In the Field with our Experts

’Tis the Season for a Data Governance Carol (Part Two)

By Rob Paller, Consultant

 

The writing was on the wall, and Ebenezer was beginning to comprehend that while the mergers and acquisitions looked good on Wall Street, the company was imperiled by the aftermath of poorly integrated systems, an absence of proper data management, and a gaping void in the governance of the company’s data. Furthermore, Ebenezer knew that one more ghost had yet to call upon him. Never before had the fear of uncertainty enveloped Ebenezer, but tonight was different.

The Ghost of Data Governance Future

No sooner did Ebenezer begin to calm his nerves, when from the shadows of Ebenezer’s office, the Ghost of Data Governance Future appeared. Ebenezer’s pulse quickened as he did not want to acknowledge the ghost’s presence or what the ghost would ultimately share with him. It was with great reluctance that Ebenezer agreed to go with the final ghost.

Much to Ebenezer’s surprise, their travels did not take them very far into the upcoming decade before trouble surfaced. Poor Bob had suffered through another year and a half of cobbling reports together before reaching his saturation point and submitting his resignation. The remaining analysts scrambled to glean what they could from the frazzled, empty shell that was once the brightest among them. Surely a lone analyst would not beckon the fall of a Fortune 500 company, but it would begin the unraveling that followed in the months to come.

After Bob left the company, the remaining analysts fought to produce the fiscal reports on time. The data fixes that Bob orchestrated in the various systems to reconcile the reports were not being done. Some analysts were entertaining the idea of bringing in an outside firm to try and help address the problems, but IT was adamant they could shore up the problems on their end. Slowly the company’s competitive advantage began to erode and confidence on Wall Street went with it. It wasn’t long before rumors of bankruptcy began circulating. By then, Ebenezer had seen enough.

He couldn’t stand to see the company he so proudly guided through the first decade of the new century crumble before his eyes. He asked the Ghost of Data Governance Future if it was too late to turn things around. And if he promised to acknowledge all the data management, quality, integration, and governance issues that had been brought to light, would the company be able to avoid an inevitable collapse? But the ghost made no promises and offered no reassurances that the company could be saved. He simply returned Ebenezer to his office and quietly retreated back into the shadows of the office.

Ebenezer Wakes Up

Ebenezer, startled awake by the sensation of falling, quickly realized that he was still seated in his office chair. However, the relief was short-lived as he wiped the sleep from his eyes, only to see that the once-organized stacks of paper on his desk were strewn across the floor. Dream or no dream, the gravity of the situation was beginning to weigh on him:  The single most vital asset neglected for the past decade would be responsible for saving or crippling Ebenezer’s company.

There would be no quick solution. In order to build the momentum necessary to stay the course, Ebenezer needed to identify projects that would make an impact with the right sponsors who would, in turn, spread the word.

Slowly, over the course of several quarters, Ebenezer began to see the results of his mission to properly manage his company’s data as an asset. There were policies in place that supported the data governance initiative, and there were regular meetings to review the company’s progress with regards to an industry-approved maturity model. Data quality was being driven back to the source systems whenever possible.

There were formalized processes in place to deal with exceptions. There were master data hubs that dealt with integrating the various sources of master data across the company.  Data was beginning to move with the precision of a fine time piece.

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system. He also believes in Christmas, and he'll gladly give you a shilling to buy the prize turkey for Christmas  day.

Did You Forget Something? Gaps in Execution in Data Governance Programs

By Carol Newcomb, Senior Consultant

Somewhere along the way to understanding the whole data governance thing, someone forgot to mention that data management really is central.  Did I say Mention?  I really meant Emphasize.  Urgently Emphasize.  Data management processes are critical to the establishment and maintenance of a data governance program.

The thing is, data governance is quite impossible without a fundamental data management process that provides tactical direction. For example, if you have a bunch of people running around acting as data stewards, asking people questions about metadata, source systems, definitions, use of data and implications of sharing it, what happens when they have to write a policy?  And when data stewards meet as a group, how can they describe the magnitude of issues to discuss with their users?  How do issues get prioritized?  How can anybody decide who is accountable if due diligence hasn’t been paid to the fundamental source data issues, and how can decisions about governance enforcement be made?

