Three steps to harnessing data science

12 Jan 2017

  • Analytics
  • Data

By: Joel Nicholson, Executive Chairman, Marketsoft

Does ’data science’ conjure images of laboratory geniuses sitting in top secret Russian military bases? Perhaps a scene out of  Minority Report of futuristic technicians swiping three dimensional visualisations of data and charts? Whatever the image you have, the definition of data science is wide and varied, but the real impact of data science on business is undeniable.

We hear about the clichéd examples such as Amazon’s data science team developing machine learning to improve exponentially on how to predict customer needs and next actions.

But what are the practical approaches for medium size organisations with limited resources who are at the start of harnessing data science?

Firstly, the cost of data science is coming down. Expertise previously required to model data is now more and more embedded within low-cost software.  For example, regression analysis previously required a university qualified statistician, however is now automatically performed with  a single click.

Secondly, data quality is still a big barrier for many data science projects. As data is pulled from a broad range of external and internal sources, the gaps in incomplete or unreliable data is only getting larger.

Finally, preparing the data for data science activities can consume 80% to 90% of the time for an expensive data scientist, so with limited resources, how can you come prepared?

There are three actions you may want to consider.

In-house expertise
Ensure you still have expertise in your team or partners that are strong on creative thinking, yet at the same time relate to business context. The need for pure technicians can now be replaced by low cost software.

Multiple data sources
Think of data science a little like a forensic investigation - there is rarely a single source of golden data on your customers, it is usually about compiling a range of subtle signals that combine together to form a better picture. For example, combine company website statistics on tag management with ANSIC industry classifications to determine customers that are not keeping up with their industry competitors online

Preparation is key
Be prepared. Continually deploy data maintenance services to enhance the data, fill the gaps, and make data science as conjured up by Minority Report become BAU!

Need more info?

CATEGORY Analytics Data

TYPE Article