Data & Innovation : listen to the business users! #Datalab

Key accounts‘ opinion on DataLab (ENGIE, TF1, MAIF…)

Data Lab, Data Fab, Data Innovation Lab… Whatever expression is used, this kind of structure have been flourishing for 4 or 5 years. Paradoxically, Gartner still pointed out in 2017 that 85% of Big Data projects failed*. Wait, what ? A datalab would not be enough to properly gather and exploit data to make it available to every business users…? Read these major accounts testimonials to find out.

DATA projects : common difficulties.

One of the main reason for failure concerning data is the low adoption rate of new tools. The issue of adopting innovation is nothing new for change managers, who are confronted to change resistance on a daily basis. It is ridiculously obvious, and yet, leading countless initiatives to death.

When it comes to enterprise data, you will then be confronted to silos. Every business unit will go right to familiar context without a glance for general value creation. And when it comes to searching for relevant use cases, you can easily be blinded by the competition showing-off instead of focusing on your own business, because you are afraid of being left aside.

These 3 dead-end have in common the neglect of the innovation concept in its core definition: searching for a new, non-existing path. The best way to avoid these dead-ends? Taking into account data-specific technological needs and offering an environment favourable to new ideas to flourish.

Definition of a datalab: innovative workspace to explore, find and execute data driven improvements of business processes.

Different structures and one goal to bind them all: transversal transformation.

A datalab has to come within the scope of a company’s global transformation. It might be its spearhead or a simple side-project. Olivier Baes, MAIF dataLab manager, presents “the MAIF digital transformation support at every step through conferences, community management and internal communication” as its datalab main mission.

datalab structure

Various models are possible.

An independant datalab is possible, made of data specialists working on projects as an independent cell. At Generali France for instance, the board chooses projects to implement. Employees are trained from 3 to 6 months within the datalab, according to subjects. When they are sent back to their teams, they acquired new data skills they can use and share to the whole team, explains Hélène N'Diaye, member of Generali COMEX.

At GRTgaz, it is more about experimentation offered by its datalab structure. A place outside of the company premises was chosen on purpose to give a unique space for considering submitted business issues in a different way. According to Frederic Mours, head of Datalab of the industrial leader, accessing data is a strategic challenge as most of the teams are on the field.

On the other side, Swiss Life launched a more transversal organisation with its team members. DataLab members keep their original missions and work simultaneously on AI and data science matters. The Datalab depends on the COMEX, data strategy being directed by Cynthia Traoré. The SwissLife DataLab members meet regularly to exchange on various data issues.

At TF1, data issues are handled inside of the innovation center named MediaLab. Every 6-month start-up batches answer different issues of this TV channel core business. Every Business units own a start-up with whom a project has already been set. “ The first stake is always operational, which means data is always at the center of our business concerns”, underlines Florence Caghassi, Open Innovation Program Manager at TF1.

To conclude, a structure entirely dedicated to data promotion has to be a pilar of innovation strategies of big accounts. It can be a place, a team, transversal skills … But it has to help new solutions flourish, thanks to data-based exchanges .

“Not only is data exploitation about digital, but also a mindset that comes from more team interactions. “

Nadège Vignol, Head of Data Innovation Lab Engie

* source Nick Heudecker, Gartner 2017

Sources : Le datalab de Generali France // Faut-il un data lab pour innover ? Data Analytics post // Un data lab pour l’innovation, entretien avec Nadege Vignol

Data & Innovation : listen to the business users!

 

CIO-France Online Conference : Joining forces on Data democratization

 
Cio Online - Etude "Comment exploiter au mieux les données au service du business ?"

Focus on Renault testimonial

Some feedback on the collaboration between Renault and the start-up askR.ai. >> original article (in french - Front commun sur la data : retour d’expérience de Renault <<

 

Top/down strategy request appropriate intermediaries

Since 2014, Renault has clearly announced its digital transformation ambition. A corporate vision that matches the “top/down strategy” definition of a company strategic orientation pushed from the top. The following years saw the creation of a new entity called “Renault Digital” to internally develop apps and softwares to answer business units’ needs. In this transformation process, data exploitation is key.

Jean Dumas, BtoB Customer Data Manager at Renault, started working on data issues in 2015. This is the reason behind his participation in the CIO France conference “Joining forces on data democratization”. As a CRM project manager, his experience feedback is all the more relevant as he is now responsible for making these positions more "data autonomous".

Choosing a manager from the field, in order to find new data tools and to make sure they are adopted, enabled a down-to-earth approach. It is about overcoming daily issues that face Renault distributors and that Jean Dumas has also faced.

 

Accepting feedback (or the absence of feedback)

Jean Dumas closely monitors the implementation of a new data portal for his teams from 2015: Fleetb@se, a dashboard helping operational teams accessing key information on their local market.  This tool was a first response to business users needs.

 However, our head of project quickly became aware that this new tool was not used enough.  He started looking for a solution that would make users more autonomous without entirely rebuilding an already operational system. It only requires a simple, instantaneous access that can be used by different types of users: business users tend to give up on a tool that is not fast enough.

Jean Dumas turned to askR.ai in 2017 to add our data bot to the portal: users could ask their questions and receive an answer instantaneously, without having to dig for the answer in FleetB@se. The number of connexions were multiplied by 7 in a few months !

Thanks to its relevance, the intuitive solution sparked off a “wow effect”. After all, every car dealer’s dream was simply to get a precise and instant answer to a question they had in mind when opening FleetB@se… Question that can now be asked to askR.ai, 24/7.

 
askR.ai interface
 
 

Develop data exploitation with a double approach

This CIO-Online conference testimonial proved that it is complex to offer business users a solution that meets both their daily issues and the company strategic orientation. When talking about data and users, different needs combine with multiple approaches and numerous tools. Last but not least, every piece of data has a different value according to its accuracy and its timing.

Decision making comes from data, which makes easy access to data for business users paramount. That is why IT and business teams have a common stake: IT is in charge of framing technical resources, and business teams, the only ones able to decide on their true needs. This, even within the framework of a top-down transformation strategy.










Finalist to ICC START UP AWARDS at EDHEC!

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Our team is proud to be a finalist of the ICC Start up Awards that will take place on November 29th in Lille.

Every start-up will in pitch in front of a specialized jury to prove he is the best innovation in retail this year . See you there !