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Is Data Quality at the Heart of the Mortgage Crisis?

By NewVantage | September 22, 2008

By Paul Barth, Managing Partner, NewVantage Partners

A panelist on a news program I was listening to recently described the executives at Fannie and Freddie as “unintelligent” for not seeing the house of cards they were constructing with securitized mortgages.   We know many C-level financial service executives — I’m pretty sure intelligence was not the problem here.  What I do know is that intelligent people can’t make good decisions without good data, and that most financial services companies have problems with incomplete and inaccurate data.  Ask marketing, finance, or servicing executives about the quality of the data they receive from sales, and you’ll see their eyes roll.  Why? Because sales is not measured or incented on data quality, they are incented on closing deals, so they often enter duplicates or omit fields that are not related to closing.

Extrapolate 1 or 2 orders of magnitude, and you have banks and  originators packaging loans to get them off their books.  How clean and accurate is this data is at the start?  How important is data accuracy to the originator if the loan  is going out the door in a few months?  How much leverage does the buyer of a loan bundle have in a competitive market to negotiate clean, accurate data?  The problems get passed on down-and further obscured–after securitization and trading in the market.  I am pretty sure intelligent executives took on unintelligent risks because they did not have any way to assess the accuracy of the data about their underlying assets.

We often talk about the costs of data quality, which I had not thought would snowball to $700 billion in a month.  Perhaps this jolt will be the harbinger for funding the significant effort and cost for systemic improvements in data management—which, at the end of the day, is the foundation of real business intelligence.

Topics: customer data integration, enterprise data management, information asset management | No Comments »

The Evolution of CRM

By NewVantage | September 16, 2008

By Peter Kim, Senior Associate, NewVantage Partners

Recently, I was working with a client who wanted to understand where we were in the development of CRM and what the big milestones were.

I put this together to explain where I thought the big inflection points were.

Is innovation complete? I don’t think so. But there are currently no drivers, like how the internet created new channels, to drive innovation. The best we can hope to see is incremental improvements.

What does this mean for the CRM buyer? It means that most of the platforms left standing will all handle the basics and handle them well. You can now focus on your “unique” set of needs and characteristics when considering platforms. At this point, your choice of systems integrators will probably far outweigh the choice of platforms in determining how well the solution fits your needs. While this has probably always been true, the buyer at this point need not be distracted by the “shifting technology landscape.” It has mostly settled.

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Data Quality Problems are Corporate IT’s Dirty Little Secret

By NewVantage | September 3, 2008

By Paul Gillin, Author of The New Influencers, and former Editor-in-Chief for ComputerWorld and TechTarget

Republished from Innovations - A Supplement to Baseline, CIO Insight, and E-Week

www.innovations.ziffdavisenterprise.com/2008/07/data_quality_problems_are_corp.html

In the early days of home broadband, I was a customer of a very large cable company; whose name I’m sure you know. When making one of my frequent calls to the technical support organization, I was typically required to give all my account information to a representative who then transferred me to a support tech, who asked me to repeat all of the account information I just gave the first person.  If my problem was escalated, I got transferred to a third rep who would ask me for — you guessed it - my account information.

This company’s reputation for lousy customer service was so legendary that if you type its name and “customer service” into a search engine today, you get mostly hate sites.  One of its biggest problems was poor data integration.  For example, its sales database was so fragmented that I routinely received offers to sign up for the service, even though I was already a customer.  I’m a customer no longer.

Does this sound familiar?  Most of us and have had frustrating experiences of this kind.  Thanks to acquisitions, internal ownership squabbles and poor project management, most large companies have accumulated islands of disintegrated, loosely synchronized customer data.  PricewaterhouseCoopers’ 2001 Global Data Management survey found that 75 percent of large companies had significant problems as a result of defective data. It’s unlikely the situation has improved much since then. Data quality is corporate America’s dirty little secret.

The Path to Dis-Integration

There are several reasons for this, according to Paul Barth, an MIT computer science Ph.D. who’s also co-founder of NewVantage Partners. One is acquisitions.  As industries have consolidated, the survivors have accumulated scores of dissimilar record-keeping systems.  Even basic definitions don’t harmonize.  Barth tells of one financial services company that had six different definitions for the term “active customer.”

A second problem is internal project management.  The decentralization trend is pushing responsibility deeper into organizations.  There are many benefits to this, but data quality isn’t one of them.  When departmental managers launch new projects, they often don’t have the time or patience to wait for permission to access production records.  Instead, they create copies of that data to populate their applications. These then take on a life of their own. Synchronization is an afterthought, and the occasional extract, transform and load procedure doesn’t begin to repair the inconsistencies that develop over time.