Data governance should never be undertaken as a project.  It is a long term program that needs to start with a foothold in core data problems, like the inability to use or share data.  To get that foothold, stewards need to identify data quality and consistency issues that resonate with end-users.  Find some tangible pain.  This requires a sound set of data quality profiling activities to prove where conflicts or inconsistencies are most problematic, and a quantifiable statement of what the impact is.  The problem needs to be quantifiable to get people’s attention, as well as to compare one set of problems against another.  The data management program should have the capacity to routinely analyze and reveal data quality issues,and keep bringing them back to end-users to build a sense of urgency towards taking restorative and preventive action. 

Data governance is all about People, Processes, Decision Rights and Controls.  But before you can even begin, you will need to have identified the problems through hardcore data management practices.  This involves understanding the data architecture, examining privacy and compliance mandates around data visibility and sharing, exploring metadata definitions and having the ability to make changes through sound data administration practices.  Without these systems and roles in place, data governance is just a hollow shell.

photo by limaoscarjuliet (via Flickr)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

Who’s on first when it comes to integrating customer data into a single version of the truth…

By Steve Bebout, Senior Consultant

 

We’ve all had situations when we think, Does this company even know who I am? Why do I get contacted about products or services I have ‘opted’ out or for which I am not a viable candidate?
Additionally, when I specifically request information, why can’t they provide me with that information?  Addressing these issues is complex but at the crux of why a single version of truth is worth investing in.

I was recently involved in a project where each line of business (LOB) was positioned against the other from a revenue standpoint.  Each LOB was rewarded by creating the largest amount of gross revenue for the company which made information sharing not a top priority.  It wasn’t a situation where they didn’t see that customer data was an asset; however the value of that asset was directly tied to the LOB’s profitability.  Senior management saw this as a problem and realized the opportunity cost associated with multiple versions of the truth.

Without coming to an agreement on a single version of the truth for customer, they would never know "Who’s on First" and how to maximize "who's" lifetime value.  Why am I using "Who’s on First?" as an analogy?  Well, it’s an Abbot and Costello routine that portrays confusion around a person’s identity.  In this parody, even with the best of intentions, they are talking past each other.  Their disagreements reveal their lack of understanding of "who" really is, what he does, and what his value is…

After creating agreement on the enterprise data definitions, senior management provided us with future business goals that we used to create key business drivers for the company.  The key business drivers were:

• Create efficient data accessibility and ensure data quality across the enterprise
• Measure the effectiveness of marketing activity
• Understand behavior for more targeted cross-sell and up-sell
• Share integrated, cleansed, and enriched customer data

Alignment was key to developing support and creating a new cultural view of data sharing.  Aligning analytical processes, key functional capabilities, and subject areas supporting customer behavior to business drivers created prioritization and a road map for creating a single version of truth.  How do you support your assumptions to the client?  We created traceability examples for the key business drivers mentioned above.

By providing the company with basic examples of how bringing together the customer data from the LOBs disparate sources into a single centralized data repository can provide value opened their eyes and was a key way to get the enterprise ‘onboard’ to create a single version of the truth.

I found traceability matrices to be a valuable tool for resolving the multiple versions of the truth in identifying ‘who’, has anyone else used this approach or have other techniques that they have found been successful?

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Steve Bebout is a consultant with over 15 years of experience in information technology in the areas of application development, business requirements, data analysis, and project management.  He has experience in the Communications Media and Entertainment, Financial Services, Life Sciences, and Insurance vertical markets; spending the majority of his efforts on customer data integration initiatives. 

Ready, Shoot, Aim: Gaining Control of Your Data

By Carol Newcomb, Senior Consultant

You’re already familiar with the common symptoms: rapid heart rate, shallow breathing, sweaty palms and jumpy behavior.  When you are asked to identify the strategy for information management in your organization, your mind draws a blank.  “So, I’m not the CIO.  Go ask him!”  When asked about the most reliable source for master data on customers or contracts or sales force, you mumble, “Try the ERP system.  That’s where I found it last time.”  Or if an outside contractor were to ask about your 5-year plan for information management and BI strategy, you shrug, puzzled.  “What good is a 5-year plan when I don’t even have a 1-year plan?  Leave me alone.”  Treading water is the mode of survival.

