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.

The Solution Maturity Cycle

I saw the news about Informatica’s acquisition of Identity Systems, and it got me thinking. I recognize a familiar pattern that all too often occurs in the enterprise software business. I’m going to call it the Solution Maturity Cycle. It goes something like this:

1. The Emergence Phase: A young, fledgling company emerges that provides an excellent product that fills a need in the industry. This was Informatica in the 90’s. Rather than hand coding a system of metadata management, companies could use a cool graphical user interface to get the job done. Customers were happy. Informatica became a success. Life was good.

2. The Mashup Phase: Customers begin to realize that if they mash up the features of say, an ETL tool and a data quality tool, they can reap huge benefit for their companies. Eventually, the companies see the benefit of working together, and even begin to talk to prospective customers together. This was Informatica in 2003-5, working with FirstLogic and Trillium Software. Customers could decide which solution to use. Customers were happy that they could mashup, and happy that others had found success in doing so.

3. The Market Consolidation Phase: Under pressure from stockholders to increase revenue, the company looks to buy a solution in order to sell it in-house. The pressure also comes from industry analysts, who if they’re doing their job properly, interpret the mashup as a hole in the product. Unfortunately, the established and proven technology companies are too expensive to buy, so the company looks to a young, fledgling data quality company. The decision on which company to buy is more influenced by bean counters than technologists. Even if there are limitations on the fledgling’s technology, the sales force pushes hard to eliminate mashup implementations, so that annual maintenance revenue will be recognized. This is what happened with Informatica and Similarity Systems in my opinion. Early adopters are confused by this and fearful that their mashup might not be supported. Some customers fight to keep their mashups, some yield to the pressure and install the new solution.

4. Buy and Grow Phase: When bean counters select technology to support the solution, they usually get some product synergies wrong. Sure, the acquisition works from a revenue-generating perspective, but from the technology solution perspective, it is limited. The customers are at the same time under pressure from the mega-vendors, who want to own the whole enterprise. What to do? Buy more technology. It’ll fill the holes, keep the mega-vendor wolves at bay, and build more revenue.

The Solution Maturity Cycle is something that we all must pay attention to when dealing with vendors. For example, I’m seeing phase 3 this cycle occur in the SAP world, where SAP’s acquisition of Business Objects dropped several data quality solutions in SAP’s lap. Now despite the many successful mashups of Trillium Software and SAP, customers are being shown other solutions from the acquisition. All along, history makes me question whether an ERP vendor will be committed long term to the data quality market.

After a merger occurs, a critical decision point comes to customers. Should a customer resist pulling out mashups, or should you try to unify the solution under one vendor? It's a tough decision. The decision may affect internal IT teams, causing conflict between those who have been working on the mashup versus the mega-vendor team. In making this decision, there are a couple of key questions to ask:

• Is the newly acquired technology in the vendor’s core competency?
• Is the vendor committed to interoperability with other enterprise applications, or just their own? How will this affect your efforts for an enterprise-wide data governance program?
• Is the vendor committed to continual improvement this part of the solution?
• How big is the development team and how many people has the vendor hired from the purchased company? (Take names.)
• Can the vendor prove that taking out a successful solution to put in a new one will make you more successful?
• Are there any competing solutions within the vendor’s own company, poised to become the standard?
• Who has been successful with this solution, and do they have the same challenges that I have?

As customers of enterprise applications, we should be aware of history and the Solution Maturity Cycle.

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.

Data Governance in a Recession

What effect will a recession have on your data governance projects? Some have predicted that the nation will fall into a recession in 2008, although others disagree. In other words, it depends on whom you believe as to our economic fate in 2008. Still, with even the hint that a recession is pending, companies often move to cut costs. These cuts tend to affect major IT initiatives like data governance.

For those of us in the IT and enterprise software business, CFO-thinking runs counter to logic. During revenue-generating high times, IT tends to spend money to deliver automation that either cuts costs and/or improves productivity. So, the money spent delivers something back. However, during tougher economic times, or even when those times are presumed to be around the corner, cost cutting will be on the forefront, preventing us from fixing the inefficiencies. When revenues are good, efficiencies can be made better through IT. When revenues are bad, efficiencies are thrown out the window.

Talk of a recession may slide your plans for big projects like master data management and data governance onto the back burner. Instead, you may be asked to be more tactical – solving problems at a project level rather than an enterprise level. Instead of setting strategy, you may be asked to migrate a database, cleanse a list for a customer mailing, etc. without devoting resources to establishing a clear corporate strategy.

