In yesterdays post we discussed different types of information required to conduct a great discovery. Obviously this information comes from systems, most of which many companies still do not have in place and can’t afford. So again, what to do?
Let’s first try to understand what analytics actually is. According to Wikipedia’ a simple definition of analytics is “the science of analysis”. A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.
So why can’t many companies afford analytics? The answer is because they are complex. In my early days of selling data warehouses with one of the industry leaders, in fact the best in the space today the combination and analysis of data from disparate functional areas of a business were nearly impossible. As such if a company was advanced enough to have this type of information it most likely existed in islands that evolved into departmental data marts like category management systems. These data marts ultimately evolved to complex databases with relational data models that allowed access of data contained in these disparate systems and then into on line analytical systems capable of managing massive amounts of data .
It’s probably no surprise that the early adopters of these technologies were the biggest of the big companies and governments. So when we get to analytics that support e-procurement systems or procurement systems in general, the systems that provide the analytics have to reside within a company’s corporately supported data model. If not, they initially at least have to have a procurement data model that supports data contained in ERP systems, Financial systems etc. Since the trend is not a backwards direction of recreating islands of information, pilots of these systems that show significant benefits, will only end up as a corporate roll out through integration within the corporate framework and data model.
I could go on to explain the expense and time associated with these implementations, but there is a reason that these solutions are not readily implemented within lower tier one and tier two retailers. Number one is that many still do not have easily accessible corporate views of data. Number two is the cost; resources and time to implement them are difficult for these companies to justify.
As such there continues to be a need (niche) for providers that understand retail from an operational and financial perspective that know where to look, what to ask for and can assemble, analyze and report on data the old way to support the procurement data requirements of mid tier one and tier two retailers.
We look forward to and appreciate your comments.