There is much discussion about whether Predictive Analytics (PA) can
be automated or not. This is a false dichotomy.

Predictive
Analytics is a strange beast - it needs to be ‘learned by

*learning*‘ and ‘learned by*doing*‘ – BOTH! That is due to the interconnected nature of the field. To be a successful hyper-specialist in “left nostril” diseases, one needs to have done Anatomy, Physiology and Biochemistry in med school. Similarly, for PA, learning-by-learning (which takes at least 6 years of grad school) is not a step you can skip and go directly to learning-by-doing and hope to become a true curer of business diseases!
In
PA, learning-by-doing can be an even steeper curve. As I have noted before in
my blogs, PA skills will have to be rounded out with mathematical inventiveness
and ingenuity applied repeatedly in a specific business vertical. These are the
hallmarks of an uber Data Scientist. Clearly, an uber data scientist as
described above cannot be bottled and passed around. Don’t even think of “automating”
all the things that an uber data scientist does.

*So what do we do about “scaling”? Are there support pieces we can automate to scale the solution.*
Comparison
to a programing environment such as MATLAB is appropriate. MATLAB supplies you
with all kinds of toolboxes. Similarly, in PA, many basic operations can be
automated – clustering, learning, classification, etc. But, like MATLAB, you also
need an environment where these toolboxes can be fine-tuned with inventiveness
appropriate to the business vertical, mixed and matched and augmented with
additional one-off solutions to address the overall business problem at hand. Otherwise, the solution will fall short (or flat!).

**So, part of PA can be automated.**PA toolboxes can be fine-tuned by data scientist associates and the overall solution can be conceived and put together with these toolboxes (with added “glue”) by the uber data scientist.

Note
that everything I talked about here refers to PA

*solution development*. Once the overall solution is developed,**“production runs” by customer personnel and visualizations by executives of the PA solution developed above can be mostly automated**(with data scientist looking over their shoulders – data can change on you on a dime; someone has to watch for the sanctity of the data and*non-stationarity*problems!).*Production*is where the solution needs to scale and it can.*In summary, PA solution development will require manual work by uber data scientists supported by data science associates; automated toolboxes for basic PA functions will help speed up the process and once the overall solution is manually cobbled together, production runs can be automated along with some amount of ongoing data science audit of the process and results.*

*Dr. PG Madhavan developed his expertise in analytics*

**as an EECS Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft Architect and startup CEO**. Overall, he has extensive experience of**20+ years in leadership roles**at**major corporations**such as Microsoft, Lucent, AT&T and Rockwell as well as four**startups**including Zaplah Corp as Founder and CEO. He is continually**engaged hands-on in the development**of advanced Analytics algorithms and all aspects of innovation (12 issued US patents with deep interest in adaptive systems and social networks).