Big Data, Business Intelligence, Analytics: where do companies stand in these areas? What have they implemented in their management structures to create a lever effect on their data, tools and emerging knowledge? How do they position these themes in their management organisation?

Some semantic definitions

– Big Data: the immense volume of data produced at every moment by us, individuals: discussions on social networks and blogs (non-structured data), internet navigations, purchases; by our “machines” and systems, often despite us: geolocalisation, radar checks, money withdrawals, telephone communications (structured data); by our administrations; by health systems; by research professionals: consumer, medical… and of course, by businesses themselves in their functioning.

– Business Intelligence (BI): the intelligent formatting of data, often internal company data, which allows easy reading and quick decision-making. BI tools do not think (it is the decision-maker who thinks), they sequence information from the past.

– Analytics (or Predictive Analytics): is the analysis of (large) volumes of data thanks to artificial intelligence tools and sophisticated algorithms. Analytics thinks, using these raw materials, and generates precise recommendations to improve performance. Analytics answers questions such as “What combinations of actions should we carry out to keep our clients? to sell them such and such new product?”; “What components should we put in a new perfume to create a trend that will spread?”; “What actions would reveal fraud?”; “How can we identify an incident?”…

What are the challenges for companies?

The market now offers very sophisticated tools to capture dispersed data and in a certain number of cases, completely non-structured data, sometimes in very large volumes, to order it with a view to analysing it. The challenge for companies is to coordinate all the elements in this ecosystem to provide “insights” to its business directors (Marketing, Risk, Sales, Research and Development, Innovation…) that are relevant and generate value.

This is the role given to the Chief Data Officer (when there is one, which is still rare). He or she must understand the important challenges present in the company, they must be at the heart of the strategic thinking (and very much upstream), have a real power of influence and have resources at his or her disposal. He or she is a member of the executive board. He or she discerns the issues to be addressed by Analytics for each business area and coordinates the implementation of the architecture that will industrialise the processes, from the collection of data right through to the delivery of recommendations to the directors. His or her mission is to implement and then manage the analytics “factory” that will best exploit the available data. Of course, he or she will be permanently monitoring the whole “value loop”:

– new subjects that should be addressed to help business directors to improve performance

– data sources that are constantly multiplying and changing (social networks and blogs, for example, are sources that are just starting to be exploitable)

– the means of collecting, storing and extracting this data on demand

– the means of analysing it: the appearance of text mining for example (the ability of algorithms to understand a text in order to extract information from it, then structure that information to analyse it) has allowed the exploitation of discussions on blogs and social networks about “products”

– and again, new subjects that can be addressed thanks to new sources of data and new means of processing it (the loop is complete…).

Many companies, right up to the very largest, are in possession (or have the capacity to possess) data whose potential is absolutely extraordinary. But the dispersal of the information and the lack of mobilisation means that very often they miss out on this value. However, the technical means are now completely virtualisable, very few in-situ infrastructures are necessary, only the brain power and mobilising energy of a CDO are necessary!

The Chief Data Officer’s competencies

This post is of course rare and emerging. As well as the highest technical competencies (linked to Big Data and Analytics technological tools), and of course the seniority acquired in management or director general consultant posts providing a strong culture on the strategic challenges in a company, the Chief Data Officer must also possess distinguishing “behavioural” competencies in leadership.

Uman Partners’ research on this subject has led us to recommend that these executive managers must have a real capacity to be “strategic”; they must have a talent for managing innovation, grasping knowledge on the fly and developing themselves in complex subjects; they must be naturally at ease with senior management in their company… and with the complexity of the organisation. Of course we measure these competencies (and others besides) in order to be as predictive as possible of the success of the talents who, today, are inevitably carrying out different jobs.

Benoît Binachon – Uman Partners – Executive Search For Data Driven Functions