The good news is that times will get better. If and when there is a recession, we most certainly DON’T want to have to rewire and re-do our efforts later on. If you are asked to become more tactical, there are some things to keep in mind that’ll save you strategic frustration:

• Convince management that data governance will save money, despite the resources needed up-front. Any vendor worth their salt has case studies showing the return on investment and can help you make the case if you bring them into the process early.
• If you have to stay tactical, make sure the tools and tactics you choose on the project-based initiatives have a life in the strategic initiative. In other words, don’t cut costs on technology that won’t scale. Don’t choose tools that have limitations like lack of global support, poor connectivity, or limited performance if you’ll need those things later. Choosing these tools may hurt you when you want to go enterprise-wide; they’ll get into your plumbing and will be hard to replace. They’ll also get into the minds of your people, potentially requiring training and retraining. Even in tough economic times, you’re setting the standard when you select tools. Don’t let it come back to haunt you when times are good.
• Make sure you understand all the pieces you need to buy early in the process. Many enterprise vendors require a LOT of different packages to do real-time data quality, for example. Hidden costs can be particularly problematic.
• Make sure you understand all of the work involved, both in your project and in an enterprise implementation. There are big differences in the effort needed to get things done. Take the services effort into account during scoping.
• If cutbacks are severe but the need is still great, consider software leasing and SaaS (Software as a Service) to minimize costs. Many vendors offer their enterprise software as a service offering. If times are tough, work with the vendor on alternative ways to purchase.

On another note, I want to thank Beth from the ‘Confessions of a Database Geek’ blog for the mention of my blog this week. If you’re a blogger, you know that every mention by other bloggers gives you street cred, and I am most appreciative of that. It's great to be mentioned by one of the best. Thanks Beth!

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.

SAP Data Management Success Stories

I’m preparing for a web seminar on SAP data management success, and I’m really starting to look forward to it.

Moen and Oki Data will be sharing their data quality success stories with our audience. These are two very successful implementations of the Trillium Software System in the SAP environment.

My Trillium Software colleague, Laurie and I will take up only about ten minutes to first frame up and then wrap up the presentation. But the bulk of the presentation is about Moen and Oki Data and the success they’ve been able to achieve in a) quickly starting a data management program in SAP R/3, SAP ERP and SAP CRM; and b) taking the process and technology from one project to another.

If you want to join us, please click here. The webinar is on February 27th at 2 PM Eastern.

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.

Mainframe Computing and Information Quality

Looking for new ways to use the power of your mainframe? My friend Wally called me the other day and was talking about moving applications off the mainframe to the Unix platform and cleansing data during the migration. “Sure, we can help you with that.” I said. But he was surprised to hear that there is a version of the Trillium Software System that is optimized for the Mainframe (z/OS server). We’ve continually updated our mainframe data quality solution and we have no plans to stop.

Mainframe computers still play a central role in the daily operations of many large companies. Mainframes are designed to allow many simultaneous users and applications access to the same data without interfering with one other. Security, scalability, and reliability are key factors to the mainframe’s power in mission-critical applications. These applications typically include customer order processing, financial transactions, production and inventory control, payroll, and others.

While others have abandoned the mainframe platform, the Trillium Software System supports the z/OS (formerly known as OS/390) environment. Batch data standardization executes on either a 64-bit or 31-bit system. It also supports CICS, the transactional-based processing system designed for real time processing. z/OS and CICS easily support thousands of transactions per second, making it a very powerful data quality platform. The Trillium Software System can power your mainframe with an outstanding data quality engine, no matter if your data is stored in DB2, text files, COBOL copybooks, or XML.

The Trillium Software System will standardize, cleanse and match data using our proprietary rules engine. You can remove duplicates, ensure that your name and address data will mail properly, CASS certify data and more. It’s a great way to get your data ready for SOA on the mainframe, too.

My hats off to Clara C. on our development team, who heads up the project for maintaining the mainframe version of the Trillium Software System. She’s well-known at Trillium Software for her mainframe acumen and for hosting the annual pot-luck lunch around the holidays. (She makes an excellent mini hot dog in Jack Daniels sauce.)

I’m not sure whether Wally will stick with his mainframe or migrate the whole thing to UNIX servers, but he was happy to know he has an option. With an open data quality platform, like the Trillium Software System, it’s not a huge job to move the whole process from the mainframe to UNIX by leveraging the business rules developed on one platform and copying them to the other.

Oracle Data Integration Suite - Trillium Software Inside

Finally! Finally, I can talk about the exciting news regarding Trillium Software’s partnership with Oracle. It’s a perfect decision for Oracle to begin working with Trillium in the data integration market, combining Sunopsis technology with Trillium Software technology to address some of the competitive challenges of IBM and the Webshere platform.

