Feb 20. 2020

Data Analytics – Commoditization vs. Creativity

Data Analytics – Commoditization vs. Creativity

Data Analytics – Commoditization vs. Creativity

Data analytics has shot into the spotlight with the proliferation of abundant internal and external (public) data. Phyton, R and other programming languages allow a more advanced gathering/collection and analysis of data from multiple sources.  Since currently the DA skillset is in short supply, organizations, governments and educational institutions extensively promote its merits and value.

Real demand vs. commoditization

However, one should not forget that many of the stakeholders except the potential employees have a built-in conflict of interest. Overestimating and inflating demand figures for data analytics would ultimately create an oversupply and lead to the commoditization of this domain.  Just like software development, which has been commoditized through and rapid ramp up of educational/training programs as well as global outsourcing.

data analytics

Creativity cannot be commoditized

Pure execution of processes tends to be commoditized over time. For instance, if data analytics is used for identification of regulatory breaches the problem/question is more exact.  This means that mapping of requirements and detection of non-compliances is more focused on execution.

However, creativity is indispensable for DA projects driven by internal audit or risk management. These initiatives require understanding of multiple domains, interdependencies and the hacker/fraudster mindset etc. As a result, asking the right questions are crucial for these DA projects to succeed.

Design of data analytics has more to do with creativity

Unless interdependent thinking/mindset is ingrained into the design of data analytics projects major anomalies could be missed.

What do I mean by that?

Let’s look at a scenario where DA is run on various databases to detect certain well-defined potential exceptions. This is a reflection of a siloed mindset, which could lead to major anomalies being missed. The reason is that in certain cases, we should be questioning why these databases are not linked at the first place.

Let me give you two hypothetical examples:

  1. Police database is not linked to the database of customs and border control
  2. Residential address is not cross-referenced among various databases such as: ministry of health, ministry of manpower etc.

Questioning the lack interface and validation routines among various independent database can bring enormous value. This is one of the many areas where creativity in the design of a DA projects can really shine through.

How would you approach designing your DA projects?

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