The Ingredients of Data Innovation

menonthemoon
Jun 7 · 9 min read

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.

The Ingredients of Data Innovation

by Robbert de Kruijff, Experience Design Consultant

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.

In the context of innovation one thing that stood out for me was something all Data Expert interviewees loved to say: “we work in multidisciplinary teams”. As a former innovation student this rang a bell immediately; innovating is a discipline that requires multiple capabilities and skills as shown in the Venn diagram for Innovation below.

Venn Diagram for Innovation

The Venn Diagram for Innovation is also appropriate in the case of creating something new, better or faster fuelled by data. This diagram illustrates the three “ingredients” needed to create such innovations really well:

  • Human: it’s desirable, people want it (whether they know it or not).
  • Business: it’s viable, it would be good for business (profitable).
  • Technical: it’s feasible, it’s possible to actually develop this idea.

During my talks with data experts, I arrived at several insights relating to these three categories. You will see that some of the insights could just as well have been categorized differently, but that’s okay, as we have seen that innovation is a multidisciplinary process anyway.

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.

“At first we thought it was going to be an IT party, we soon realised that other stakeholders would need to be included in the process.”
Don’t forget, for innovation technology is a tool, an enabler, not the goal. Any technology is the means to an end, enabling new possible solutions.
Think of data as a raw material, like wood for instance. To create something valuable you must envision a chair or a table. What is mostly happening now is that the data broker industry is “burning enough wood” (a $200 billion industry, WebFX.[1]), just by selling data without using it to create valuable solutions.

“… was the lowest hanging fruit and therefore a good first use case. Not because of the technical feasibility (which is not the biggest challenge at the moment) but because of the easy-to-understand business case and awareness of the problem.
This Data Scientist showed that the biggest challenge is not whether a fancy algorithm or model can be made. He said finding a case that is relevant for the business and showing that this solution is brought to life is more important.

“Our robotics (automation) team did demonstrations for the rest of the business to show how the technology works and what it’s capable of.”
Communicating what is technically feasible is often harder than it sounds. By offering hands-on demonstrations and tutorials, this data expert increased the visibility of the data team at his company.

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.

Regarding the approach this data expert’s competitor took, several researches state that many big data projects end in failure because before coming up with innovations, many of these projects tend to (re-)structure the whole infrastructure and clean all the data. In many cases this ends up to be non-viable because the investments and successful innovation is not guaranteed, and any sort of profit (ROI) is not shown for a long time [2].

“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.

“It is always very beneficial to make sure a data team is “on the same floor” as the business practices.”
This Data Consultant stated that Data Experts and Business practices being physically closer to each other helped with a closer collaboration. Close collaboration between data experts and business is the only way to arrive at solutions that are truly valuable and feasible..

“Leveraging data is becoming a cultural challenge as well. That is why we work closely together with several stakeholders and identify “ambassadors” for projects.”
The challenge of convincing people was a major topic which came up during many of my talks with data experts. Data Consultants confronted this by seeking support from others within their clients’ organisations, people who really backed-up the development and implementation of data innovations.

”..I talked to a data expert at another company that only used Logistic Regression. He mentioned 2 reasons:

  • It is much easier to explain to colleagues internally, so the business case is clear and;
  • Logistic regression is legally approved, so it takes less time to put that into production.”

The innovations created with this more “basic” data analytics technique are easier to understand, and for legally accepted methods the implementation is a lot less difficult. If you are a data expert in the situation of choosing use cases, it is good to take into account whether they: appeal to business, are easy to explain or whether they are difficult to implement (or put into production).

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).

For those who are looking for the scoop, here is an overview of the challenges and suggested solutions as mentioned by the data experts I spoke with (including other mentions by data experts which are not quoted above).

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.

In other words for the less “data-mature” companies, the biggest challenge data experts face in creating valuable innovation from data, is the gap between the data disciplines and the business. To close this gap (based on the insights in this article) and thus being able to cook up something from the ingredients you have, I believe;

  • In the end, business experts and data experts must work together. They will have to look for common ground and (by means of examples of successful use cases) based on these quick wins, expand;
  • 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.

Desirability
No, I did not forget the last ingredient from the Venn diagram of Innovation. What about desirability? How does a data team determine whether an idea for innovation is something people actually want? Because, let’s face it, any innovation (data or not) must be made for, and appeal to people. Ensuring desirability means looking for use cases that provide value for your intended audience, it’s the secret sauce to any data innovation.

That is exactly what experience design tries to do; get a better understanding of a company’s audience and ideate solutions that could improve their experience.

I am certain that finding use cases will become an essential part of future data innovation processes, especially if all the “low hanging fruits” use cases have been developed into innovative solutions. The best way to do so is to understand the people you are catering for, and based on this understanding find ways to improve their experience when interacting with your organisation.

A data consultant told me: “If a project is aimed at benefiting a customer, user research is definitely something we do. The Design Thinking team interviews, test prototypes, iterates and interviews the target group again when needed.”

This data expert told me Design Thinking (the methodology for Experience Design) was used to find out the desires of their audience. Taking a deep dive into the people you are catering for will ensure the desirability in any innovation. Simply said, if it will make you money (viable) and it’s possible to produce (feasible), doesn’t mean people will have a need for it.

Isn’t the best way to show the added value of a data innovation, by proving that it would improve the lives of your audience? Whether your audience are employees (e.g. help to simplify their tasks or enhancing their outcomes) or customers (e.g. help to make their process effortless or delight them with a new service or product).

Coming soon… menonthemoon’s Data & Design-services; our take on data innovation and the role of experience design. In the meantime want to know more about experience design, design thinking and menonthemoon? Check out our website or contact us.


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