What is Data Governance?

I recently did a quick movie for a Talend promotion to define data governance. It turns out that defining data governance is trickier than you think. Here, I examine the characteristics of data management initiative and how they define data governance.

Top Ten Root Causes of Data Quality Problems: Part Five

Part 5 of 5: People Issues
In this continuing series, we're looking at root causes of data quality problems and the business processes you can put in place to solve them.  Companies rely on data to make significant decisions that can affect customer service, regulatory compliance, supply chain and many other areas. As you collect more and more information about customers, products, suppliers, transactions and billing, you must attack the root causes of data quality. 

Root Cause Number Nine: Defining Data Quality
More and more companies recognize the need for data quality, but there are different ways to   clean data and improve data quality.   You can:

• Write some code and cleanse manually
• Handle data quality within the source application
• Buy tools to cleanse data

However, consider what happens when you have two or more of these types of data quality processes adjusting and massaging the data. Sales has one definition of customer, while billing has another.  Due to differing processes, they don’t agree on whether two records are a duplicate.

Root Cause Attack Plan

• Standardize Tools – Whenever possible, choose tools that aren’t tied to a particular solution. Having data quality only in SAP, for example, won’t help your Oracle, Salesforce and MySQL data sets.  When picking a solution, select one that is capable of accessing any data, anywhere, at any time.  It shouldn't cost you a bundle to leverage a common solution across multiple platforms and solutions.
• Data Governance – By setting up a cross-functional data governance team, you will have the people in place to define a common data model.

Root Cause Number Ten: Loss of Expertise
On almost every data intensive project, there is one person whose legacy data expertise is outstanding. These are the folks who understand why some employee date of hire information is stored in the date of birth field and why some of the name attributes also contain tax ID numbers. 
Data might be a kind of historical record for an organization. It might have come from legacy systems. In some cases, the same value in the same field will mean a totally different thing in different records. Knowledge of these anomalies allows experts to use the data properly.
If you encounter this situation, there are some business processes you can follow.

Root Cause Attack Plan

• Profile and Monitor – Profiling the data will help you identify most of these types of issues.  For example, if you have a tax ID number embedded in the name field, analysis will let you quickly spot it. Monitoring will prevent a recurrence.
• Document – Although they may be reluctant to do so for fear of losing job security, make sure experts document all of the anomalies and transformations that need to happen every time the data is moved.
• Use Consultants – Expert employees may be so valuable and busy that there is no time to document the legacy anomalies. Outside consulting firms are usually very good at documenting issues and providing continuity between legacy and new employees.

This post is an excerpt from a white paper available here. More to come on this subject in the days ahead.

See also:

• Part One: The Basics
• Part Two: Renegades and Pirates
• Part Three: Secret Code and Corporate Evolution
• Part Four: Data Flow

Data Quality Scorecard: Making Data Quality Relevant

Most data governance practitioners agree that a data quality scorecard is an important tool in any data governance program. It provides comprehensive information about quality of data in a database, and perhaps even more importantly, allows business users and technical users to collaborate on the quality issue.

However, there are multiple levels of metrics that you should consider. There are:

	METRIC CLASSIFICATION	EXAMPLES
1	Metrics that the technologists use to fix data quality problems	7% of the e-mail attribute is blank. 12% of the e-mail attribute does not follow the standard e-mail syntax. 13% of our US mail addresses fail address validation.
2	Metrics business people use to make decisions about the data	9% of my contacts have invalid e-mails.  3% have both invalid e-mails and invalid addresses.
3	Metrics managers use to get a big picture	This customer data is good enough to use for a campaign.

All levels are important for the various members of the data governance team.  Level one shows the steps you need to take to fix the data.  Level two shows context to the task at hand. Level three tells the uniformed about the business issue without having to dig into the details.

So, when you’re building your DQ metrics, remember to roll-up the data into metrics into slightly higher formulations. You must design the scorecards to meet the needs of the interest of the different audiences, from technical through to business and up to executive. At the beginning of a data quality scorecard is information about data quality of individual data attributes. This is the default information that most profilers will deliver out of the box. As you aggregate scores, the high-level measures of the data quality become more meaningful. In the middle are various score sets allowing your company to analyze and summarize data quality from different perspectives. If you define the objective of a data quality assessment project as calculating these different aggregations, you will have much easier time maturing your data governance program. The business users and c-level will begin to pay attention.

