000 reports that go out from our sales team every year to our customers. Sales [was] always asking questions about what’s okay to share.
We came up with what we called the “sales report catalog”, which is a tool that breaks down, based on the type of customer and based on the report what information can be sent, what needs to be held back, and what agreements such as NDAs or business associate agreements need to be in place.
It made it a lot easier for the sales team to make their decisions instead of all the back and forth. The savings for that, just in time alone, was much bigger than we thought it would be. To this day, it’s still one of the wins that sales team talks about.

Example 3: Creating relevant and meaningful consumer experiences with data governance

Cross-selling — which includes data-driven recommendation engines and hyper-targeted marketing campaigns — is another powerful value add that can only be made possible through data governance.

Any organization that wants to successfully build the trusted data sets required for effective cross-selling will need a strong data governance program.

Das Dasgupta, Chief Data & Analytics Officer of Saatchi & Saatchi, talked about how some of the most successful entertainment and retail companies in the world use recommendation engines for CDO Magazine:

There was a time when you would go to a site, and you would be served with a bunch of movies that you don’t really care about…
Netflix creates a recommendation engine based on a methodology called a “collaborative filtering process”, which takes your likes and dislikes and builds a machine learning model to know what would be the best experience for you.
Similarly, if you go to Amazon, you see products that will be more relevant to you every time you go back to the site and make a purchase… When we do this right, we create better, relevant, meaningful experiences for the consumers at large.

Robert Seiner, President and Principal, KIK Consulting and Educational Services and Publisher of TDAN.com, made the connection between data governance, cross-selling, and corporate financial statements at Enterprise Data Governance Online 2022:

I have a client now [whose] senior leadership didn’t just ask that we linked data governance to their bottom line to their corporate financial statements — they demanded it.
… So many organizations are made up of other organizations that have different products and different services, and they don’t know who buys what from whom. Amazon does a great job of this. That ability to cross-sell is what’s going to positively impact the financial statement.

How can you optimize opportunities to provide business value with data governance?

Data leaders who want to see results similar to the examples above should choose the focus of their data governance initiatives wisely.

Much like Delta Dental of Michigan, defining key business drivers – the “why” of data governance – is a valuable first step. This way, individual projects within the initiative will be achievable, scalable, and capable of delivering results that the business cares about.

Another pragmatic starting point for data governance is deceptively simple – look at the reports that the organization uses regularly to run the business. The data in these reports contain the most important metrics to the organization, so the most valuable and impactful data governance activities will concern the quality and management of those data sources.

Data leaders who start with small use cases that deliver quick wins are more likely to see their data governance initiatives succeed. Once they establish that data governance can deliver tangible business value, they can expand the scope across the rest of the organization.

How to Implement an Agile Data Governance Framework

Knowledge Shared By Gensquared Inc on February 21, 2022

Data is arguably your organization’s most valuable asset. But as your organization evolves in its use of data, common governance challenges tend to emerge.

You may be bogged down by manual processes and frustrated with a lack of data quality controls. You’re also probably struggling with limited data usability because there is no single source of truth.

Data governance can help address these challenges and work towards making data usable, accessible, and protected. Easier access to trusted, high quality data leads to improved, data-driven decision making and potentially increased profit and decreased costs.

So why do organizations struggle to build and maintain data governance?

Most organizations are still applying outdated thinking to modern data problems. This is still true, even after the pandemic forced organizations to rethink their data strategies and adopt more agile business practices

As a result, the data governance initiatives that many organizations rely on or try to implement cannot keep pace with the fast-changing data landscape. The result is overly complex, rigid, and outdated processes with little to no benefit to the organization.

In the last few years, a new approach has emerged that is both practical and achievable, aligns with the needs of today’s modern enterprise, and is flexible enough to accommodate the data needs of tomorrow.

Can data governance be agile?

There is an emerging interest in applying agile thinking to existing data governance approaches.

At the recent Enterprise Data Governance Online conference hosted by Dataversity, conference organizers identified “sustainability” as one of the top five data governance challenges facing data practitioners.

Panelist Peter Aiken (PhD Professor of Information Systems, VCU and Founder, Anything Awesome) addressed this challenge:

Keeping [data governance] relevant is really the most important thing and also avoiding comprehensive approaches. I know that sounds crazy. But think about where our planning was three years ago, and how well did any of that come to fruition? I’m very much a believer in adaptability, which some would say is agile, as opposed to trying to slavishly follow a plan from start to finish.

