“Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care.” – Bowker, 2005
The number of analytics platform providers has grown substantially over the past decade, from a few niche players servicing specialised clientele to many ‘mass market’ providers servicing a range of businesses.
A large sales force with fancy suits, armed with carefully constructed dashboards, make it seem like these solutions are a ‘silver bullet’ to solve the world’s problems, however they often require expert users to develop the same standard of work.
Many of these platforms targeted at the lower end of the market are marketed as being ‘DIY’ or ‘easy to use’, however the promise is often met with implementation problems, frustration and most importantly, more dollars spent from the budget.
Why ignoring the need for understanding ends in disaster
Automation and ready-made analysis make these tools seem attractive, as many businesses, particularly in financial services, do not employ statisticians, data scientists, or mathematicians within their business. Often they employ staff from an IT background as their base role involves specialist knowledge of data architecture or other IT systems.
Data architects are vital members of the team and should not be underestimated, however, many lack the analysis skills required for business intelligence. These ready-made analysis tools seem like a cost effective way of filling a skill deficiency, however, lack of understanding of the results and analysis being performed is fraught with danger.
Easy to use but hard to master
Many of these tools are easy to use at a base level. However, performing basic functions very intuitively to get the full utility out of these tools requires an inhouse expert. In particular, visualisation tools are often easy to use, allow many people to access and manipulate data, and have allowed many businesses to take the first step towards true analytics sophistication.