Change Management and Data Governance

Years ago, I worked for a large company that spent time and effort on change management. It has been popular with corporations that plan significant changes as they grow or down-size. Companies, particularly high-tech companies, use change management to be more agile and respond to rapid changes in the market.

As I read through the large amount of information on change management, I’m struck by the parallels between change management and data governance. The focus is on processes. It ensures that no matter what changes happen in a corporation, whether it’s downsizing or rapid growth, significant changes are implemented in an orderly fashion and make everyone more effective.

On the other hand, humans are resistant to change. Change management aims to gain buy-in from management to achieve the organization's goal of an orderly and effective transformation. Sound familiar? Data governance speaks to this ability to manage data properly, no matter what growth spurts, mergers or downsizing occurs. It is about changing the hearts and minds of individuals to better manage data and achieve more success while doing so.

Change Management Models
As you examine data governance models, look toward change management models that have been developed by vendors and analysts in the change management space.  One that struck my attention was the ADKAR model developed by a company called Prosci. In this model, there are five specific stages that must be realized in order for an organization to successfully change. They include:

• Awareness - An organization must know why a specific change is necessary.
• Desire - The organizational must have the motivation and desire to participate in the call for change.
• Knowledge – The organization must know how to change. Knowing why you must change is not enough.
• Ability - Every individual in the company must implement new skills and processes to make the necessary changes happen.
• Reinforcement - Individuals must sustain the changes, making them the new behavior, averting the tendency to revert back to their old processes.

These same factors can be applied when assessing how to change our own teams to manage data more effectively.  Positive change will only come if you work on all of these factors.

I often talk about business users and IT working together to solve the data governance problem. By looking at the extensive information available on change management, you can learn a lot about making changes for data governance.

Data Quality Pro Discussion

Last week I sat down with Dylan Jones of DataQualityPro.com to talk about data governance. Here is the replay. We discussed a range of topics including organic governance approaches, challenges of defining data governance, industry adoption trends, policy enforcement vs legislature and much more.

Link

Three Conversations to Have with an Executive - the Only Three

If you’re reading this, you’re most likely in the business of data management. In many companies, particularly large ones, the folks who manage data don’t much talk to the executives. But every so often, there is that luncheon, a chance meeting in the elevator, or even a break from a larger meeting where you and an executive are standing face to face.  (S)he asks, what you’re working on. Like a boy scout, be prepared.  Keep your response to one of these three things:

1. Revenue – How has your team increased revenue for the corporation?
2. Efficiency – How has your team lowered costs by improving efficiency for the corporation?
3. Risk – How have you and your team lowered the risk to the corporation with better compliance to corporate regulations?

The executive doesn’t want to hear about schemas, transformations or even data quality. Some examples of appropriate responses might include:

• We work on making the CRM/ERP system more efficient by keeping an eye on the information within it. My people ensure that the reports are accurate and complete so you have the tools to make the right decisions.
• We’re doing things like making sure we’re in compliance with [HIPAA/Solvency II/Basel II/Antispam] so no one runs afoul of the law.
• We’re speeding up the time it takes to get valuable information to the [marketing/sales/business development] team so they can react quickly to sales opportunities
• We’re fixing [business problem] to [company benefit].

When you talk to your CEO, it’s your opportunity get him/her in the mindset that your team is beneficial, so when it comes to funding, it will be something they remember. It’s your chance to get out of the weeds and elevate the conversation.  Let the sales guys talk about deals. Let the marketing people talk about the market forces or campaigns. As data champions, we also need to be prepared to talk about the value we bring to the game.

A Data Governance Mission Statement

Every organization, including your data governance team has a purpose and a mission. It can be very effective to communicate your mission in a mission statement to show the company that you mean business.  When you show the value of your team, it can change your relationship with management for the better.

The mission statement should pay tribute to the mission of the organization with regard to values, while defining why the data governance organization exists and setting a big picture goal for the future.
The data governance mission statement could revolve around any of the following key components:

• increasing revenue
• lowering costs
• reducing risks (compliance)
• meeting any of the organization’s other policies such as being green or socially responsible

The most popular format seems to follow:

Our mission is to [purpose] by doing [high level initiatives] to achieve [business benefits]

So, let’s try one:

Our mission is to ensure that the highest quality data is delivered via company-wide data governance strategy for the purpose of improving the efficiency, increasing the profitability and lowering the risk of the business units we serve.

Flopped around:

Our mission is to improve the efficiency, increase the profitability and lower the business risks to Acme’s business units by ensuring that the highest quality data is delivered via company-wide data governance strategy.

