Then it's about scouring data and finding potential for use cases to work on in the beginning. At this stage, which likes to have the character and positive working atmosphere of an experimental laboratory, as little as possible should be invested in IT technology. Everything you need is either available free of charge or at very low cost. A larger investment only makes sense if an application has been confirmed.
Nevertheless, it is advisable to send your own personnel resources to the outlined lab for customer analytics. Ideally, these are the so-called Data Scientists, but because many companies are currently dealing with
Big Data> Big Data link> & Advanced Analytics, they are a rare species.
<h2> It's also easier with Customer Analytics </h2>
Get motivated people who have a basic understanding of advanced analytics, free space, and a little time to "roll the Euros" down. almost by itself. Because applications quickly come to light that no one at first thought of. Best practice cases show that even for large companies with several thousand employees, it is sufficient to release 0.5 to two full-time equivalents (FTE) for the test lab at the start.