The single most common problem organizations face these days is too much data.  It comes from all directions for all sorts of purposes.  It comes from dozens of applications designed to fulfill specific functions.  It has grown over the years until reluctantly, one of the organization’s leaders says “Enough already!”   Data is now costing more to manage than it is contributing to informed decisions.  Data is not an asset, it’s a liability!  How did this happen?  It’s like a teenager’s fantasy: wanting and having, wanting and having, bright shiny objects, more and more, until the sobering realization sinks in that the dream is no longer just a dream—it’s a full-fledged nightmare! 

Information management is all about control.  Stop.  Step back.  Get some perspective.  Assess the true situation. Itemize what’s good and what’s subpar.  Think about what the organization needs to achieve.  Think about how the data supports those objectives, not how the organization manages despite its data.  Data management is a deliberate, stepwise set of practices designed to ensure that value can be extracted from the data, and that the overhead associated with poor quality, inconsistent definitions, unaligned source systems and discontinuous data management practices are orchestrated to work as a whole.  The objective is to deliver information value.

So how do you go about changing from ‘Ready, Shoot, Aim’ to ‘Ready, Aim Shoot’?  First, get ready.  Admit that survival is not a strategy.  Survival is survival, and it is getting more difficult to maintain your organization’s survival, much less its competitive edge.  Next, invest the time and resources in drawing that big ugly spaghetti diagram, an inventory of ALL the source applications, software systems, ETL points, staging systems, landing points and delivery channels.  Then, take a true inventory of the end-users.  This is critical, because this is where data begins to result in business value.  Take time to really research who does what with certain pieces of data.  Do they further augment it with external data?  Do they merge it with other internal or external data?  Do they modify it to match other data sources?  How much time do they spend cleaning, standardizing or massaging data?  Start to build your ROI equation.  Data Sources X Time X People = Business Information Cost.  How much is this really costing?  Where is the value?

As you learn more about how the business uses data, start to lay out a business value matrix (the other side of the ROI equation).  This will help prioritize your data management roadmap.  Estimate the amount of effort required to integrate each source against the perceived business value, and chart a business value/implementation effort matrix.  Consider drafting more than one, as this reflects perceptions more than actual fixed units.  Share this information with end users to validate it and socialize a plan.  Then start to lay out a tactical roadmap.  Aim!

Now you’re getting somewhere!  Having faced the ugly reality (Ready) and incorporated real-life facts from business end-users (Aim), you can start to craft your strategy to streamline and rationalize the multiple data lifecycles supported by your organization.   Shoot!  Prioritize the data sources and systems to begin with, and estimate the expected value.  Launch your governance, metadata, quality and delivery resources, and engage your business users each step of the way.  Deliver value and repeat.  Evaluate the process, refine, and keep going.  It’s a deliberate, engaged, transparent and proactive approach, and it’s the only way you will ever conquer those adolescent nightmares!

photo by Erik Charlton (via Flickr)

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

Get on Board the MDM Express

By Bob Wall, Senior Consultant

Imagine if you will you're the CIO of a mid-market pharmaceutical company and you've just been to a TDWI conference that had a session (“Introduction to MDM for BI Professionals”) that provides an overview of master data management. You’re heading back to your corporate headquarters by train. You feel convinced that like this train, MDM is speeding along gathering momentum, and you don’t want to be left at the platform and miss the opportunity to get your company on the MDM Express!

You may have heard that MDM offers a single version of the truth, enterprise-wide, about customers, products, and other information types. As CIO, if you look around at your company’s issues you realize right off the bat that customer data integration is a real issue. You think to yourself that it would be nice to be able to take an MDM strategy and implement it to consolidate, integrate and synchronize all of the customer data so that it would be more accurate and it would enable your sales and marketing teams to better leverage the market value of your customer relationships. However, you're faced with the daunting fact that MDM is a very complex concept—and potentially an expensive one. Moreover, many MDM vendors price their products such that they’re out of reach for the small to medium size company.

How do you get started?  Here are four proven starting-points for MDM:

1. Find the business sponsor. I know it’s trite and overused, but since you’re the CIO it’s likely you know where the pain is among your C-level colleagues. Enlist the CFO whose numbers don’t reconcile across billing and invoicing systems, or the CMO who struggles with a growing number of customer lists.
2. Engage a readiness assessment. You can do this with internal or external resources, but do it. Gauging readiness has little to do with whether people know what MDM is and more to do with your incumbent skills, data management practices, toolsets, and needs.
3. Define a Small, Controlled Project. Work with the sponsor and cut a slice out of the overall problem that you can use to not only demonstrate the value of MDM, but establish the technology foundation.
4. Build the team. You can’t just “assign” MDM to your BI or CRM team. You need to construct a new team of data-savvy practitioners who will own enterprise master data for the long-term.