Not bad, but a mission statement should be inspiring to the team and to management. Since the passions of the company described above are unknown, it’s difficult for a generic mission statement to be inspirational about the data governance program. That’s up to you.
 
Goals & Objectives
There are mission statements and there are objectives. While every mission statement should say who you are and why you exist, every objective should specify what you’re going to do and the results you expect.  Objectives include activities that can be easily tracked, measured, achieved and, of course, meet the objectives of the mission.  When you start data governance projects, you can look back to the mission statement to make sure we’re on track. Are you using our people and technology in a way that will benefit the company?

Staying On Mission
When you take on a new project, the mission statement can help protect us and ensure that the project is worthwhile for both the team and the company. The mission statement should be considered as a way to block busy-work and unimportant projects.  In our mission statement example above, if the project doesn’t improve efficiency, lower costs or lower business risk, it should not be considered.

In this case, your can clearly map three projects to the mission, but the fourth project is not as clear.  Dig deeper into the mainframe project to see if any efficiency will come out of the migration.  Is the data being used by anyone for a business purpose?

A Mission Never Ends
A mission statement is a written declaration of a data governance team's purpose and focus. This focus  normally remains steady, while objectives may change often to adapt to changes in the business environment. A properly crafted mission statement will serve as a filter to separate what is important from what is not and to communicate your value to the entire organization.

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Overcoming Objections to a Data Governance Program

You’ve created a wonderful proposal for a comprehensive data governance program. You’ve brought it up to management, but the chiefs tell you there’s just no budget for data governance. Now what?

The best thing you can do it to keep at it. It often takes time to win the hearts and minds of your company. You know that any money spent on data governance will usually come back with multipliers. It just may take some time for others to get on board. Be patient and continue to promote your quest.

Here are some ideas for thinking about your next steps for your data governance program:

Corporate Revenue
Today, companies manage spending tightly, looking at the expenses and revenue each fiscal quarter and each month to optimize the all-important operating income (revenue minus expenses equals operating income). If sales and revenue are weak, management gets miserly. On the other hand, if revenue is high and expenses are low, your high-ROI proposal will have a better chance for approval.

For many people, this corporate reality is hard to deal with. Logical thinkers would suggest that if something is broken, it should be fixed, no matter how well the sales team is performing. The people who run your business have their first priorities set on stockholder value. You too should pay attention to your company’s sales figures as they are announced each quarter. If your company has a quarterly revenue call, use it to strike when the environment for spending is right.

Cheap Wins
If there is no money to spend on information quality, there still may be potential for information quality wins for you to exploit. For example, let’s say you were to profile or make some SQL queries into your company’s supply chain system database and you found a part that has a near duplicate. So, part number “21-998 Condenser” and part number “2-1-998 Cndsr” exist as duplicated parts in your supply chain.

After verifying the fairly obvious duplicate, you can ask your friend on the procurement side how much it costs to store and hold these condensers in inventory. Then use some guerilla marketing techniques to extol the virtues of data governance. After all, if you could find this with just SQL queries, consider how much you could find with a data discovery/profiling tool. Better yet, consider how much you could find with a company-wide initiative.  In a previous blog post, I referred to this as the low-hanging fruit.

Case Studies
Case studies are a great way to spread the word about data governance. They usually contain real-world examples, often of your competitors, who are finding gold with better attention to information quality. Vendors in the data governance space will have case studies on their websites, or you can get unpublished studies by asking your sales representative.

Consider that built-in desire of your company to be competitive, and keep your Google searches and alerts tuned to what data management projects are underway at your competitors.

Analysts
Analysts are another valuable source for proving your point about the virtues of data governance. Your boss may have installed his own custom spam filter against your cajoling on data governance. But he doesn’t have to take your word for it; he can listen to an industry expert.

If you own a subscription to an analyst firm, use it to sell the power of data governance. Analysts offer telephone consultations, reports and webinars to clients. These offerings may be useful to sway your team.  If you are not a client of these firms, go to the vendors. If there is a crucial report, they will often license it to offer on their website for download, particularly if it speaks well about their solution.

Data Governance Expert Sessions
This technique also falls within the category of “don’t just take my word for it.” You can find a data governance workshop from many vendors to assist your organization with developing your data quality strategies. Often conducted for a group, the session leader interacts with a group of your choosing and presents the potential for improving the efficiency of your business with data governance. As the meeting leader, you would invite both technologists and business users. Include those who are skeptical of the value a data-quality program will bring to their company; a third-party opinion may sway them. The cost is usually reasonable and it can help the group understand and share key concepts of data governance.