Adaptability is key. In its broadest sense, when we talk about agile thinking, we are talking about a mindset that involves understanding, collaborating, learning, and being flexible enough to adopt to the shifting demands of the business.

Traditional approaches to data governance are often lengthy and overly academic exercises that take considerable time to generate business value—in other words, there are the opposite of sustainable.

Data governance participants are often fatigued before value is realized, as Peter Aiken explains:

I am doing a lot of “restarting” of data governance. [Programs need to be restarted] because people end up being in meetings and coming to the realization that they’re arguing about how many angels can dance on the head of a pin. And it’s not adding business value. It’s demoralizing to the folks that are involved, and it’s not productive for the organization.

Agile thinking is iterative, consisting of phases that build on top of the previous. It focuses on delivering scalable and repeatable business use cases that deliver value quickly. And most importantly, it enables everyone who creates and uses data across the business to participate in the governance process.

Democratizing data across the organization is critical in unlocking data siloes, increasing data usability, expediting decision making, and driving business growth.

What is an agile data governance framework?

Frameworks are useful because they help us organize how we think and communicate about complex concepts.

An agile data governance framework consists of five foundational phases. Each phase of the framework is designed to build off the previous, increasing the chances that you get data governance right from the outset.

Phase 1: Establish data governance outcomes

Start your agile data governance by documenting your objectives and strategies. What are your monthly, quarterly, and annual outcomes? How do they align with the organizational goals?

Activities to support this phase include:

Identify business success criteria
Build your roadmap
Review and reprioritize

Phase 2: Define data governance owners and stakeholders

Next, define your owners and stakeholders. Who makes up the governing bodies? Who are the stewards and stakeholders? This phase is all about people.

Activities to support this phase include:

Establish governing bodies
Define communities
Identify data stewards, owners, and analysts

Phase 3: Define data governance organizational assets

Once you’ve defined governing bodies, stewards, and contributors, now you need to define the data assets that are to be governed, measured, and monitored.

Activities to support this include:

Review and rank company data assets
Discuss and align on quality standards

Phase 4: Design your data governance processes

Phase four is where you design the data management process flow. In this phase, you’ll establish the data management processes that will be incorporated into the operating mode of each iteration.

Activities to support this include:

Define core processes
Establish performance management dashboards

Phase 5: Enable data governance with technology

In the final phase, you’ll use technology for automated data integration, data quality, MDM, and cataloguing.

During this phase, you’ll:

Establish technical patterns & workflows
Catalogue assets
Enable stewards

Agile data governance in the real world

In their keynote address “Building a Data Governance Program” at Enterprise Data Governance Online, Delta Dental’s Toby Hall and Curtis Mischler stressed the importance of rolling out data governance iteratively:

We very consciously rolled things out over time to increase our footprint of folks that were involved in the program. The idea here is we wanted to keep building on what we’ve done before, and we wanted to make it sustainable. We tried to stand up all this stuff on day one and realized it wasn’t going to work. So, we spread it out over time.

With agile data governance practices, you can create a sustainable, trusted, and adopted data ecosystem where high quality assets are quick to discover, safe to use, and easy to understand.

If you need help applying an agile framework to your data governance program, Gensquared is here. We are a people-focused data and analytics consulting firm that is simplifying how businesses access and use their data. We enable leaders like you to make data-driven decisions with ease and confidence.

Get in touch today.

The Top Five Data Governance Challenges

Knowledge Shared By Gensquared Inc on February 14, 2022

Anyone who works in data governance will tell you that it’s full of challenges. CDOs, CIOS, and other data professionals struggle to communicate the true business value of data governance and in turn fail to secure buy-in from stakeholders.

Enterprise Data Governance Online 2022 is out to change all that. An annual virtual event, this years’ conference featured six live presentations from some of the world’s foremost experts on data governance. They covered everything from improving data quality and implementing a data catalog and business glossary to the five essential elements of a data governance framework.

In the day’s final session, The Top Five Data Governance Questions, industry vets Peter Aiken, Karen Lopez, and Robert S. Seiner came together to discuss the five most common data governance challenges facing data professionals today.