Sure, there are other deliberate steps. Maybe you want to do an educational workshop to get everyone on the same page, or talk to some incumbent vendors about their offerings. But these tactics and others could backfire if they are ill-timed. Better to think globally but act locally, and reap the rewards.

photo by jurvetson (via Flickr)

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Bob Wall is a senior consultant with Baseline Consulting. He is an information technology specialist with 30 years experience in all areas of data warehouse administration, data architecture, data resource management, training, and applications systems development, as well as in corporate management.

Disruptive Technologies: Governance in Action

By Carol Newcomb, Senior Consultant

photo by roland

I recently consulted on a Customer Data Governance program implementation project.  I must say, most companies get to the point where they design to death what governance should look like, how it should operate, who should be on which committees and who should have specific decision rights.  But how often have you ever seen data governance work well?  What does a governance policy even LOOK like?
Yeah, yeah, governance is the “4 pillars of People, Processes, Decisions Rights and Controls”, or it’s the “5 boxes of Alignment, Oversight, Measurability, Visibility, and Transparency”, but come on—what does it FEEL like?  How does it really WORK??  What makes it happen??  How can people understand it when they are told it is so important?  Who does what?

Face it.  Governance is disruptive.  We speak of disruptive technologies, like the cell phone or FaceBook, the Hybrid automobile, or the laptop computer.  These were all disruptive in their day.  They changed the way we think about social networks, distances between people and getting around.  Governance is disruptive because it seizes control from the hands (silos) of data analysts, IT staff and report writers, and puts it squarely into the laps of business people who (unknowingly) need the data to make accurate and timely business decisions.  Oh, and did we mention—share the data with each other?  Just think of the power of sharing trusted data and how much more efficient and effective the business would become with strong dependencies instead of weak, redundant independencies.

“Don’t put those decisions in MY LAP.”  We hear this all the time.  Why not?  It’s your bread and butter.  Stand up and be accountable for the fact that you don’t trust the data, but you still report it up the chain of command.  Show that you’re willing to expose who made what data decision, when, and how broad the impacts on actually are.  Be proud of the data that you use to run your portion of the business.    This is uncomfortable stuff.  Trust?  You want me to TRUST the data you lob over the fence? 
Well, let me think about that and I’ll get back to you.

Governance is disruptive because it breaks down the weaknesses we have that permeate most corporations, no matter what industry you’re in.  It forces people to talk to each other.  It forces people whom we call Data Stewards, to beat the bushes, get people to admit what they’ve been doing and how they’ve been doing it.  It wrests the decisions out of the IT cubicle and puts it squarely on the Director’s desk.  Data quality?  Let me show you our data quality.  Ouch.  Metadata?  How do you spell that?

So, get used to the fact that all change is disruptive.  But, the changes that are REALLY disruptive are those that break down the social and cultural barriers (aka personal barriers) that corporations have allowed to take control of their data, weakened its use as a decision-making tool, and contributed to massive cash outlays in data correction, cleaning and rework just to manipulate data so that it can serve its ultimate purpose: to measure the effectiveness of decisions made and to guide more profitable decisions in the future.  It’s that simple.

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Carol Newcomb is a Senior Consultant with Baseline Consulting. She specializes in developing BI and data governance programs to drive competitive advantage and fact-based decision making. Carol has consulted for a variety of health care organizations, including Rush Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross Blue Shield Association and more. While working at the Joint Commission and Northwestern Memorial Hospital, she designed and conducted scientific research projects and contributed to statistical analyses.

7 Ways to Cover Your Asset

By Rob Paller, Consultant

If you have attended a conference on data warehousing, business intelligence, data management, or data governance recently you ultimately have heard the saying, “Treat your data as a corporate asset”, countless times. Treating your data as a corporate asset means investing in data comparable to other corporate assets in terms of resources and technology. It also means that you understand the cost of poor quality, inaccurate, or missing data.