Guerrilla Marketing
Why not start your own personal crusade, your own marketing initiative to drive home the power of information quality? In my previous installment of the data governance blog, I offer graphics for use in your signature file to drive home the importance of IQ to your organization. Use the power of a newsletter, blog, or e-mail signature to get your message across.


Excerpt from Steve Sarsfield's book "The Data Governance Imperative"

New Book - The Data Governance Imperative

My new book entitled The Data Governance Imperative is making its way to Amazon, Barnes and Noble, and other outlets this week. I’m very proud of this and happy to see it finally hit the streets. It was a lot of work and dedication to get it done.

I decided to write this book because I saw a common recurring question that arose during discussions about data governance. How do I get my boss to believe that data governance is important? How do I work with my colleagues to build better information and a better company? How do I break through the barriers preventing data governance maturity like getting money, resources and expertise to accomplish the task? When it comes to justifying the costs of data governance to their organization, building organizational processes, learning how to staff initiatives, understanding the role and importance of technologies, and dealing with corporate politics, there is little information available.

In my years working at Trillium Software, I have been exposed to many great projects in Fortune 1000 companies worldwide. Over the years, I’ve made note of the success factors that contribute to strong data governance. I’ve seen successful strategies for data governance and the common threads to success within and across the industry.

I’ve written the Data Governance Imperative to help readers pioneer data governance initiatives, breaking through political barriers by shining a light on the benefits of corporate information quality. This book is designed to give data governance team members insight into the art of starting data governance. It could be helpful to:

• Data governance teams – those looking for direction/validation in starting a corporate data governance initiative.
• Business stakeholders – those working in marketing, sales, finance and other business roles who need to understand the goals and functions of a data governance team.
• C-level executives – those looking to learn about the benefits of data governance without having to read excessive technical jargon, or even those who need to be convinced that data governance is the right thing to do.
• IT executives – those who believe in the power of information quality but have faced challenges in convincing others in their corporation of its value.

This book does not focus on the technical aspects of data governance, although technologies are discussed. There are some great books on the technology of data governance in the market today. Some are listed on the left side of this blog in the carousel.

The Best Books on Data Governance

Is there a comprehensive book on data governance that we should all read to achieve success? At the time of this post, I'm not sure there is. I haven't seen it yet. If you think about it, such a book would make War and Peace look like a Harlequin novel in terms of book size in order to cover the all aspects of the topic. Instead, we really must become students of data governance and begin to understand large knowledge areas such as 1) how to optimize and manage processes; 2) how to manage teams and projects; 3) public relations and marketing for internal project promotion; and 4) how to implement technologies to achieve data governance, just to name a few.

I’ve recently added an Amazon widget to my blog that lists some printed books on data governance-related topics. The books cover the four areas I’ve mentioned. As summer vacation arrives, now is the time to buy your books for the beach and read up! After all, what could be more relaxing on a July afternoon than a big frozen margarita and the book “Business Process Improvement: The Breakthrough Strategy for Total Quality, Productivity, and Competitiveness” by James Harrington?

The Amazon affiliate program generates just a few pennies for each book, but what money it does generate will be donated to charity. The appeal of the Amazon widget is that it's a good way to store a list of books and provide direct links to buy. If you have some suggestions to add to the list, please share them.

EDIT: My book on data governance is now available on Amazon. The Data Governance Imperative.

Data Governance Structure and Organization Webinar

My colleague Jim Orr just did a great job delivering a webinar on data governance. You can see a replay of the webinar in case you missed it. Jim is our Data Quality Practice Leader and he has a very positive point of view when it comes to developing a successful data governance strategy.
In this webinar, Jim talks exclusively about the structure and the organization behind data governance. If you believe that data governance is people, process and technology, this webinar covers the "people" side of the equation.

Must-read Analyst Reports on Data Governance

If you’re thinking of implementing a data governance strategy at your company, here are some key analyst reports I believe are a must-read.

Data Governance: What Works And What Doesn't by Rob Karel, Forrester
A high-level overview of data governance strategies. It’s a great report to hand to a c-level executive in your company who may need some nudging.

Data Governance Strategies
by Philip Russom and TDWI
A comprehensive overview of data governance, including extensive research and case studies. This one is hot off the presses from TDWI. Sponsored by many of the top information quality vendors.