Their insights (and a few key points from the keynote Building a Data Governance Program with Delta Dental’s Toby Hall and Curtis Mischler) were too good not to share.

We’ve gathered the panelists’ most provocative, practical, and downright inspirational quotes below.

Data governance challenge #1: Business and executive support

Securing executive support for data governance is top of mind for most data professionals, and with good reason – data governance initiatives that don’t have the full support of the C-suite are far more likely to fail (or fade over time). The panelists shared their perspectives on this key question — How do you get management to see and promote data governance as a high priority?

Peter Aiken (Ph.D. Professor of Information Systems, VCU and Founder, Anything Awesome) suggests leading with a different definition:

[It helps] if you get them to understand a proactive definition of data governance. The one I like to use is “managing data with guidance”. If you make it that simple, one of the things that management sees is that “Oh, so if I’m not doing data governance, I’m managing data without guidance and that’s bad.” And if I can get that part of [data governance] explained to them… we’re going to have lots of conversations about it.

Robert S. Seiner (President and Principal, KIK Consulting and Educational Services and Publisher of TDAN.com) recommends leading with a new message:

The people who need to be convinced at the highest level of the organization, they’re tired of hearing us say over and over again that we need a governance program.
What we need to do is take that message from the rest of the people in the organization… And if we can get people in the organization to articulate what they can’t do because they don’t have the data or don’t have access to the data. And what would they be able to do [if we had the data]?
Let’s carry that message to the highest levels of the organization.

Data governance challenge #2: Value and benefits

Most data professionals struggle with demonstrating the day-to-day value of data governance.

Curtis Mischler (Vice President and Chief Data Officer, Delta Dental of Michigan) shared how his team tracked their success metrics:

We have a massive spreadsheet… as we accomplish different things to the program, you put it up on the list. And then at some point, we’ll go in there and say, do we think it’s worth trying to quantify this into some kind of savings metric? It could be time saved, it could be real dollars, that we reduced expenditures, it could be cost avoidance, opportunity cost, whatever it might be…
And it’s kind of impressive. Sometimes you pull the whole spreadsheet out and people can see the whole list by itself, even if we haven’t quantified the item on the list, just to get an appreciation of all the different things that we’ve done. And we’ve been surprised ourselves, sometimes [by] how many different things we’ve accomplished.

Data governance challenge #3: Engagement and ownership

Another essential component of data governance success is bringing lines of business into the conversation. For data practitioners who have experienced challenges getting traction with executives, changing vocabulary may be part of the solution.

Peter Aiken advises dropping “data owner” for good:

I don’t let anybody that I work with use the words “ownership” and “data” at all… Where I always point them is to answer the question “Who owns accounting data?” It’s coming from all parts of the organization. So certainly, there are responsible stewards…. And there are a number of different roles that stewards can play but if you give somebody the actual ownership of the data, it causes nothing but problems in the organization.

Karen Lopez (Sr. Project Manager/Architect, InfoAdvisors) shared a new term:

A term that’s being used a lot in data science and by some of the new people who come into the data management world [from other disciplines] … is data curation – like a museum curator. I like that one because it’s about care and it’s about protection. It’s about archiving, it’s about archaeology and forensics… like all things, we struggle for the right label.

Toby Hall and Curtis Mischler of Delta Dental advocate for using humour to keep executives engaged in data governance:

We wanted to have fun, because we knew data governance wasn’t always going to be the most exciting topic at face value. We brought in some different things like making [80s pop star] Richard Marx our unofficial mascot.
We’ll put easter eggs in minutes… We had one that was ‘Which senior executive has a subscription to Tiger Beat magazine?’. We have another one which is that Toby has agreed to pay the cost for anyone who gets a tattoo of certain data governance artifacts that we’ve created.

Data governance challenge #4: Sustainability

So you’ve launched data governance at your organization — how do you keep it fresh and relevant? With its focus on policies, processes, and roles, data governance can be painfully bureaucratic. Can data governance ever be modern and agile?

Peter Aiken recommends an flexible approach:

I am doing a lot of “restarting” of data governance. [Programs need to be restarted] because people end up being in meetings and coming to the realization that they’re arguing about how many angels can dance on the head of a pin. And it’s not adding business value. It’s demoralizing to the folks that are involved, and it’s not productive for the organization.
Keeping it relevant is really the most important thing and also avoiding comprehensive approaches. I know that sounds crazy. But think about where our planning was three years ago, and how well did any of that come to fruition? I’m very much a believer in adaptability, which some would say is agile, as opposed to trying to slavishly follow a plan from start to finish.