We have belabored the point that you should invest time and money into data integration technologies to assist you in mastering your master data elements. This is after you have invested the time, people and money to establish sound data governance policies. Nor does it discount the investment in the BI tools that can translate your data into information that can be used, understood and profitable.

What cannot be overstated is to the need to protect what is arguably your most important corporate asset from not only natural disasters and hardware failures but from both accidental deletion and intentional sabotage.  At the end of 2008, JournalSpace, an online company in business since 2002, was forced offline permanently because their entire database was overwritten as the result of a malicious act. What made the situation fatal was their reliance on the hardware redundancy of RAID alone to protect their database. In other words, they did not archive their data.

photo by wonderferret

Are you doing everything possible to cover your corporate asset?  Here are several questions to help you determine your own disaster readiness:

• Are you logging all queries submitted to your databases and archiving these logs daily?
• Have you established a disaster recovery process for your database systems?
• Have you performed periodic disaster recovery drills to make sure you are able to bring the affected system(s) back online?
• Are your archives maintained in separate location from your database systems?
• When an employee with administrator privileges leaves the company do you not only remove their user accounts but change the admin passwords on the systems administered by the employee?
• Do you maintain an audit log of users with the ability to purge production data and failed logon attempts for these users or admin users?
• Do you maintain a separation of duties and/or environments for testing ETL processes?

Are you able to sleep well tonight knowing how well your asset is covered? If not, follow the seven steps above and you’ll rest easy.

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Rob Paller is an expert at business analytics and database administration. Since joining Baseline, Rob has been responsible for developing a case analysis system to streamline the oversight of food assistance benefits, implementing a common citizen data model, and assisting in the rollout of a new public assistance data model integrating data from over 10 years of legacy with a new benefit eligibility determination system.

Finding the Gold in Enterprise Data

By Adam White, Senior Consultant

Without control of their data corporations can no longer compete. The emphasis on globalization, new competitive pressures, and the widespread adoption of the Internet have introduced a new sense of urgency around data integration and management.  These days a company must not only have control of its physical data, the actual information that it has captured, it must have the ability to derive information from that data, interpreting it accurately and leveraging it for operational efficiencies and strategic advantage. And it must be able to deploy enterprise data across business users, including call center agents, point of sale staff, sales representatives, managers, directors, and executives. The data may also be required for direct use by customers and/or prospects.

Corporate control of data extends beyond just using data. Data management is also an emerging discipline in most companies. Data must be protected. There are regulatory requirements, customer privacy expectations, and the need to securethe customer base from competitors.  The unforeseen forces of a global economy mean that companies must be able to work with data that is not English. And, of course, the need to integrate the data across various external and internal silos means that point-to-point interfaces must be phased out in favor of a single version of the truth.

Indeed, many of your company’s databases are replicated to provide access to various constituencies. But the point-to-point interfaces they use to exchange data are costly both development and maintenance-wise. Control over heterogeneous data—not to mention sustained management and integration of that data—is next to impossible.  Application upgrades and data enhancements mean large, expensive projects that come with a high risk of failure. A deliberate approach to enterprise data integration can help IT save time, headcount, and rework, as well as launching efficiencies that can not only drive down costs—ensuring responsiveness, repeatable development practices, and data reuse—but, more importantly, delivering value to the business.

So what are the approaches that will give you control over your data and provide the functionality that you need to support your business?  There are different options for integration (the sharing of data and services), including: Extract, Transformation, and Loading (ETL); Enterprise Application Integration (EAI); Enterprise Information Integration (EII); and Customer Data Integration (CDI). The optimal solution at your company may be one of these, or a combination of all four. The point is that decisions about data integration remedies should involve a complete understanding of the differences of data integration solutions, and a deliberate technology selection process.  Also, the implementation of the solution means that you will have to have a robust data governance and data management framework in place so that data integration supports your needs going forward, and doesn’t over time revert back into the original legacy architecture, losing the capabilities that are instrumental in implementing the solution in the first place.

Note: Baseline Partner Evan Levy will be teaching a class at the TDWI World Conference in Las Vegas next month, titled Architectural Options for Data Integration, in which he will outline the differences in these and other integration approaches, compare the strengths and weaknesses of each, and offer lists of vendors in each category. I strongly recommend attending!