The Forrester Wave™: Information Quality Software by J. Paul Kirby, Forrester
This report covers the strengths and weaknesses of top information quality software vendors. Many of the vendors covered here have been gobbled up by other companies, but the report is still worth a read. $$

Best Practices for Data Stewardship
Magic Quadrant for Data Quality Tools
by Ted Friedman, Gartner
I have included the names of two of Ted’s reports on this list, but Ted offers much insight in many forms. He has written and spoken often on the topic. (When you get to the Gartner web site, you're going to have to search on the above terms as Gartner makes it difficult to link directly.) $$
Ed Note: The latest quadrant (2008) is now available here.

The case for a data quality platform
Philip Howard, Bloor Research
Andy Hayler and Philip Howard are prolific writers on information quality at Bloor Research. They bring an international flair to the subject that you won’t find in the rest.

Politics, Presidents and Data Governance

I was curious about the presidential candidates and their plans to build national ID cards and a database of citizens, so I set out to do some research on the candidates stance on this issue. It strikes me as a particularly difficult task, given the size of the database that would be needed and the complexity. Just how realistic would the data governance strategy for the candidates be?

I searched the candidate’s web sites with the following Google commands:
database site:http://www.johnmccain.com
database site:http://www.barackobama.com
database site:http://www.hillaryclinton.com

Hardly scientific, but interesting results nonetheless. The candidates have very different data management plans for the country. This simple search gave some insight into the candidate’s data management priorities.

Clinton:
Focused on national health care and the accompanying data challenges.
• Patient Health Care Records Database
• Health Care Provider Performance Tracking Database
• Employer History of Complaints
Comments: It’s clear that starting a national database of doctors and patients is a step toward a national health plan. There are huge challenges with doctor data, however. Many doctors work in multiple locations, having a practice at a major medical center and a private practice, for example. Consolidating doctor lists from insurance companies would rely heavily on unique health care provider ID numbers, doctor age and sex, and factors other than name and address for information quality. This is an ambitious plan, particularly given data compliance regulations, but necessary for a national health plan.

Obama:
Not much about actual database plans, but Obama has commented in favor of:
• Lobbyist Activity Database
• National Sex Offender Database
Comments: Many states currently monitor sex offenders, so the challenge would be coordinating a process and managing the metadata from the states. Not a simple task to say the least. I suspect none of the candidates are really serious about this, but it’s a strong talk-track. Ultimately, this may be better left to the states to manage.
As far as the lobbyist activity database, I frankly can’t see how it’d work. Would lobbyists would complete online forms describing their activities with politicians. If lobbyists have to describe their interaction with the politician, would they be given an open slate in which to scribble some notes about the event/gift/dinner/meeting topics? This would likely be chock full of unstructured data, and its usefulness would be questionable in my opinion.

McCain:
• Grants and Contracts Database
• Lobbyist Activity Database
• National Sex Offender Database
Comments: Adding in the grants and contracts database into McCain’s plan, I see this as similar to Obama’s plan in that it’s storage of unstructured data.

To succeed in any of these plans from our major presidential candidates, I see a huge effort in the “people” and “process” components of data governance. Congress will have to enact laws that describe data models, data security, information quality, exceptions processing and much more. Clearly, this is not their area of expertise. Yet the candidates seem to be talking about technology as a magic wand to heal our country’s problems. It’s not going to be easy for any of them to make any of this a reality, even with all the government’s money.
Instead of these popular vote-grabbing initiatives, wouldn't the government be better served by a president who is understands data governance? When you think about it, the US Government is making the same mistake that businesses make, growing and expanding data silos, leading to more and more inefficiencies. I can’t help but thinking what we really need is a federal information quality and metadata management agency (since the government like acronyms, shall we call it FIQMM) to oversee the government’s data. The agency could be empowered by the president to have access to government data, define data models, and provide people, process and technologies to improve efficiency. Imagine what efficiencies we could gain with a federal data governance strategy. Just imagine.

Mergers and Acquisitions: Data's Influence on Company Value

Caveat Emptor! Many large companies have a growth strategy that includes mergers and acquisitions, but many are missing a key negotiating strategy during the buying process.

If you’re a big company, buying other companies in your market brings new customers into your fold. So, rather than paying for a marketing advertising campaign to get new customers, you can buy them as part of an acquisition. Because of this, most venture capitalists and business leaders know that two huge factors in determining a company’s value during an acquisition are the customer and prospect lists.

