Digital transformation, data science and intrapreneurship culture

10 November 2015 Updated at 23 May 2019

It’s a fact: digital transformation is here and certainly more than a hype term. The number of neologisms and new concepts attests it: uberization, data scientist, innovation labs, Chief Data/Digital Officer, Agile Marketing, growth hacker…

In many aspects, old concepts do only have an evolution, natural considered the context:

Data science? I used to study statistics and was thinking 20 years ago I would be a statistician. I discovered I had to be named data miner and had to investigate some other areas (algorithmic, IT, optimization). Nowadays I am a so-called data scientist. And yes, one expect me to have new (additional?) competencies like hacking, storytelling, map-reduce paradigm, functional programing for Scala and so on.

Real time CX? This concept is far from being recent. Only difference is that applying marketing dreams is now a possibility: using data science to allocate in real time best action/offer based on optimization under constraints. We do deploy Real Time platforms (Nice, Pega, Oracle RTD…) and observe effective ROI.

On the other hand, there is disruption. Frontiers between sectors are more and more fuzzy:

  • Cross mobile phone and Internet: got an iPhone capturing many market shares so quickly.
  • Add powerful search algorithms with a touch of Social and network (collaborative filtering): justify Netflix and Amazon success who just make CX and in particular discover process become seamless.
  • Housing + a touch of digital: bypass real estate broker (and thus capture part of the fee…), adjust to customer interests based on price, location, characteristics. Cherry on the cake: cross data from notaries, land registry, geo-localization and local statistic, gently ask your data scientist to crunch and model and you got houses prices prediction with impressive accuracy. Not surprising end user is so delighted by your online cost effective solution.
  • Digital Automotive: prevent anxious transactions with salesmen, price negotiations, required investigations and sales processes. Have a look at Beepi and be sure we will see other digital transformation in automotive sector.

Anticipating what will happen for your business is crucial. Starting to work on (re)inventing it should be CEO top priority. Question: how to start with this ambitious project? We think one of the key factors is to develop an intrapreneurship culture. Encourage your own employees to participate to it.

Arrival of start-ups that will uberize you is your recurrent nightmare? Here are some practical advices that may help you sleep better:

  • Set-up innovation lab and nurture data scientists: if it’s all about data, let’s have people play with your own data. Put everything at disposal without suffering from the pain of costly DWH projects; regularly feed with new (external) data.
  • You don’t want to invest in innovation labs? Externalize: leverage data science emerging communities with hackatons for instance, sponsor those communities.
  • In both cases, you need an internal Analytical Innovation leader. Identify this guy and give him access to key resources. Beginning with part-times and small projects may be sufficient and fruitful.
  • Encourage employees to have interactions with the outside world (conferences, meet-ups, hackatons…). If they have interactions with start-ups, they will identify for you emerging trends. If they broaden their activities, they may imagine for you the new cross-sectorial synergies.

Have a proactive HR approach to identify intrapreneurs, hire and retain them. Answer to innerpreneurs needs and expectations and you will have done a giant step (innerpreneur, a term coined by in 2007 by Ron Rentel refers to the one who basically is an entrepreneur who will use his business to find personal growth and fulfilment (creatively, spiritually, emotionally) and create social change).

 

Eric Lecoutre
Eric Lecoutre
Business & Decision

More than 15 years of experience in Analytics with a broad knowledge of statistical and machine learning methodologies.

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Commentaires (4)

Mick LEVY Le 02 December 2015 à 20h41
Indeed ! It's clearly a global trend. We clearly notice that because all the countries in which B&D is represented are launching projects of this type.
Yannick Le 02 December 2015 à 15h35
Nice input
Exactly the topic I'm working on in Asia...
So it looks like a global trend :)
Yannick Even
Eric Lecoutre Le 13 November 2015 à 14h08
Hi Lohic,

Thank your for your feedback.

I will try to answer to your question.

I certainly don't want to reduce the importance of a good DWH that indeed has to play a central role withinn (big) operation as providing a single-truth version of data, serve as a basis for operational applications, and in particular for reporting at every level of the organisation, also concentrate actionable analytical assets.

Building a DWH is a big project that has to be carefully planned, requires detailed functional and technical analysis, need long discussions with all parties to end up with a someway definitive business glossary. Data and corporate governance are necessary with this regards.

Note that technically, there are some methodology that allow agile DWH, I can for instance mention Data Vault modeling.
Nonetheless, to me DWH are yet not enough agile for data science. You need to evolve faster, add quickly new data, don't care about data glossary and so on.
Hence my opinion that data scientists will certainly prefer data lakes and play with data without waiting for IT implementation.

That said, for companies having a good DWH, this one is certainly of high interest to begin to play with data from the organisation.

Kind regards,

Eric
Lohic Beneyzet Le 13 November 2015 à 8h20
Hi,

Nice post. I totally agree with you that CEO should have a special focus on innovation nowadays.
Playing with and discovering data could help to achieve this.
You write "Put everything at disposal without suffering of the pain of costly DWH projects".
Can you maybe explain this in more details?
From my experience dwh's don't need to be costly, fullfill a specific rol in management information landscape and can be great jumpstart for data science project.

Keep up the good job.

Kind regards, cordialement,
Lohic Beneyzet

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