Karen Lopez believes we should rethink how we refer to governance:

This is my speaking point that a lot of people don’t agree with, but strategically, I want data governance as a label to just fade away.
When we think about traditional engineering, like building a building, they don’t talk to their customers and their managers about “engineering governance”, because the definition of professional engineering includes governance.
They have processes [that are] embedded into law [and] into their normal practices. Engineers themselves are legally required to follow those processes… [They have to] make sure what [they’re] designing is legal, ethical, cost-effective… [and they have to] get approvals and acceptance on those designs, then people are going to go build it and the engineer herself has to verify that what was built actually matches the requirements or has waivers in case it doesn’t. And then once it’s built, there are engineering services that go through and check that it’s still working.
All of those things are actually governance, but we don’t call it engineering governance. So, I want data governance to just be part of managing data.

Curtis Mischler explained how Delta Dental came to choose an iterative, sustainable data governance rollout:

We very consciously rolled things out over time to increase our footprint of folks that were involved in the program. The idea here is we wanted to keep building on what we’ve done before, and we wanted to make it sustainable. We tried to stand up all this stuff on day one and realized it wasn’t going to work. So, we spread it out over time.

Data governance challenge #5: Roles and structure

And finally, the age-old question at the heart of data governance — should your initiative be led by IT or the business?

Robert Seiner shared a unique perspective:

When I’m asked the question of where data governance should reside, if it should reside in the business or if it should reside in IT, I answer “Yes”. It needs to reside somewhere. I’ve seen organizations (although this might not be the preferred approach), where the function of data governance resides in IT… Typically, it’s in a business area, oftentimes in a risk area.
But the fact is that it’s not going to happen by itself. It needs somebody to guide it. If it’s in IT and viewed as only being an initiative for IT’s sake, it’s going to fail. If it’s in IT, and the business people are truly the stewards and they’re engaged as stewards, you’ll have a much higher likelihood that you’ll be successful.

Want more advice from business and technology leaders who are solving data challenges?

Data governance is no easy task. It takes genuine commitment, constant vigilance, and an understanding of business priorities. Leaders who successfully employ these strategies, though, can look forward to a number of benefits: a defined set of data standards and processes, higher quality data, and robust data-driven decision-making.

In the Data-Driven Leaders podcast, Gensquared CMO Christina O’Reilly goes behind the scenes with business and IT leaders for insightful conversations on their data journey, thoughts about the future of the data and analytics industry, and more.

Listen to the latest episode on the Gensquared site, or wherever you get your podcasts.

Why Data Governance Matters and How to Get Started

Knowledge Shared By Gensquared Inc on January 12, 2022

What is data governance and why does it matter?

It seems like everyone has a different definition.

Gartner defines data governance as “the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.”

The Data Governance Institute (DGI) has a similar definition:

“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

We wanted to dig deeper and really understand the meaning behind the words.

We sat down with Jesse Glick, Senior Director, Analytics and Data Governance at Gensquared for his expert perspective. A former Business Intelligence and Data Governance Manager at Ubisoft, Jesse has an extensive background in computer science, business intelligence, and data analysis.

There are many definitions of data governance. In your own words, how would you define data governance?

I hate to use a definition within a definition, but it’s really about governing your data. It’s making sure your data has checks and balances, from a security, process, and quality standpoint. If you boil it down even further, it’s making sure there’s a structure surrounding your data, where you have the best people looking at the data to make sure that it is accurate, aligned, secure, and safe.

Why does data governance matter? Is it something every organization should do?

Every organization is doing data governance, whether it’s formalized or not, so in many cases, it’s simply putting a name to and creating a formalized structure around something that people are already doing.

Without data governance, it’s the wild west of data, and it’s total chaos. Data governance is about unraveling that chaos.

A good example is master data, for example, a master list of products in an organization. If it’s not a governed list, person A is going to be using one list, person B is going to be using a different list, and so on, and that leads to chaos. You can’t perform any useful analysis on that data because everyone is accessing different data. You can’t make intelligent business decisions based on data that isn’t governed.