Having said that, it’s strange how little this is examined in the buy-out process. Before they buy, companies look at certain assets under a microscope - tangible assets like buildings and inventory are examined. Human assets, like the management staff are given a strong look. Cash flow is audited and examined with due diligence. But, data assets are often only given a hasty passing glance.

Data assets quickly dissolve when the company being acquired has data quality issues. It’s not uncommon for a company to have 20%, 40%, or even 50% customer duplication (or near duplicates) in their data base, for example. So, if you think you’re getting 100,000 new customers, you may actually be getting 50,000 after you cleanse. It’s also common for actual inventory levels in the physical warehouse to be misaligned with the inventory levels in the ERP systems. This too may be due to data quality issues, and lead to surprises after the acquisition.

So what can you do as an acquiring company to mitigate these risks? The key is due diligence on data. Ask to profile the data of the company you’re going to buy. Bring in your team, or hire a third party to examine the data. Look at the customer data, the inventory data, the supply chain data or whatever data is a valuable asset in the acquisition. If privacy and security are an issue the results of the profiling can usually be rolled up into some nice charts and graphs that’ll give you a picture of the status of organizational information.

In my work with Trillium Software, I have talked to customers who have saved millions in acquisition costs by evaluating the data prior to buying a company. Some have gone so far as evaluation of the overlap between their own customer base and the new customer base to determine value. Why pay for a customer when (s)he is already on the customer list?

Profiling lets you set up business rules that are important to your company. Does each record have a valid tax ID number? What percentage of the database contact information is null? How many bogus e-mails appear? Does the data make sense, or are there a lot of near duplicates and misfielded data. In inventory data, how structured or unstructured is the data? All of these can quickly be ascertained with a data profiling technology. All of these technical issues can be correlated into business value, and therefore negotiating value, for your company.

The data governance teams that I have met that I have done this due diligence for their companies have become real superstars, and are very much a strategic part of their corporations. It’s easy for a CEO to see the value you bring when you can prove that they are paying the right price for a company acquisition.

Approaching IT Projects with Data Quality in Mind

I co-authored a white paper at the end of 2006 with a simple goal: to talk directly to project managers about the process they go through when putting together a data intensive project. By “data intensive” project, I mean dealing with mergers and acquisitions data, CRM, ERP consolidation, Master Data Management, and any project where you have to move big data.

Project teams can be so focused on application features and functions that they sometimes miss the most important part. In the case of a merger, project teams must often deal with unknown data coming in from the merger that may require profiling at part of their project plan. In the case of a CRM system, companies are trying to consolidate whatever ad hoc system is in place and data from people who may care very little about data quality. In the case of master data management and data governance, the thought of sharing data across the enterprise brings to mind a need for a corporate standard for data. Data intensive projects may have different specific needs, but just remembering that you need to consider data in your project will get you far.

To achieve real success, companies need to plan a way to manage data as part of the project steps. If you don’t think about the data as part of the project preparation, blueprinting, implementation, rollout preparation, go live and maintenance, your project is vulnerable to failure. Most commonly, delay and failure is due to late-project realization that the data has problems. Knowing the data challenges you face early in the process is the key to success.

This white paper discusses the importance and ways to best involve business users in the project to ensure their needs are met. It covers ways to stay in scope on the project while considering the big picture and the going concern of data quality within your organization. Finally, it covers how to incorporate technology throughout a project to expedite data quality initiatives. The white paper is still available today for download. Click here and see "Data Quality Essentials: For Any Data-Intensive Project"

Answer to above: All of them

Taking Data Governance from Theory to Practical Application

There is a lot of theoretical hype about data governance in the data management world, some valuable, and some not so valuable.

I personally have a hard time with any articles that try to cut data governance down to the “five most important things”. To me, it’s akin to saying, here are the five most important things you need to remember when trying to disassemble and reassemble a Boeing 757. You just can’t distill it that far and expect anything useful. Usually, this is the type of white paper produced by a marketing department run amok and useful only as a preface in the book of data governance.

Instead of trying to distill it, we need to become students of data governance and then take that knowledge and shape it for our company. It’s safe to say that the list of five, ten, or twenty five most important things to watch will be different across industry, across the company, across the globe, and across time. That’s why I have plenty to write about in this blog, and plenty to talk about on my webinars.

However, I wanted to share with you another chapter in the book of data governance that speaks to practical application. My colleague Len Dubois had a chance to interview Nigel Turner and Dave Evans from BT Design. I’ve mentioned Nigel and Dave before on my blog. They have a phenomenal story of starting up small and building increasing ROI over time. They’ve calculated huge gains in efficiency, to the tune of $1 Billion. I also credit them for the very clever Do Nothing Option.

