You would be surprised to know how many organizations fail miserably in their efforts to become truly data driven. Many would attribute the resistance of becoming data driven to the intrinsic nature of humans to trust their own judgements more than anything else. But that is not true.

In most cases, organizations collectively agree to the notion of taking data driven decisions, but they fail in building the systems necessary for building data driven organizations.

The common problems that organizations face are

  1. Incomplete data – Fragmented data spread across the businesses
  2. Inaccurate data – Lack of trust in data
  3. Inappropriate data – Data does not mirror the reality

Well, the obvious answer to these questions is to build an enterprise solution that consolidates data, monitors the quality of data and ensures appropriateness of data for businesses.

Not so easy – as there is always huge resistance from businesses to adopt enterprise solutions due to reasons related to both politics and convenience. If you cannot convince your users to adopt the enterprise solutions, the efforts are a waste of time and money.

The key aspect of building an enterprise data architecture is to be able to clearly differentiate its benefits from the existing solutions, so that there is smooth adoption. A deep study of industry, markets, competition and trends is necessary to build a good enterprise data architecture.

You need to ensure that the proposed solution is the best of its breed, and at the same time, you need to know who your users are, what do they really like, where is the gap etc to design an enterprise solution that will be successfully adopted.