You need someone to define a master list of products, with no duplicates, validated data, that’s stored somewhere where it’s safe and secure. That way, everyone in the organization is accessing the same, trusted data from the master data system.

Now, a lot of companies do this already. They’ve been doing it for 20-30 years, but they may not call it data governance. But they’ve created a lookup table that has their products, and somebody is looking at it and checking it and saying it’s accurate. That’s governed data.

What are some of the barriers that people come up with when trying to organize a more formalized approach to data governance?

There are two main challenges, as I see it.

First, change management is hard. Most people want to do data governance; they understand why it’s valuable. But when it comes to changing their job a little bit or adding a new process to create more structure around data governance, there can be resistance.

You want to make sure that you’re not creating friction with new processes. For instance, if someone is used to accessing a specific report immediately, and you add a new process where they have to fill out a form to gain access to those analytics, you need to clearly communicate why the benefits outweigh that little bit of additional friction that’s added to their day.

The second, and much bigger challenge, is trying to do too much. It‘s easy to look at your whole organization and say, “We need governance here, there, and everywhere, and we’ve got to do it all at once.”

Often, we will see these kinds of programs tank because it’s too much at once. Once that scope creep happens, all of a sudden, you’re accomplishing nothing.

The way that I like to handle it is with an agile, iterative, use case-based approach. What is the one thing you can do that will add the most value? And then just keep chipping away, one use case at a time. The beauty of an approach like this—which is the product management approach that Gensquared uses with all our client’s data initiatives—is that it gets easier and faster with every use case.

How do you recommend organizations decide on that first, most valuable data governance use case?

Prioritization frameworks are really effective. We recently used the RICE (Reach, Impact, Confidence, Effort) framework for a large client in the food retail industry.

With RICE, you look at all the use cases that you have, assign them a score for each factor, then multiply Reach by Impact by Confidence, and divide by Effort for your RICE score. That number tells you which use case gives you the most bang for your buck, and what’s your lowest-hanging fruit.

It’s an effective way to make data governance more palatable since it can feel overwhelming at first glance.

What are the core “features” of data governance?

Data governance has to be collaborative. You can’t spring it on people. It’s not about dictating to people how to do their jobs, and it’s not about saying “this is the way things are going to be from now on”.

With a collaborative approach that welcomes input and buy-in from all departments and stakeholders, you will make governance a lot easier for yourself.

What roles do people play in data governance?

A data governance council is critical because they are the people who are going to be making the decisions.

Typically, people fit into one of three tiers of governance:

Strategic: These are the key decision-makers who determine the high-level data governance strategy.
Tactical: This is typically a working data governance council or committee, made up of leaders from each line of business. These people take the strategy and decide how to actually implement it. They bring the tactics back to their departments and organize the next steps.
Operational: This is where data stewards come into play. They’re in charge of facilitating the governance initiatives on a pragmatic, day-to-day basis. They do everything from validating the information in a table or answering questions on data.

This is why collaboration and alignment are so important. Every part of the enterprise is involved with data governance, so there must be a two-way flow of information as people run into data issues. That information needs to go back to the council and then spin back to the operational folks to turn into actionable results.

Finally, on a personal note—what drew you to data governance as a career?

I like data governance because it gives you an understanding of the holistic nature of data in a company. Data governance isn’t just data in a table—it’s about how we’re using data to make smarter, more strategic decisions, how we align on definitions, how we make sure we’re compliant with legal. It touches on every single aspect of the business.

Data governance is the future. It’s the natural progression of where data has to go. As our technologies have increased, as cloud-based software has increased, as we’ve moved into more powerful and wider-reaching databases, data warehouses, and data lakes, it’s become more and more important that we govern our data. And of course, privacy and regulation are more pervasive than ever, even outside of traditionally regulated industries like health care and finance. Data governance is critical for maintaining regulatory compliance in addition to everything else it’s used for.

Do you have any advice for data professionals who are looking to implement data governance at their organization? What are the essential, go-to resources?

DAMA is a big one. That’s what I started with.

There are a ton of resources out there. The best thing you can do is curate a wide variety of knowledge. You might not find one framework that perfectly fits your organization.

There are even multiple ways to do governance—federated models, centralized models, decentralized models. My best advice is to seek out knowledge from a variety of sources, and learn as much as you can to find the best data governance framework for your organization.