Data entry is another problem.  With more customers entering their own data in online forms and fewer validity checks being performed in the background, the potential for error has grown.  Data validation is a tedious task to begin with and really effective quality checks tend to produce a lot of frustrating error messages.  E-commerce site owners sometimes decide it’s easier just to allow telephone numbers to be entered in the ZIP code field, for example, as long as it moves the customer through the transaction.

Finally, data ownership is a tricky internal issue. Many business owners would rather focus on great features than clean data.  If no one has responsibility for data quality, the task becomes an orphan.  The more bad data is captured, the harder it is to synchronize with the good stuff.  The problem gets worse and no one wants responsibility to clean up that mess.

Barth has a prescription for addressing these problems.  It isn’t fast or simple, but it also isn’t as difficult as you might think.  Next week we’ll look at an example of the benefits of good data quality and offer some of his advice for getting your data quality act together.

Topics: customer data integration, enterprise data management, information architecture | No Comments »

Quality Data Can Yield Amazing Insight

By NewVantage | September 3, 2008

By Paul Gillin, Author of The New Influencers, and former Editor-in-Chief of ComputerWorld and TechTarget

Republished from Innovations - A Supplement to Baseline, CIO Insight, and E Week

 www.innovations.ziffdavisenterprise.com/2008/07/quality_data_can_yield_amazing.html

NewVantage Partners co-founder Paul Barth likes to tell the story of a commercial wholesale bank that saw the opportunity to grow business and customer satisfaction by up-selling existing customers. Its sales force had traditionally been organized around product lines, but the bank knew that cross-selling was an unexploited opportunity. The question was what to sell to whom.

Sales management decided to pull all its customer information into one place to look for patterns. By targeting segments, the sales force could sell its products to the most likely buyers. Small businesses, for example, were good prospects for lines of credit.

Armed with these profiles, the bank retrained its sales force to sell a portfolio of products to different customer types.  The predictive data allowed reps to focus their energies on the customers who were most likely to buy. The result: sales to existing customers grew $200 million, product-per-customer ratios increased and customer satisfaction rose 50%.  All of this was achieved without expanding the product line.

Clean Data Yields Insight

This wasn’t an example of technology wizardry.  It was an example of clean, well-integrated data leading to a positive business outcome. By taking an integrated view of its customers, the bank was able to spot patterns and derive insights that led to smart business decisions.  Cost was minimal, upside was huge and the decisions resulted in a new annuity stream of increased business that lasted years beyond the original decision.

Data quality is an under-appreciated advantage.  For companies that apply the discipline and planning to address their data-quality needs and take an innovative approach to identifying new insights, the payoff is an order of magnitude of the cost.

Most large organizations have data quality problems that have resulted from years of acquisitions, stovepipe application development and error-prone data entry. Making sense of the mess looks like a huge job, but it can be done if you follow a disciplined process, suggests Barth, an MIT computer science Ph.D. who’s also co-founder of NewVantage Partners.

First, you need to step through all their critical data elements and decide what information is really strategic to the business. This process alone can winnow down tens of thousands of data elements to perhaps a few hundred.

Then establish consistent terminology. Barth cites one client that had six different definitions of “active customer.”  Everyone in the company needs to use the same language. A metadata repository can help standardize terms

“Start to centralize where data is stored, how it’s provisioned and how people access and use it,” Barth recommends. Minimize copies. Each copy of production data creates complexity and the possibility of error

Put in place a governance process for data quality that specifies rules about what levels of quality is acceptable. Create metrics by which to measure quality levels. Establish data ownership. One of the reasons companies have so many data problems is that no one owns the quality process. Ownership creates responsibility and discipline

Get a handle on application development. Line-of-business owners shouldn’t be allowed to create rogue applications without central coordination. Many of these skunkworks projects use copied data because it’s more expedient than tapping into production databases

Identify opportunities to create insights from data. This is the most critical step. Strategic opportunity comes from thinking up innovative ways to apply data analysis.

Annuity Benefits

Here’s one more example of the benefits of data quality. An acquisitive bank had to make decisions about closing and consolidating branches. These choices are usually made based on location, but some analytical thinkers at the bank had a better idea.  They built a database of customer transactions across the bank’s many channels, including branches, telephone banking and Web.  Then they looked at the behavior of customers across those profiles

They discovered that customers who used multiple physical and electronic channels were less likely to leave the bank. That meant that branches that counted many of those high-value customers were actually better candidates for closure. Using this innovative approach to decision-making, the bank was able to close 50 branches and save $38 million annually without any revenue impact. That’s an annuity benefit that resulted from an innovative analysis of data the bank already had.

