“People are often more willing to act based on little data or no data than to use data that is a challenge to assemble,” Robert J Shiller, Economist.
The process of collecting, securing, organizing, managing, integrating, and analyzing data doesn’t sound like a one man job. In fact, it is a process that requires a lot of time and manpower. Without the right tools, it’s inconceivable especially for a big chunk of data.
It’s not even the complexity of any of the procedures mentioned above but the amount of effort and expertise required at each stage.
For instance, let’s take a company x that exports flowers to the Republic of Ireland. The company ships 10,000 boxes in a day, 70,000 boxes in a week, 280,000 boxes in a month and 3,650,000 boxes in a year. Now, let’s say that the data specialist misses a (0) and instead of 10,000 boxes, keys 1,000 boxes. The analytical report becomes incorrect. It’s a simple garbage in garbage out situation.
What this shows us is that even the slightest slip even in the simplest of tasks like data input in the process, affects the result and if this result is used for decision making like in producing a forecast then a lot could go wrong; not to mention possible loss. Let’s bear in mind even with the best validation tools and system there is still the likelihood of missing a digit or two, a symbol or even a letter. This explains why even your bank balance sometimes isn’t your bank balance.
For this reason, most people would rather work with no data at all. Working with data is not only detail specific but also laborious.A study done by HillMarc Consulting on use of data to support decision making shows that 80% of senior managers in Kenya would rather use their gut feeling when making vital decisions that use data. Well, who can blame them, but usually this would prove costly as chances of making wrong decisions are extremely high.
Most big companies in the world have embraced the use of data analytics to support decision making in various aspects of their business like operations, finance, administration and even marketing because every company wants a return on any investment. Speaking of Return on Investment (ROI), in order to manage something, you have to measure it so that marketers can easily point out why a certain advertisement platform is better than the other hence make the most of on the platform.
So, are we saying that all companies should have analysts? Or that all major departments should have analysts for companies to make data supported decisions? Is it even feasibly possible? We all know how the economic down turn over the last 3 years has affected most companies. With the country still trying to recover from the post-election fever, most companies would rather keep their staff as lean as possible. Financial institutions are in full retrenchment mode and I am sure other companies will go down the same road if the economic climate doesn’t change for the better.
There lies a silver lining however as more data analytics companies will come in to fill the gap with the highly qualified staff and high-end analytical tools.