Data

DATA IS THE MATERIAL OF THE INFORMATION AGE. AN ASSET MUCH LIKE ANY OTHER BUT HOW TO START?

To state the obvious, we need data to do Data Science. For this reason, a significant part of Data Science focuses on the ability to capture, store and organize large and diverse datasets. A rationale approach to how one should approach the development of a data core for the organisation is a significant business case in itself.

Depending on the maturity level of the organisation key questions should be established to address data science challenge.

Is more data better?

Principally, anything that you know is not useful shouldn’t be stored. The tricky thing is to understand what is not useful.

What data is important to capture?

We recommend a common sense approach of capturing first the most obvious information one should understand. Such information should already contain relevant information that will verify existing knowledge and beliefs.

To develop this data core, we then recommend an objective approach based on what you want to achieve. Again, that should be a very centered approach. In general, this approach means value is achieved to mark against the cost of investment. Ideally, this also warrants greater investment risk as confidence grows in addressing larger and complex challenges.

How to capture data

In general, we class data capture solutions as Input Adaptors, as in most cases data is sourced and adapted for storage. We have a range of Inputs Adaptor solutions that capture data from large existing static pools to highly dynamic event based environments. Information can be in numeric, text or pattern format and in many cases the question of learnability is handled at this level. Our base range of adaptors can easily be modified for most more specialized cases.