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NewVantage CIO/Executive Thought-Leadership Dinner Held in New York on August 5

By NewVantage | September 3, 2008

The most recent NewVantage Partners CIO and Executive Thought-Leadership Roundtable Dinner was held on Tuesday August 5 at Bouley in New York. Participants included:

• Lynda Applegate - Henry Byers Professor of Business Administration at Harvard Business School, and noted author on IT innovation
• John Fiore - CIO at Mellon Bank of New York and former CIO of State Street Bank
• Tom Sanzone - Chief Administrative Officer for Merrill Lynch and former CIO at Credit Suisse and CitiGroup
• Steve Sheinheit - recently retired CIO of MetLife and former CIO at Chase Manhattan Bank
• Joe Simon - CIO of Viacom
• Joe Smialowski - recently retired EVP/Technology and Operations at Freddie Mac and former CIO/Vice Chairman at Fleet Bank (Bank of America) and CIO of Sears
• Mel Taub - former Corporate CTO at CitiGroup and former CIO at Smith Barney
• Paul Zazzera - former CIO of Time Inc and chair of the Time-Warner CIO Council.

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Information as a Strategic Asset

By NewVantage | July 29, 2008

By Paul Barth and Randy Bean, Managing Partners, NewVantage Partners

The idea that information can be a competitive weapon is not new. Yet paradoxically, many organizations today may actually have less control over their information than ever before. Let’s look at why this predicament exists and what CIO’s and business line executives can do about it.

Proliferation of Data

One of the challenge facing businesses and CIO’s is a crushing data quality problem.  Typical of this problem is the example of the CFO who receives financial reports from sales and operations that don’t reconcile. This is often because the legacy systems and databases that were developed during the 1980s and 90s are not integrated with new systems and databases resulting from mergers, acquisitions, new products, and new information sources, including web applications.  Data integration and data consistency is often minimal or non existent.  Data lineage is often undetectable.  Without reliable information, business executives are hampered in their ability to make informed, fact-based decisions.

Data quality problems are a chronic and worsening issue in corporate America today.  PriceWaterhouseCoopers has reported that 75% of Fortune 500 companies have significant problems because of disparate data.

This has been borne out in our own experience advising many leading Fortune 1000 companies. One firm conducted an audit and discovered that it had 500 Microsoft Access databases in use, each an island unto itself. Another client found that it was processing 28,000 database extracts each night. These copies were being disseminated across the organization without thought to synchronization or control.

Business Impact of Bad Data

What is the impact of bad data?   Information quality problems that led to financial restatements cost one public company hundreds of millions of dollars in lost market capitalization.  In another case, a large telecommunications firms was able to identify tens of millions in “revenue leakage” attributable to poor data management.

What is the benefit of correct and actionable information? A leading national bank was able to realize tens of millions of dollars in annual savings by making intelligent consolidation decisions based upon integrated customer data.  Another financial services firm used analytics to profile its customers and identify cross-selling opportunities that resulted in several hundred million dollars in additional annual revenues without the need for new products.

These examples illustrate the dramatic impact of data quality on business operations.  But data quality impacts the customer as well.

Most of us are familiar with the experience of navigating through a customer service organization, only to be asked to repeat the same information by each representative.  This frustrating experience occurs because different parts of the organization each believe that they “own” the customer.  Often, these operations often have their own software applications and their own copies are of customer data.  Databases are populated via extracts from the central data store, and those copies quickly get out of synch with each other. No one has responsibility for data quality or it is treated as an afterthought.  Over time, these inconsistencies can become dramatic, resulting in dissatisfied customers and loss of business.

Ironically, the proliferation of new business applications has only made this problem worse.  As business units bring in new systems and solutions to address point problems or opportunities, they may also create more data extracts and greater potential for inconsistency.  In an unintended way, technology may actually be contributing to the problem of data proliferation and inconsistency.    

Chief Intelligence Officer?

Addressing these issues doesn’t always require new technology or significant new investment.  Improvement begins with the recognition that data is a strategic asset.  Organizational alignment and the establishment of business processes that ensure data consistency and integrity on a going forward basis are a necessary first step.

CIO’s have a vested interest in helping their companies recognize that information is a strategic asset, like other critical business assets.  For CIO’s who have often expressed the desire for a “seat at the table” in formulating business strategy, the opportunity to take the lead in helping organizations develop an information strategy represents a path from the operational role of “Chief Infrastructure Officer” to the business role of “Chief Intelligence Officer”.

Our series of upcoming postings will explore in greater depth some of the aspects of this topic, including: Data Proliferation, Information Entropy, Data Lineage, Enterprise Data Management, and the Role of the CIO as Chief Intelligence Officer.   

Topics: CIO, customer data integration, customer relationship management, enterprise data management, information architecture, information asset management | No Comments »