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Adam White is an expert in Customer Data Integration (CDI), Services Oriented Architecture (SOA), Systems Development Life Cycle Methodology, Project Management, IT Governance, and establishing IT Process and Organization. Since joining Baseline, Adam has been responsible for conducting enterprise CDI assessments, implementing CDI, implementing SOA architecture, and implementing development methodologies.

One Problem, Three Parts: Making MDM Work at a Bank

By Frank Dravis, Senior Consultant

We have a banking client that is grappling with how to get a single view of their customers from the nine major types of systems they have and the myriad of subsystems. You’ll recognize those systems as you probably have them yourself: Billing, Product, Agent, G/L, Call Center, etc. When you first approach the problem there is a tendency to think, “Oh, this is a data warehouse problem. The bank wants to get a 360 degree view of their customers.”

But the problem is that our client’s challenges and issues go deeper. Yes, a data warehouse would achieve one objective of their goals, getting customer data from heterogeneous systems loaded onto a common platform, but the problem is more multidimensional. 

A main component of the solution is customer master data management, otherwise known as Customer Data Integration (CDI). CDI solutions automate the harmonization and integration of customer data from operational systems. Unlike a data warehouse, CDI guarantees that the data is cleansed and that business rules are applied before the customer data is stored on the CDI hub.

However, for the CDI system to function effectively it cannot do so in isolation. The rules it needs to apply should be defined. Enter data management. Data management is the practice of implementing rules and policies on data.  It is all about building systems and processes  to ensure consistent application of data quality, data integration including ETL and federation, metadata management, security, access, etc. Moreover, data management establishes the framework and culture for the original source systems to turn into target systems when the CDI solution serves as the system of record or reference and sends data back. Yes, for those of you who have implemented a successful MDM system, you have some form of functioning data management effort.

But unless senior management at the bank is willing to engage in sanctioning the right business rules, security and access policies, and corporate definitions, neither CDI nor data management will work. Thus data governance is a critical component of the bank’s long term data integration success. Understand, that data governance comes in big and little forms, such as the more operational policy of “the parts data mart shall attain a quality score of 82% in within the next six months.” At the bank, data governance policies serve as crucial guidance to business, IT, and their data stewards. 

So the next time you see one of those star-like diagrams with disparate data sources all feeding towards the customer center, consider there is a multi-dimensional view of that diagram: MDM, data management, and data governance.

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Frank Dravis is a senior consultant with 21 years of experience in enterprise information management (EIM) and data quality solutions design, implementation, and consulting. He specializes in data integration, data quality, and data governance solutions, advising key clients and industry vendors.

I've Never Metadata I Didn't Like

By Stephen Putman, Senior Consultant

The prospect of implementing metadata in most organizations is somewhat daunting. The types of metadata that the typical enterprise system produces are quite varied, and the value of collecting and reporting it is not immediately obvious. Many times these programs run out of steam before they even really get out of the starting gate because it is difficult to know how to start collecting this data.

I recently experienced this sort of issue at a large financial services client. The staff understood the basic value of metadata collection – end-user documentation, elimination of data element duplication, and data traceability through their process – but had a hard time getting started. I explained to them that as business analysts they already had the tools and experience to gather the metadata they needed, and were already doing it! The subject of requirements documents and mapping spreadsheets is quite familiar to business analysts, so the key to pitching an ongoing metadata process is to show that the techniques used to collect data for these documents are exactly the same as the techniques used to create a sustained metadata management system, just in a different format.

I illustrated this by citing examples from a human resources outsourcing project that was in progress at the time. I took the data mapping spreadsheets that were being used and translated them into a homegrown but industry-standard database system. By doing this, I demonstrated that data could be used to produce an ad hoc reporting capability to produce end-user data dictionary documentation, data lineage reports, and reference material for other business analysis activities. This is accomplished by standard database query techniques, which is much easier than extracting data from spreadsheets and other standalone documentation.

Convincing business people and end users that a metadata management system is worthwhile involves two main factors, neither of which involves the tools themselves: (1) the realization that the analysts are already doing the collection work, and (2) the idea that a centralized metadata repository can be leveraged across business requirements and applications to deliver common data definitions and rules. Implementing these two concepts puts you well on your way to a successful metadata management system—and evolving metadata maturity!

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Stephen Putman has over 20 years experience supporting client/server and internet-based operations from small offices to major corporations.  He has extensive experience in a variety of front-end development tools, as well as relational database design and administration, and is extremely effective in project management and leadership roles.