Data scientists are a curious breed. The term encompasses a wide range of specialties, all of which rely on statistical algorithms and interactive exploration tools to uncover nonobvious patterns in observational data.

Who belongs in this category? Clearly, the “quants” are fundamental. Anybody who builds multivariate statistical models, regardless of the tool they use, might call themselves a data scientist. Likewise, data mining specialists who look for hidden patterns in historical data sets — structured, unstructured, or some blend of diverse data types — may certainly use the term. Furthermore, a predictive modeler or any analyst who builds fact-based what-if simulations is a data scientist par excellence. We should also include anybody who specializes in constraint-based optimization, natural language processing, behavioral analytics, operations research, semantic analysis, sentiment analysis, and social network analysis.

But these jobs are only one-half of the data-science equation. The “suits” are also fundamental. Any business domain specialist who works with any of the tools and approaches listed above may consider him- or herself a data scientist. In fact, if one and the same person is a black belt in SAS, SPSS, R, or other statistical tools, and also an expert in marketing, customer service, finance, supply chain, or other business specialties, they are a data scientist par excellence.

Both of these skill sets are fundamental to high-quality data science. Lacking statistical expertise, you can’t understand which are the most appropriate algorithms and approaches to make the foundation of your statistical models. Lacking business domain expertise, you can’t identify the most valid variables and appropriate data sets to build into your models around.

“… and they lived happily ever after.” This is the typical ending of most Hollywood movies, which is why I am not a big fan. I much prefer European or independent movies that leave it up to the viewer to draw their own conclusions. It’s just so much more realistic. Keep this in mind, please, as you read this blog, because its only purpose is to present my point of view on what’s happening in the cloud BI market, not to predict where it’s going. I’ll leave that up to your comments — just like your own thoughts and feelings after a good, thoughtful European or indie movie.

Market definition

First of all, let’s define the market. Unfortunately, the terms SaaS and cloud are often used synonymously and therefore, alas, incorrectly.

• SaaS is just a licensing structure. Many vendors (open source, for example) offer SaaS software subscription models, which has nothing to do with cloud-based hosting.
• Cloud, in my humble opinion, is all about multitenant software hosted on public or private clouds. It’s not about cloud hosting of traditional software innately architected for single tenancy.

Whoa! Hold your horses. If this is indeed a key challenge that you’ve tried to address in the past without much success, consider switching jobs. This is not a joke. Business intelligence (BI) is an employee market right now; a key challenge for most BI employers is finding, recruiting, and retaining top — or actually any, for that matter — BI talent. Consider that IBM BAO alone added more than 4,000 (!) BI positions in just over a year! Every other major, midsize, and boutique BI consultancy I talk to is struggling to find BI resources. So if you’ve been fighting this uphill Sisyphean battle for a while, consider new channels for your noble efforts.

Now, some more practical advice — albeit not as exciting. Start from the top down. In a few minutes I am getting ready to talk to yet another large client whose CEO does not “get” BI. Can you rightfully blame him/her? Yes and no. Yes, because how can you manage any business without measurement and insight into your internal and external processes? So if your CEO didn’t learn that in his/her MBA 101, suggest that he/she look for another job. And if you’re still standing after that and have suffered only a mild concussion, consider that many BI projects have been less than successful, and ROI on BI — one of the most expensive enterprise apps — is extremely difficult to show. So can you really blame your CEO?

By Boris Evelson and Rob Karel

Our latest BI solution center (BISC, which in our definition is more than a BICC/BI COE) report is now live on the Forrester website. Here’s a brief summary.

Forrester firmly believes that tried and true best practices for enterprise software development and support just don’t work for business intelligence (BI). Earlier-generation BI support centers — organized along the same lines as support centers for all other enterprise software — fall short when it comes to taking BI’s peculiarities into account. These unique BI requirements include less reliance on the traditional software development life cycle (SDLC) and project planning and more emphasis on reacting to the constant change of business requirements. Forrester recommends structuring your BISC along somewhat different lines than traditional technical support organizations.

Earlier-generation BI support organizations are less than effective because they often

• Put IT in charge
• Remain IT-centric
• Continue to be mostly project-based
• Focus too much on functional reporting capabilities but ignore the data