If you’d like to hear this three-part podcast series, as told by these pioneers of data governance from Wales, please follow the link. The podcast covers the information quality challenges tackled, software selection, and lessons learned.

Data Governance – Does it take a platform?

I was reading through a major enterprise software vendor’s white paper and their recommendations on how to launch a data governance program. (I’m not going to provide a link - it wasn’t worth it.) Of course, much of the messaging was around buying software and the “platform” you need to do data governance… their platform.

Yet, I’m not sure it’s the wisest choice to start by buying a data governance platform. If your solution to data governance is to buy software, then you’re not really doing data governance. So much of data governance is about things like getting executives to recognize data as an asset, setting up processes, planning teams and resources, the politics of data ownership, understanding the goals of the organization and making decisions about data to support them, and so on.

Now I know it’s blasphemy for a guy who works for an enterprise software company to talk like that. In the past, I probably have been guilty of pushing the platform over process improvements. But, it’s a new day. I see real successes starting to emerge from companies who begin by taking a look at the strategy and process of data governance in the context of their business plan. Companies are beginning to soul-search a bit, before buying a platform, to know how ready they are for data governance and plan their maturation process.

Why not bring in some expertise on data governance first? Bring in the right mix of technology and business experience to build a plan, build a process and work through the politics of data governance first. There are some pretty good systems integrators out there who can help. We have partnerships with Accenture and Deloitte, for example, and they have helped set strategy on many projects.

Trillium Software also has a growing business around the business strategy of data governance. These programs are run by an arm of our professional services team called strategic services, and they too are really starting to show promise, as they work hand-in-hand with our customers to set up the processes and strategy of data governance, opening up communications between IT and management on data governance. These include the following programs:

• Data Quality Workshop - a knowledge sharing exercise that incorporates interactive group dynamics, analytics, and presentations to learn about the customer’s business, understand and share key aspects of a total data quality solution, and determine how to best solve business problems through a comprehensive data quality program. We’ll come in for a couple of days and help you through some of the data governance strategy.

• Strategic Planning Services - a service offering that helps you to build a future vision for data quality that optimizes processes and improves data quality enterprise-wide. This service focuses on future data quality strategies such as dealing with complex enterprise data quality deployments, expansion of data quality initiatives and the effects of mergers and acquisitions on the business.

• Data Governance Planning – This service helps organizations with developing, refining, and supporting their data governance strategies and programs. It recognizes that data quality by itself does not define data governance. Rather, it also includes a focus on business processes and people to achieve success.

If this is something that your company needs, send me an e-mail and I’ll set it up for you, or find out more here. These workshops are particularly helpful if you have some key stakeholders dragging their feet on data governance. They can help you all get on the same page.

Does it take a platform to do data governance? Maybe, but data governance is a far-off dream for many companies. In this case, it takes a lot more than technology to fulfill a dream.

The “Do Nothing” Option

When it comes to writing proposals and setting scope for data intensive projects like data integration, master data management, CRM, and data warehouse, project managers often find themselves struggling to justify the additional funding needed for information quality. But I recently picked up a neat trick when I was talking to the folks at BT. If you recall, BT has a huge implementation of enterprise-wide information quality and a great data governance story. If you want to read the details, they are written up in a 2006 Gartner report (See " Strategic Focus on Data Quality Yields Big Benefits for BT" on Gartner.com). Also, you can hear it in BT’s own words in a webinar on Trilliumsoftware.com.
Project managers, can I share a secret with you? Let’s keep this secret just between us, OK? Let’s not let upper management know you’re pulling this stuff on them.
The key is to swaying the approval process, according to BT, is to always calculate what will happen if you don’t do anything about the data quality. They call it the “Do Nothing Option”. Sounds simple, but I suspect it’s something that is missing from many proposals. In your scoping documents, make sure that you state in black and white both the ROI of improving data quality processes and the potential risks that you run when you ignore it. In short, if you invest resources in information quality, all will be right with the world. If you do nothing, anarchy and chaos will ensue.
For example, you’re installing a new CRM application. The new CRM application will likely have ROI on customizing workflow, tracking customer interactions, efficiently contacting customers and prospects, and reporting, among many other benefits. However, if you do nothing with information quality, what are the risks and what are the costs associated with the risks? Be specific about what kinds of problems are likely to occur and the costs associated with them. Are there mailing costs? Are there process costs associated with chasing down duplicate accounts and customers? Are there billing efficiencies that can be mitigated with information quality? Any inventory inefficiencies? Will reports show incorrect information requiring more process and problem chasing? Will all forms of corporate compliance be met with the current data, or are there risks of fines associated with compliance? Will data management issues instead have to be handled by the call center and if so, at what cost? What is the long-term impact of not managing the data to the corporation? Etc. What’s the cost of doing nothing?
If you’re still having trouble with management, even after explaining what will happen if you do nothing, feel free to contact me. I’m very interested in the process that project managers must endure in order to do the right thing and take on the responsibility for managing data.

