In my consulting work in the Enterprise IT space, I am seeing a definite trend of growing interest in Data Product/Advanced Analytics Design and Development which is becoming increasingly mainstream. Even as I view this a positive, it comes with its own set of perils and pitfalls that will need to be avoided.
Enterprise IT Application Development is often bureaucratic and involves multiple and redundant levels of management through the design, development and testing phases. Conflicting requirements from multiple stakeholders can make even the simplest of development efforts an intractable effort. For example, enforcing standardization is an underlying premise in most Enterprise IT Initiatives. In my experience standardization efforts undertaken without creating context and meaning around the standardization for the end users is self defeating in the long run. While Agile practices are in vogue and show some promise, they are a long way from dealing with the fundamental issues involved when dealing with large teams. Users want personalization and technology to solve their problems. This is particularly true in the area of Advanced Analytics and Data Products.
The Enterprise IT organizations I refer to here are not software product companies like Microsoft, Google or Facebook. They are typically IT organizations within Service companies like Banks, Retailers, Health Insurance Providers, Utilities and so on.
In his book "Data Jujitsu: The art of turning data into a product" , DJ Patil gives some simple guidelines which are very applicable to Advanced Analytics Design and Development within an Enterprise which I have listed below and added my own observations.
While many of the rules listed here may seem like common sense to adopt and follow, in my experience, more often than not this is not the case and is something all of us would be well served to keep reminding ourselves about.