Trillium Software has long been a supporter of the Oracle platform, first offering batch technology for cleansing Oracle databases. A few years ago, we began offering direct support for Oracle’s older data integrator, OWB. Now, this integration with ODI is going to serve Oracle customers with excellent data quality within a superb data integration platform.

Trillium Software prides itself in it’s our connectivity into major enterprise applications. Here are a few of the most popular ones:

• SAP - SAP R/3, SAP CRM, SAP ERP and SAP NetWeaver MDM.
• Oracle - OWB, ODI, Siebel eBusiness, Siebel UCM, Oracle CDH, and Oracle eBusiness Suite.
• Ab Initio
• Siperian

In addition, we still have quite a few customers on the Informatica platform, and we continue to support those customers, despite the fact that Informatica has had a competitive data quality solution since its acquisition of Similarity Systems. We even maintain our integration with IBM Websphere, despite IBM’s acquisition of Ascential, who had acquired data quality vendor Vality. Still, we have a significant number of users who are using Datastage with Trillium Software and don’t want to switch.

Why support all these integration points when other vendors don’t? It’s where the reality of the marketplace meets product development. Let’s face it, large companies most often don’t run a single application platform across their entire enterprise. Most have a mixture of IBM, Oracle, Siebel, and many other enterprise vendors. Sometimes, this makes perfect sense for the organization. The heterogeneous enterprise often occurs when the application vendors can’t meet all the needs of the organization. So, for example, SAP ERP may meets the need of manufacturing, but Siebel better meets the requirements of sales and marketing.

On the other hand, it makes sense to standardize the data platform of your company. If you can plug the same rules engine into any of these platforms, data quality is more easily a simple component of corporate governance. Now you don’t have to hire staff to operate and maintain multiple data quality tools. Now, you won’t have to try to tune one data quality tool to make it behave like another. It is much easier to achieve a company-wide gold customer master record with a single information quality platform like Trillium Software.

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 Quality and Being Green

It’s clear that the green movement, specifically the desire for the general public to want to work with companies who are environmentally responsible, is here to stay. The general public is overwhelmingly in favor of your efforts to be green. For example, Wal-mart made headlines when it recently announced a program to reduce greenhouse gas emissions. Not only was this positive news for the world, but Wal-mart saved money on reduced energy costs.

For this and other reasons, marketers are relying less and less on direct mail as a core channel to send targeted information to customers. Consumers' desire to be green are causing marketers and finance teams alike to rethink paper-based channels, increasing their reliance on electronic communications (eg, websites, email, and e-statements).

The green movement is changing the world of data management, as follows:

• De facto name and address standards – As we go into 2008, the general public simply won’t accept duplicates - bills, marketing offers and other mailings from you must be as clean as possible, or the customer is more apt to unsubscribe to your offers. In the past, if you got three catalogs from that computer retailer, it was a joke. They will laugh no more. Being green is a serious subject to many of your customers.
• Importance of non-name and address data – Sure, the customer name and address will still be an important, but additional information such as e-mail address, customer contact preferences, and whether the customer is on the “Do Not Call” list are fundamental. Build this type of data as you go forward with additional processes at the call center and sales level. In other words, if you want someone’s e-mail address, you should ask for it.
• Electronic Billing – As a customer, it sure is easier to get your bills via web site. As a company, it sure costs less to bill your customers via e-mail and secure web site. As an eco-friendly company, it sure looks good to provide a way to stop all those perfume soaked papers from being delivered by the mailman. Electronic billing is the way to go.
• Potential Higher Revenues – Let’s face it, the cost of direct mail is much higher than sending an e-mail blast. Within reason, you can afford to make more offers to your customers and increase upsell potential, as long as you’ve done your data management homework. A men’s clothing store e-mails me weekly about specials, and I’m happy to get the offers. As a customer, I have seen their evolution. In the past, I received barely one postcard per quarter. As a result of their switch to e-mail and the increased touches, I do buy more at the store.

Trillium Software can help you meet these data management challenges and become greener. How? Of course, the Trillium Software System helps remove the duplicates, but it can help understand and repair the quality of e-mails and contact preference data. In association with our parent company, Harte-Hanks with can often do reverse look-up on data, so if you have an address, Harte-Hanks can often find a phone number or an e-mail. We can help manage the “Do Not Call”By managing data more effectively; you can become a stronger, greener company.

More info on being green? Take a look at the DMA’s Green15 Toolkit.

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.