Corporate concern
- Article 5 of 6
- Database Marketing, September 2006
Software choice is widening as corporate awareness of data quality rises and demand for customer data management software increases. Robert Buckley discusses what functions are needed to extend data cleansing across the enterprise.
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Hopewiser’s national sales manager Mark Dewell says that it’s had its feet in both camps for some time. “The prime users of Atlas tend to be marketing or IT. We tend to integrate into other business processes that deal with customer and prospect data.” The difference is in how the software fits into the organisation. “With marketing, with very conscious of Windows and GUIs. We provide set-up wizards. But while IT sometimes like GUIs, they prefer the functions that integrate it into a larger business. They use the API route, starting at the engine level. We’re providing IT with the flexibility for integrated workflow.”
Similarly, Hans Ruigrok, VP sales and marketing of Human Inference, says his company’s products have appeal for the larger organisation that transcends the marketing department. “In marketing, they tend to use the software as a stand-alone tool. But we can be implemented as a business service that can be called from every application.”
While name and address cleaning are the most well known DQ issues, corporate data quality requires other cleansing. Phone numbers, national insurance numbers, social security numbers of US residents and personal information of customers, prospects and employees might require validation; supplier and product information might need cleansing; customer lists could require checking against lists of known terrorists and money launderers. There may be fields that only have relevance to an individual organisation that need cleaning according to its rules.
Additionally, Human Inference’s Ruigrok says the software’s multi-lingual capabilities make it of particular interest to multi-national companies that want to standardise on a single tool throughout its different offices. Knowing how to work with individual countries’ different datasets and languages, which can each present unique problems and misspellings, is one of its main appeals.
Nevertheless, it’s the ability to be integrated with other applications that really makes or break a marketing application that has ambitions of being used by the rest of the enterprise. Smaller, more specific tools that can be integrated into larger applications, for instance, are also doing well.
Arc En Ciel’s data cleansing tool is integrated into various applications at the point of data capture. “Almost all estate agents use our software,” says technical manager Tony Reynolds.
“I never cease to be surprised where our products get to,” says David Dorricott, managing director of AFD. “Through the use of our API tools and through access to our cleaning engine, there’s the option to build both in enterprise systems.” If anything, says Dorricott the rest of the organisation tends to be more demanding in its requirements of data cleansing tools than marketing.
To be able to integrate with other business applications requires functions that many marketing applications do not have. As Hopewiser’s Dewell points out, being able to run more or less unattended and without a graphical interface is a necessary first step. Bureau in a Box’s Cygnus, while excellent for marketing work and seeming some interest from the rest of the organisation, will need further development work before it will truly appeal, says managing director Mark Dobson.
“At the moment, we’re a one-product company, about to embark on an 18-month development of some new products. There are certain limitations [to Cygnus] that might not fit with the rest of the organisation. It won’t run lights-out so it doesn’t interrupt activities. It’s also not client-server.”
Running “lights out” presents some obstacles that GUI-based apps don’t. While marketing can often have far looser criteria when dealing with names and addresses, operational data requires far more precise matching. This will either lead to more exceptions that require manual intervention or requires far more sophisticated matching algorithms and datasets. The formercosts far more in both time and wages so needs to be avoided. This means the latter option is far more popular with the rest of the enterprise: what’s “good enough” for marketing won’t do for everyone else, unless the marketing app is capable of far greater precision than marketing may demand of it.
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