The Ingredients of Data Innovation

This is the second article based on Data Experts interviews, which aim to give insights into the world of Data. By looking closer at the heroes who are bringing data innovations to life and at the work they do, I hope to create a mutual understanding between Data Experts and organisations so that the potential of data can be grasped and better leveraged.

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The Ingredients of Data Innovation

Data Innovation

When taking a deep dive into the world of data, one thing is certain: the ultimate goal is to create something new, something better, something faster. In other words, for companies innovation is most important, regardless of whether your goal is to pioneer new processes, user experiences, products or services.

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Venn Diagram for Innovation
  • Business: it’s viable, it would be good for business (profitable).
  • Technical: it’s feasible, it’s possible to actually develop this idea.

It’s important to create a common understanding regarding the limits and possibilities of data technologies.

Feasibility

“70% of the work we do as data consultants is related to cleaning and preparing data.”
This illustrates the fact that problems often arise due to misguided expectations about what can be done with data. It’s important to create a common understanding regarding the limits and possibilities of data technologies.

Any technology is the means to an end, an enabler for new possible solutions.

Viability

“What we did differently (compared to our competitors) was to actually start putting innovations into the business. We knew not all the databases and infrastructures were perfect, but in our opinion it was essential to lead by example, showing the success of the innovations we came up with.”
It’s safe to say that by nature Data Experts are perfectionists, therefore, for them it’s a challenge to try to innovate by failing fast (trying out new ideas, even if there is a chance of it being unsuccessful). This data expert said trying out new ideas was an important step to quickly learn the limitations and possibilities of the data they had. In other words, the first step they took was finding out what is feasible with the data you have NOW by trying to develop data innovations. Starting small by developing more accessible “low hanging fruits” was something that worked for many of the Data Experts I spoke with.

“…. was the lowest hanging fruit and therefore a good first use case…. because of the easy-to-understand business case and awareness of the problem”

“The most important skill of a Data Scientist is storytelling”
This quote came from a Data Scientist who together with one other data expert had to compete with other innovation teams with different expertises. As an internal “start-up” these data scientists aimed to get budget from the business’ traditional business lines. This shows that there is often a lack of awareness or understanding of what data experts are capable of. For this data expert, communicating a successful data innovation was an essential part of his job.

  • Logistic regression is legally approved, so it takes less time to put that into production.”

“We work closely together with several stakeholders and identify ‘ambassadors’ for projects.”

One topic that came up again and again was: How can we prove the value of a certain use case of data innovation? By experimenting, proving a hypothesis, performing a quick scan of the data, creating an MVP (Minimum Viable Product) or presenting a business case? It seems like ensuring that Data Experts communicate their concept and prove the value to the business is in many cases an important milestone in any data innovation project. Some take a long time (up to 12 weeks), while others are much quicker (an A/B test lasting less than a day).

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Challenges & Solutions

Cooking up data innovations

When comparing the accounts of data experts working for companies where data innovation is still something new (and mysterious) and companies with more data experience (the data mature company I wrote about in my THE ORDER OF DATA EXPERTS ARTICLE), there are many important differences. The most glaring is the awareness of the capabilities, the level of understanding of what is possible and the trust that these “data mature” businesses show in their data experts.

  • Data experts need to develop their communications skills in order to make sure their “abstract” science is understood by others who may be less technically-minded or experienced with the topic:data;
  • Businesses need to trust their data experts (and their skills) and to be more willing to invest both time (to understand possibilities and limitations) and money (the right skills for the right job) and;
  • Technology is an enabler, not the goal. Treat it as such: collaborate, don’t isolate.

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