Well, all of the tub-thumping is paying dividends, it would seem. Since late last year, I`ve felt like John the Baptist, a lone voice in the wilderness proclaiming the benefits of data disciplines. I`ve advised how the lack of these disciplines can bring doom and disaster crashing around the heads of management, undoing strategic initiatives such as enterprise resource planning, customer relationship management and business intelligence.
What is becoming clearer by the day as various companies turn up the heat is that the scale of the problem has been underestimated.
Julian Field, GM, Ascential Software SA
Now, after almost a year of spreading the gospel, I`m finding increasingly that I`m in good company. Gartner, Meta, IDC and more have all come to the same conclusion: that unless companies recognise the critical nature of their data and identify the problems in their systems, they will continue to unravel the value these systems can and should represent. Accordingly, they will miss many business opportunities and see the impact on the bottom line.
What is also becoming clearer by the day as various companies turn up the heat is that the scale of the problem has been underestimated.
Consider this real-life example:
A telecommunications company has a critical problem at source, with many errors being incurred in the data capture process. So, when an application is submitted or granted for a new service, there is typically a certain percentage of error in the capturing of this client information. Then further error is incurred in information handover: this is always the way with any process that depends on multiple, standalone functions. The result, ultimately, is that the company cannot bill many customers properly, and it is losing a significant amount of money each month. In cascading order, this company needs data profiling, data quality and data cleansing and mapping solutions.
The issue has also become much broader than business intelligence, which is where it has been focused to date. Typically, when people have spoken of data quality, or the ETL (extract, transform and load) routine, it has been in the context of making correct decisions against correct data. Now there is acknowledgement by large corporations that their data is shot at source, and that they are haemorrhaging as a consequence.
The new wave is enterprise data integration, and unfortunately that`s what it is and we can`t call it anything else. I say unfortunately because the resulting acronym, EDI, usually relates to the historical concept of EDI, or electronic data interchange!
Enterprise data integration - which includes data cleansing, matching, conversion, consolidation and augmentation - is typically being delivered today through companies acquiring, at significant cost, products from vendors.
It almost goes without saying that vendors are presented with an awesome challenge of their own: integrating their acquisitions with their own products.
The market is now in a different space, one involving metadata transfer between ETL and data quality, and, for the first time, coming to embrace process integration. (This, on its own, is an important development, as historically companies have not viewed data and process as interlinked, and their Chinese Wall separation holds negative implications for companies.)
The market has not begun to see the full implications of the emerging enterprise data integration evolution: for instance, when does ETL come first, and when does data quality?
Also, with so many vendors having depended on each other and channel alliances having been the order of the day, who`s going to fall by the wayside?
Fortunately, for customers desperately seeing to understand what`s "wrong with this picture", they won`t have to worry about too many of these issues. Instead, they`ll simply gain the benefits of this new and emerging world.
Share