Why is the successful implementation of "big data" applications a question of knowing how to sell?

Business|Opinion| October 29, 2015| JeanMarc

Jean-Marc Bellot, Business Partner with CustomerCentric Selling and mentor for the Dataiku sales team, first published the following post on his personal blog in French. We've translated it and decided to share his insights with you here.

Each time a technological innovation comes onto the scene, we witness the re-run of a well-rehearsed script that unfolds more or less according to the following three steps:

  1. Technicians seize control of the innovation, arguing that they are the only ones who know what it's all about;

  2. The first implementations of the technological innovation fail with those who are supposed to be precisely the people to benefit from it. And with good reason: usually, they have hardly been consulted;

  3. The top management is concerned about the scale of investments made and the weak results obtained. The technicians are then stripped of their pet project.

Think of the emergence of the first generation CRM with Siebel in the role of the evangelizing power. CIOs took up the subject, and invested millions in the implementation of sophisticated customer relations management systems. Once served, sales reps, the designated beneficiaries, shunned the applications put at their disposal. Despite change management programs as expensive as they are ineffective, the take-up rate has remained low. This, of course did not fail to cause concern amongst general management, who was often forced to wipe a discreet sponge - a write off - over the investments made for the project. CIOs were taken off the case in favor of sales departments, which took a perverse pleasure in subscribing to services like salesforce.com that require minimal intervention from IT experts.

Some would say that the problem is technical, that it's normal for an innovation to be entrusted to the technicians first and that its dissemination within the organization takes time. Another more fatalistic view is that it is a manifestation of a psychological invariable. As for me, I see it as a sales problem.

In my view, the major reason that the first wave of implementation of a technological innovation tends to break like a wave over the undertow is due to two equally detrimental phenomena coming together:

  • On one hand, its unwarranted appropriation by the technicians
  • And on the other, the incompetence of sales reps.

In both cases, this results in too little consideration of the beneficiaries when putting together the sale. Because for the sale of an innovative product to take place for the benefit of everyone in the B2B environment, it is important that it be firmly grounded on three main pillars:

  1. The innovation is the object of desire for those who are its beneficiaries (principle of desirability);
  2. Its operational implementation is guaranteed by technicians (principle of feasibility);
  3. Economically, its implementation must represent a profitable investment (principle of viability).

By trying too hard to seize control of the innovation as soon as it is released on the market, the technicians forget to create the conditions for desire to emerge on the part of the beneficiaries. By neglecting to involve the professions in the solution's design phases in the conceptual sense of the word, they alienate the support of those who should benefit most from it. As for the sales reps, they are content to address the technical contacts and make "small-scale" sales because they didn't seek the perspective of those who, beneficiaries or representatives of top management, are likely to appreciate both the desirability and the economic viability of the initiative.

This is exactly what is happening now in the oh-so-promising "big data" market. In a recent McKinsey report entitled “Getting Big Impact from Big Data”, David Court highlights the barriers impeding the take-up of "Big Data" within organizations. I have counted six, which are like so many symptoms of the repeated "enthusiasm/disenchantment" cycle mentioned above:

  1. The "data scientist" is the star player yet remains rare and is often quite expensive;

  2. Aggregating data from disparate sources presents complex integrity problems;

  3. While the investments - particularly the maintenance costs of existing systems - are high, "top management" is disappointed to see that these “big data” projects have not yet resulted in a noticeable ROI;

  4. Unfortunately, due to skill set disconnect and tool incompatibility between tech and business oriented teams, data scientists often have no choice but to go along with the tune of "give us the raw data, we'll extract the nuggets from it", thus alienating themselves from the support of those who should be their biggest fans;

  5. The "top management" is disappointed to see that despite some initial outcomes worthy of interest, the systems in place falter in comparison to the bombastic promises associated with "big data";

  6. Finally, the beneficiaries in the trades are put off by the "black box" aspect associated with the implementation of the first "big data" applications. It is difficult for them to build strategies on results whose origin they don't understand. The result: they tend to fall back on driving blindfold, which has been more successful for them thus far.

We see it coming, the six impediments highlighted by McKinsey require a technical as well as an original sales approach if we want to avoid giving up on a technology that is full of promise.

Technically speaking, it is important to have a user-friendly "big data" application generation environment to be put in the hands of the beneficiaries, namely the trades. By manipulating the environment iteratively, they will be able to find out which algorithms on which datasets can best explain a particular phenomenon (e.g. customer defection) and how to exploit the results.

It so happens that I am lucky to be working with a company - Dataiku - that has made a positive decision to break down the barriers in the design and implementation of "big data" applications.

When Dataiku decided to build Data Science Studio software - an advanced analytics tool for the whole data team - they all agreed upon one undeniable truth: data teams are diverse and often include data scientists and software engineers who share projects with marketers and salespeople. That's why they decided to create a tool where analysts can point, click, and build, developers and data scientists can code, and high-level data consumers can visualize. This construction and approach allows different skill sets to productively work together using the tools and languages they know best to build end-to-end services that turn raw data into business impacting predictions quickly.

But once again, I'd like to get back to the sales approach. The trick in complex sales, we have seen, is to harmonize the perspectives of three types of stakeholders - the beneficiaries who express the desire, the technicians who ensure feasibility, and the economic decision-makers who look at economic viability. Neglecting one of the panels of this triptych results in a situation of failure:

  • With the creation of a "chimera" in the event that you have omitted to take into account the technical dimension;

  • With the creation of a "dancer" if you have neglected the economic viability panel;

  • Or worse, with the creation of a "false bargain," if you've simply obscured the expression of desire in the mouths of the beneficiaries.

By putting the beneficiaries back at the center of the process - that is to say, in this case by denying exclusive grip of "data scientists" on the "big data" subject, organizations give themselves the means to minimize the risk of disappointment on one hand, and on the other hand to maximize the chances of seeing their investment bear heavy, tasty fruit.

Hello, good sellers!


Note: Gartner, the research firm, has created a pretty awesome tool for accounting for the now well-known hot-cold phenomenon characterizing the emergence of innovative technologies on the market and their adoption by the organizations: the "hype curve". It is no more nor less than a question of placing technological innovations on a curve describing the five main phases of new technology take-up, namely:

  1. The Innovation Trigger
  2. The Peak of Inflated Expectations
  3. The Trough of Disillusionment
  4. The Slope of Enlightenment
  5. The Plateau of Productivity

Today, according to the Gartner analysts, "big data" is in the process of beginning the plunge into the abyss of disillusionment.

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