I am a great fan of podcasts, listening many hours a day, learning all sorts of stuff across many domains of knowledge. It makes traffic jams fun. It makes the stationary bicycle at the gym fun. And so it was that I stumbled upon a podcast called Data Skeptic.

Data Skeptic is a podcast about data science. Not your basic Excel analytics but rather advanced data science such as Bayesian techniques, machine learning and adversarial neural networks. In other words a host of things that might otherwise scare the average citizen into changing the channel. But this podcast is extremely cleverly conceived in that it is delivered as a lesson to the host Kyle Polich’s wife, Linh Da.

And this is the brilliant bit - Kyle is a super guru in data science. His wife knows diddly squat about the subject (really, I mean it). So when explaining advanced concepts like MapReduce or back-propagation or multiple regressions, she gets to ask the most basic of questions, and he is forced to explain it in terms she can understand. So as a non-threatening resource for people wanting to know about data science, but being too scared to ask, this podcast is gold. I am a computer scientist, but I learned more about this field sitting in traffic than I would have done in a university lecture hall.

Anyway, moving on. I have recently joined Ixio Analytics as Chief Strategist, coming from a fairly broad background in IT and Computer Science. There is a reason why I am so attracted and enthusiastic about Ixio Analytics. It is this - the company is staffed by scientists and R and Python programers as opposed to clever tool wranglers. I do not wish to demean tool wranglers - it takes a great deal of skill to learn and properly deploy Tableau or Qlikview or Periscope or the analytics bolt-ons to SAP or Oracle or MS Dynamics. There is much value to be extracted by judicious use of these packages.

But for the most advanced of data solutions, one needs the flexibility to build tools out of raw computer code, rather than to simply access a bunch of configurable tools out of the toolbox, as robust as they may be. This approach gives the broadest possible palette to the data scientist, and expands the horizon of data possibilities far into the distance.

Furthermore, the liberal sprinkling of science post-grads in the company brings a discipline of carefully structured problem-solving and dogged determination into the DNA of the company. Scientists thrive on the battlefield of difficult questions, and they like to fight until they win.

Of course, this approach to insight extraction from dull data brings with it challenges. Corporates are often more comfortable with ‘big name’ software modules, sometimes mollified by impressive and sparkly graphics, and are rarely pleased by being made to feel that they do not really understand their own data, or more pointedly, that there is data available outside their own companies that can act as a multiplier to their own.

To date, our approach has been simple - give us your datasets. Our scientists’ hands are itching to caress them. Let us show you the magic we can make from the fields which sit mutely in your database records.

And in all cases, the data owners come back for more - their revenue is increased, their costs are contained and their competition is weakened. Linh Da couldn’t have put it more succinctly herself.