Business Intelligence needs Data Integration

Since the mid-nineties, we have been hoping for higher knowledge through Business Intelligence systems. In many cases, hope has remained the same. Because most projects that were started with ambitious goals were already stuck in the initial phase – the creation of the underlying database. Although enterprise solutions contain the relevant data for planning and controlling even the most complex business processes, in the rarest of cases this data is maintained in such a way that it can be transferred to a BI system without manual postprocessing. The problem increases if the data from several applications from different providers is to be combined with different database structures and system architectures.

To describe the problem strikingly: For the date field alone, there are several dozens of formatting options – from the American version with a two- or four-digit year, through full or abbreviated month names, to punctuation. The variability of master data is practically unlimited. Furthermore, transaction data and their aggregated forms allow the richness of variants to explode millions of times over. No wonder that data management and data maintenance remain a constant issue even after the successful introduction of business software.

Here the point is often reached where the topic of Business Intelligence is already out of focus because the preparatory work is already getting out of hand. By the time the data has been consolidated in a data warehouse, it is not uncommon for the first hundred thousand euro bill to have been burned. At this point in time, no real benefit has arisen – not to mention an insight.

In addition, a data warehouse is based on copied data that is regularly updated. In the rarest of cases, the copy is as up-to-date as the original data. The volume of data alone is an obstacle if the update covers the entire dataset. BI solutions therefore have the disadvantage that they generate knowledge from “yesterday’s” data.

With the actesy metadata framework, we at actesy have therefore developed a way to achieve a result at a fraction of these costs that opens up real business intelligence. We use more than 250 pre-configured adapters to extract the data from existing application architectures and normalize them within the actesy metadata framework. Of course, the integrity of the data is maintained: If the data changes in the productive systems, the data in the actesy metadata framework is also updated immediately. Thus evaluations are always carried out on the newest possible state.

Also in the second phase – the creation of relations, patterns and principles – relationships between data, data fields and metadata can be established comfortably with the actesy metadata framework. The methods on the basis of which the findings are to be drawn from the data can be determined.

After all, the actesy metadata framework shows its full strength when it enters the third phase of the BI project – where many BI projects never reach: the actual knowledge management. With the actesy metadata framework, you can develop a complete platform for the evaluation and dissemination of knowledge and results – from the management dashboard to drill-down functions, via which you can finally return to the original data.

The actesy metadata framework can also provide additional help where a BI system is already operational or in the process of being introduced. As an upstream “data crawler” it can generate the up-to-dateness in the data warehouse it needs for real-time intelligence.

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