From Innovations, a website published by Ziff-Davis Enterprise from mid-2006 to mid-2009. Reprinted by permission.

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.

From Innovations, a website published by Ziff-Davis Enterprise from mid-2006 to mid-2009. Reprinted by permission.

Over the last couple of entries, I’ve talked about the benefits of deploying social network technology internally for business benefit. While the benefits may be clear to you, chances are you have to get past the sticky process of selling the idea to management, who invariably ask the return on investment (ROI) question.

ROI is important but it’s also a smokescreen that executives can use to avoid spending money. Corporations invest in plenty of functions that have vague ROI, including human resources and training. Unfortunately, social networking projects don’t lend themselves to clean ROI calculations. The benefits of better communications, more productive collaboration and access to organizational knowledge don’t fit neatly into a spreadsheet.

If you’re stuck on ROI, point to the often-neglected “I” part of the equation: investment. The new of tools, many of which are open source, are often significantly less expensive than the proprietary programs they replace. Then ask managers to consider the cost of a sales opportunity lost because team members didn’t have access to relevant knowledge within the organization. Or look at turnover rates and ask what the value is of capturing the knowledge that those departing workers take with them.

The best way to justify social media investments is through examples. Every large organization has a few people who understand the transformative effects of technology and who are eager to try new things. Embrace these people as your allies, particularly if their organizations produce revenue. Enlist them to participate in a pilot project that applies social networking concepts and technologies to an existing process. Perhaps it’s replacing an over-engineered project management system with a wiki. Or maybe it’s adopting podcasts in lieu of staff-wide conference calls. Offer all the support you can to make the pilot work. The experiences of early adopters wills rapidly spread throughout the organization.

One key to successful adoption of any new technology is to eliminate alternatives. If people are told to use a wiki for collaboration, for example, but still have the option of communicating with colleagues by e-mail, they will choose the latter. People default to the path of least resistance, and that usually means sticking with what they know. You have to remove that option to demonstrate the effectiveness of an alternative solution. You can’t eliminate alternatives, but you’re sponsor can. That’s why choosing allies at the management level is so important.

A successful pilot project is a foundation for revisiting the ROI question. Interview project participants to document their impressions. Identify what they believe are the cost savings or revenue opportunities. Share these field experiences with the skeptics. Real-life stories are more convincing than formulas.

Supplement this with an internal public relations effort. Subscribe to RSS feeds of bloggers and publications that advocate for your position. Share expert opinions and published case studies with your internal decision-makers. Keep an eye out, in particular, for activities by your competitors. Don’t expect results overnight, but with time and repetition, you can wear down the critics.

From Innovations, a website published by Ziff-Davis Enterprise from mid-2006 to mid-2009. Reprinted by permission.

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.