The Rise of the Business-focused Data Steward

In a December 2007 research note from Gartner entitled “Best Practices for Data Stewardship”, Gartner give some very practical and accurate advice on starting and executing a data steward program. They reiterate this advice in a press release issued this month. The new advice is to have business people become your data stewards. So, in marketing you have someone assigned as a data steward to work with the IT. The business person knows the meaning of the data as well as where they want to go with it. They become responsible for the data, and owners of it.

It’s a great concept, and one that I expect will become more and more a reality this year. However, there is some growth that needs to happen in the software industry. There are very few tools that serve a business-focused data steward. Most tools on the market are additional features that have been tacked on to IT-focused tools. Sure, a data profiler can show some cool charts and graphs, but not many business users want to learn how to use them. Should a business user really have to learn about metadata, entities, and attributes in order to find out if the data meets the need of the organization?

Rather, a marketing person wants to know if (s)he can do an offer mailing without getting most of it back. A CIO wants to know if a customer database that they just got as part of a merger has complete and current information. Accounting wants to know that they have valid tax ID numbers (social security numbers) for customers with whom they give credit, and the compliance team want to know that they are stopping those listed on the OFAC from opening accounts. Metadata? They don’t care. They just need the metrics to track the business problem.

This was really the concept that Trillium Software had when we designed TS Insight, our data quality reporting tool. The tool uses business rules and analysis from our profiler and presents them in a very friendly way – via a web browser. The more technical users can set up regular updates that display compliance with the business rules. The less technical users can open their web browsers to their customized page and metrics that are important to them. The business rules can track pretty much anything about the data without being too technical.

TS Insight is still in ramp-up for us. We came out with version 1.0 last year and we’re about to release version 2.5 this quarter. Still, we have a big head start on anyone else in the industry with this tool, serving the needs of the business-focused data steward. If this is something you’d like to see, please send me an e-mail and I’ll set up a demo.

Data Governance and the Glib

My absolute favorite blog is the Creating Passionate Users blog, authored by Kathy Sierra and Dan Russell. I am a faithful reader and often find value in going back in time and re-reading the articles.

One article is extremely powerful in the data governance world, particularly when it comes to managing teams of business users and IT users. I’m referring to a brilliant entry entitled “When only the glib win, we all lose” I quote:

In way too many meetings, the fastest talkers win. And by "fastest talkers", I mean those who are the first to articulate an idea, challenge, issue, whatever. Too many of us assume that if it sounds smart, it probably is, especially when we aren't given the chance to think about it. The problem is, the guy with the "gut feeling"--the one who senses that something's not right, but has no idea how to explain it, let alone articulate it on the spot--might be right. Too bad, though, because the glib usually rule.

When you begin to build your teams for data governance, you must accommodate for both the glib and those who mull. Not to stereotype, but technical users may be more the type to contemplate, while your sales and marketing folks, for example, might be more the glib type. If your meetings are ruled by the glib talking sales folks, the mulling IT folks may become frustrated, causing pain and conflict.

Frankly, very few decisions need to be made right away. Decisions to completely change the ways your company does business, which are often part of data governance planning, can probably stand a little think-time. One of the best suggestions I’ve heard was for teams to adopt a 24 hour rule, which allows for anyone in the group to call for a break for 24 hours before making a final decision on a big issue. If someone feels like something is missing, chances are it is. People can take time to organize, digest and process. With a small waiting period on big decisions, the glib won’t rule over those who need to mull, and justice will be served.

Also, it may be necessary for those who mull to come out of their comfort zone and become more glib. The blog entry highlights a "Glib Continuum" with ideas for changing your place in the continuum. Attending that Dale Carnegie training, or joining Toastmasters are good ways to move up the glib continuum. Thanks to Kathy and her readers for the excellent ideas.