Top reasons why “Big people” matters more than “Big data”: lessons from F1.

I’ve read thousand of articles about Big Data and the benefits it can bring.

But there are no so many articles or blogs talking about what kind of capabilities are needed in an organization to drive this kind of initiatives.

Fernando Alonso, winning Spain Grand Prix, Valencia 2012

What matters most, the F1 car and the power of the engine, the driver, or the engineering team that builts and maintains it ?

I feel some people is oversimplifyng the matter and only asking about what make of F1 do we need to buy ? Is it a Ferrari, a McLaren, or Mercedes ?

Shouldn’t we be talking about what are theh capabilities we need to drive that car ? And moreover, even “modify” or adjust our car to better suit the driving style of our driver ?

Shouldn’t we be talking about what are the capabilities of our engineering team ? It is only about mechanical engineers, or do we need also aeronautics, and materials experts ?

Building on this alegory, I find many times the focus is only about if the right tool is SAP Hana, Tableau, Tibco, Qlick, or Microsoft BI (to name a few), and we forget about the two most important questions:

  1. Who are our users (driver) and what are their specific needs (driving style)
  2. What are the capabilities that we need to develop  or adquire to drive this kind of program (the F1 engineering team)

I will be sharing some ideas regarding the capabilities needed in an INDUSTRIAL environment to drive this kind of programs.

Organization and capabilities

In my opinion we need an interesting mixture of profiles and experiences. We need people with:

  • Operations and process experience. These will be in direct contact with the user (or will be the user themselves) and will be in charge of giving context to the data.
  • Automation and Equipment knowledge. This profile will be able to extract the meaningful data and also put the contect relative to the equipment and phisical processes
  • IT knowledge. Will be in charge of the technology solution (the car; or with a more sharper focus, the “engine”)

Now my questions are :

  • Do we need a team core team in which ALL members have this Super rich  (You can imagine what the “S” in the middle stands for)
  • Is just combining the diversity of single-discipline specialists under a single umbrella enough to achieve our goals?
  • Or…given that it is almost impossible to have a bunch of “S” people, we can look to the  hybrid profiles that appears in the intersection of every two areas: Data scientists, Industrial IT experts, and Process Controls engineers. Do we need to  combine these dual-profiles under a single team ? Is that enough ?

As a summary, I think if we want our company to be sucessful (win the race), we need to:

  • Select the right technology (the engine).
  • Assemble a well diverse team, with a rich mixture on each one of them (the engineering team).
  • Design, configure and adapt the tools (the car) to the specific needs and style of our user (the driver).


And the final reflection…, what matters most ….Big data …or Big People ?

What do you think ?


(Please leave your feedback / comments in the linkedin page)






Industrial Big data or Smart data ?

Is Big Data applicable to Industrial environments ?

  • Do we really need a “big data set”  with strong analytics to extract knowledge, or we only need a small set of data ?
  • Do we need to create a strong structure (data model) or can we have non structured data (“data lake”)  ?

A friend of mine shared his experience in a recent project. They decided to apply Big data to an Industrial manufacturing process.

  • They installed a big server with a lot of processing power and a huge amount of memory.
  • They install best-in-class analytical engine.

They selected a coating process. The process is similar to this:



In this process, the product is heated by a hot air current, when the setpoint is reached then  spray “paints” the product, and when the thickmess is the appropiated, the spay stops and the air is cooling down the product.

The first analytics was intended to establish the robustnes of the process and the influence of the average temperature in the thickness coverage.

The outcome was the average temperature was 50°C, well below the specs.

Of course, the user rejected inmediately the validity of the result.

They reviewed the calculations and brought a  data statistician on board, that concluded the data was not following neither a normal nor a chi square distribution.

After all this, they decided to bring a Process Expert on board. He said the data has to be structured in three independent phases


Now, the “Smart Data” basic statistics could be easily applied to each of the phase, with very basics statistical tools and obtaining very quick results.

The conclusions of my friend were:

  • The number of data needed for extracting meaningful conclusions in industrial processes is normally very low
  • The data has to be structured, because the process itself is heavily structured, and following phisical laws
  • And the most important: If you do not know your process….don’t ask a big data engine, ask to your process experts !


What do you think ? What are your experiences on this field ? Please leave a comment