My 2016 Business Intelligence & Analytics (BI&A) Wish List

It's that time of year where we bid goodbye to what we did not like about last year and look forward to our best hopes and wishes for this coming year. As I look back on the past year in BI&A, here is what I'd like to see in 2016:

1.       Some real standards the software vendors will respect

I'll admit I've wanted this for a long time, but I can still hope. We are now in the big-data driven third generation of BI&A technology. The first was relational databases. Then, the industry settled on SQL as the standard query language and that facilitated a whole industry with interoperable query tools, ETL tools, database platforms, and a generation of expert professionals. The second was multi-dimensional technology. In this case, we never even saw a standard for query languages aside from some weak attempts to extend SQL. Now 'No SQL' is the emerging non-standard. Metadata standards? Dream on. The one credible attempt at it, the Common Warehouse Metamodel in the 90's, never really caught despite support from several major vendors. Those same vendors eventually decided that proprietary solutions could become de-facto standards if they acquired enough market share through buying out smaller competitors. So metadata standardization is achievable, but mostly with single vendor solutions. This brings me to my second wish.

2.       The next consolidation wave

As is always the case during a technology consolidation, you get a wave of startups with new technology and established players trying to reposition older technology in a fight to win over the early adopters in the market and some love from the industry analysts. In time, the weaker players drop out of sight and the stronger ones get swallowed up by the big enterprise players looking to buy technology and market share. It will be a little different this time as SAAS deployments will allow more of the upstarts to thrive on their own. Some may end up pushing out older players in the process. For me though, there are just too many incomplete solutions out there right now and the consolidation wave cannot come fast enough.

3.     Fewer Big Data wannabees

Another consequence of this technology shift is the emergence of a huge number of resumes on the market promising a depth of knowledge and experience in big data technology stacks that is more hype than substance and becomes clear 10 minutes into an interview session. I’d rather work with seasoned BI&A professionals that have mastered the basics of software engineering, project management, requirements development, data governance, and testing who have demonstrated the ability to learn and adapt to new technology quickly.

4.        More agile BI&A teams and less Agile methodology zealotry

BI&A pros have known for decades that agility is mandatory in our work and waterfall methods do not work. Requirements are generally not completely known in advance and only revealed through prototyping and iterative development. Value should be delivered on an ongoing basis. On the other hand, some of the main tenets of the now-revered Agile movement do not work all that well in the BI&A space. We do not develop structured applications as much as we strive to create environments where our users can create their own applications. This limits the development of workable user stories in advance. The evolution of our platforms over time and need for stable development and support teams does not lend itself well to the scrum concept as it is typically advocated. Yes, we need to be agile (small A) but not necessarily Agile (capital A.)

Business Intelligence and Analytics has never been more recognized as vital to success in business, government science and education. Our tools and technology are better than ever. Those wishes have come true. Now, I wish for all who read this a happy and successful 2016.