Self-serve analytics? Can you please define?

Is this place transitioning to true self-serve? Credit: acolori

Is this place transitioning to true self-serve? Credit: acolori

I have been living in the UK for now more than 15 years and, when I first arrived, I was struggling to understand or be understood (some say that is still the case). I owe it to many people that I now control (vaguely) this beautiful language though I still recollect when someone told me: "So, you are actually French". In French, the word "actuellement" stands for "currently". A so called faux-ami. So my answer was: "Well, I've been French for a long time".

Bemused look ensued...

Now that I work in the advanced analytics space, I am starting to hear a word, well an aggregation of words, which is also open to misinterpretation. The expression "self-serve analytics". Many organisations are using this definition in marketing literature and it seems to be gaining a lot of popularity. But as we (PredicSis) seem to define self-service in a different way than many other definitions I came across and, as I've learned my lesson and want to avoid sounding foolish again, let me share some perspective.

 

I am seeing a few great companies for which "self-serve analytics" seems to serve one part of the market which I would qualify as the "advanced" part. The professional who do know their algorithm language. People proficient with statistical languages, R and other complex Python "things" (note: I am not falling in this advanced segment so "apologies for the technicality of "thing"). There are data science studios or pre-packaged R algorithms and many other very useful solutions that suit a growing market of data scientists.

 

So what does PredicSis call self-serve analytics?

Good question dear reader. Well, what we call "self-serve advanced analytics" is to give analytics capability to these advanced users (more on this below) but also, importantly, to people who know nothing about data science jargon, to whom the letter R is simply between the letter Q and the letter S or those who are starting to lose track when the algorithm word crops up in a conversation. The extent of the technical knowledge of these people is loading up a csv file into a browser interface and running an SQL query before hand. Mention the words overfitting, grid search, fine tuning parameters and they get lost. Mention even the word "feature" or "aggregate" and they are walking away. BUT, and it is a big but, these people know their data and their business very well. These are the BI analysts dealing with data daily. Or even the CMO who know their strut.

What PredicSis enables them to do is not to get distracted by jargon but simply inject non perfect data into Predicsis and they can get, in minutes, not in weeks or in months what the churn risk at the population level or at the individual level. And the underlying reason that drives the risk of churning. So they can get clear insights in minutes: "oh, so my customers who have a big car AND have clicked on the shampoo page are the ones with the highest risk to churn? I did not realise that, let's get some activities in place right away". Ok, slightly over-streched example but we hear of CMOs who use PredicSis on a daily basis and telling us they had preconceived ideas, insert data in PredicSis and realise their views of the market was flawed. That is the true power of AI in corporate environment (I had to put AI somewhere...). A picture or even a video is worth a hundred words so this can be seen in a 5 minutes short video:

 

We believe that both "self serve analytics" approaches are needed, both those who know about advanced data science and those who don't. Needless to say that, as Predicsis falls in the so-called auto Machine Learning category, we also have very advanced data science teams using it. Very large teams of data scientists use PredicSis as they want to do far more in far less time, they want to increase their interaction with the business as a whole. What they particularly use is what is known as the automated feature engineering and selection (sorry...). No need to get into the technicalities but in short, this helps them find combinations of variables which us, mere humans full of biases would not naturally do without a sheer amount of work and data combinations that would take a large amount of time and yes, money (the shampoo and big car example above would be one of these features).

 

This is what the Predicsis team calls self-serve advanced analytics. A mechanism to enable the drive towards a true data centric organisation, where not only data science teams but also CMO, BI analysts, product managers, marketing manager, HR managers can use advance analytics on a daily basis with the ability to get data centric answers in minutes, not weeks or months.

 

After 15 years in the UK, I've learned the hard way that words can have different meanings. Hope that this little post will prevent some bemused looks when we talk about self-serve analytics. And if you don't believe me (fair enough), if you are really serious about predicting your customer behaviours, in minutes, and have some projects lying around that you haven't started because "it is too complicated (actually it's not), the data isn't perfect (actually it doesn't need to, the machine deals with it) and it is too expensive (actually it's not, if you are on AWS it starts at $3ph)", contact me. What works for Top tier US payment companies, EDF, Orange and others might not work out for you but you'll be able to test what we define as self-serve advanced analytics.


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