Business intelligence (BI) service providers should offer data preparation as an integral part of analytic platforms to overcome the delays and problems caused by unprepared, incorrectly formatted or “dirty” data, says business intelligence subsidiary AIGS Yellowfin South Africa sales and marketing director Gustav Piater.

Typically, business data has either not been prepared for presentation through a BI layer or is locked behind firewalls.

Preparing data or assessing data preparation tools before getting around to running queries are major inhibitors for most; however, it can easily be readied along the way, he says.

Data preparation in a virtual environment uses the computing power of the server on which the data is stored. Rules are set within the BI platform and executed automatically when running the query; this overcomes delays in providing data for analysts. This approach also reduces the cost and complexity of managing data migra- tion processes, he explains.

“Integrating data preparation in the BI tool enables organisations to realise immediate value, whether dealing with well- or ill-prepared data. Presenting data straight from the source provides organisations with the opportunity to highlight where problems may exist and users can simultaneously analyse and clean data. BI projects do not need to be hamstrung by poor-quality data.”

Additionally, integrated data preparation and analysis lead to greater organisational trust in the validity of data and accuracy of its decision-making, as well as enabling users to proceed from data preparation to decision-making in one solution, which, in turn, enables organisations to maintain data lineage, visibility and control.

Disjointed data preparation processes also suffer from poor data governance, since a measure of self-service is involved. This usually leads to a proliferation of ad hoc datasets across the organisation for each analytics project, which increases the cost, risk and reliability of data preparation while decreasing scalability, security and governance, Piater states.

However, data in integrated virtualised data preparation BI solutions does not leave the source application, enabling information technology departments to maintain data governance and control, while data analysts can integrate more data sources directly into the BI environment in less time and without increasing data governance risks.

An important technical requirement to achieve this is a comprehensive metadata layer in the BI platform, and execution of the data preparation process at this metadata level. With this layer in place, any changes made will be uniformly reflected across all content based on that metadata layer throughout the enterprise – from reports and charts to dashboards, Piater concludes.