15 time but do not assume that tools will prevent the need for some coding. This is where you are going to spend the bulk of your time and people so make sure you are being realistic about your entry-point skills here. Also keep in mind this isn’t the hardest part. In many cases the second challenge here is the bigger one – not can you manipulate the data how should you manipulate the data. The real open question here what to collect in the first place and how to actually use it in a meaningful way. That, of course, is a bigger issue which brings us to the data scientist question. Data Science – so finally to the hotly debated data scientist role. Popular press would have you believe that there is a plus or -10 year shortage of people that are skilled in data science. At the same time literally tens of thousands of people have completed open coursework from MIT and others on data science. Another variable from the mix is the evolution and progress tools that make data collection and analytic routines are commonly available and easier understood. So where does that put us? First, it is important to note that there are many-many use cases that never get to this level such as creating a data landing zone, data warehouse augmentation, and alternative ELT (yes, I wrote that correctly) approaches. No data science needed there – and as I’ve written elsewhere diving directly into a data science driven projects is a lousy idea. What if you have a project that has a data science dependency, what should you expect? Frankly, your experience here will vastly differ depending on the depth and robustness of your existing analytics practice. Most large Enterprises already have pockets of expertise to draw on here from their SPSS, SAS or R communities. The data sources may be new (and faster moving or bigger) but math is math, statistics is statistics. These tools increasingly work with these technologies (especially Hadoop) so in some cases they won’t even have to leave their existing environments. If you have the existing skills, so far so good. If you
don’t have these skills you are going to have to grow, buy or rent them. Growing is slow, buying expensive, and renting somewhere in between. Do not expect to be successful taking people with reporting or BI backgrounds and throwing them into data science issues. If you cannot honestly say “yes, we have advanced statisticians that are flexible in their thinking and understand the business” you are going to struggle and need a grow, buy or rent strategy. We’ll pick up effective strategies for dealing with grown, buy or rent issue, including notions of Center of Excellence, in future topics. TOM DEUTSCH CONTRIBUTOR