Have faith in your own employees for innovation
Do you want to innovate? Then do not appoint an expensive innovation manager, but trust your own people and use smart internal structured data that you can trust. Moreover, understand that the most important work of a data scientist is to structure data.
Everyone is talking about digital transformation, business intelligence and innovating with data. Smart Profile is also innovating with all the intelligence we have collected over the past 25 years. We have a lot of B2B data available from various European countries, in particular the Benelux. The challenge is to keep that data correct, complete and up-to-date and to extract value from it in an innovative way. As a result we are increasingly shifting from data supplier to data partner and analyst. The basis for this shift is the development of the necessary products and services that have been fully developed internally by people from various roles and disciplines.
No hierarchy
A big mistake that organizations make is the appointment of a special innovation manager. You must then ask yourself whether you can innovate successfully by someone who merely ‘innovates’. A manager who is only hired to tell people: you have to do new things? With the reason that the organization has to move forward and therefore has to come up with something new?
I don’t believe in that, you have to use the knowledge of the people in your organization. In my view, an organization that successfully innovates is an organization where employees, in whatever role, are given the space to innovate. So a few years ago we started changing the ‘operating system’ (the way we work together here). I wanted to get rid of the hierarchical corporate culture in which the director needs to come up with where everything has to go. I try to lay the responsibility with the people. If they see things that could be better within the organization, they must have the freedom and the possibilities to initiate change.
By choosing Holacracy, every employee in the organization is a ‘sensor’ for what could be improved. Moreover, everything is allowed, provided it does not endanger our organization. Innovations must of course not contradict our purpose: “Realize Potential.”
Young people
Offering freedom through the choice of self-managing teams does not work for everyone. Some people duck away for more responsibility. You need people who want to take responsibility, are curious and are not afraid to do something that may never have been done before. It must also be possible to make mistakes in such a new organizational culture. We therefore mainly want to bring in young people who do not think ‘linearly’. Older people tend to want to solve problems as they always have. Young people look at things differently. I see people of 26 years come up with solutions that make customers super happy, but which I hadn’t even thought of myself.
In addition to less hierarchy and more young people, we also deliberately seek contact with the outside world. What happens outside of our organization in the field of data and innovation? For a number of years we have therefore been investing in the Jheronimus Academy of Data Science (JADS) in Den Bosch, a joint venture of the province of Noord-Brabant, Eindhoven University of Technology and Tilburg University. For us, that is a kind of ‘outboard motor’ that makes our ‘ship’ more agile. JADS is a breeding ground for young data scientists and helps us stay ahead with the latest knowledge in data science.
Smart Profile Labs
The collaboration between Smart Profile and the universities brings education and practice closer together, and fits in with Smart Profile’s aim to provide market data as well as offering smart data science applications. The university institutions in turn make grateful use of the (very) structured data from Smart Profile.
Everyone is talking about data, but the biggest challenge is good and structured data. A data scientist spends around 80 percent of his time on data engineering: getting data “right” so that you can trust it. That you know what you have and what you don’t have. That you can analyze and model from the available data, but that you know exactly what you are doing. You can use the most interesting analyzes on data, but if your source is not correct, you make the wrong decisions.
People like to be seduced by sexy terms such as data science, predictive analytics and machine learning. But the practice of data science is primarily a lot of data preparation, structuring and engineering. This is a huge challenge. That is why we have had data scientists in our organization for some time and of course also the necessary analysts. At the moment we are mainly recruiting people who are really trained as data scientists or are still in training. Mathematicians in their twenties. To reinforce that even more, we recently launched Smart Profile Labs: a laboratory-like environment where people can experiment with data and set up things that do not have to yield money immediately. In Smart Profile Labs we innovate together with our customers in the field of market intelligence, data management and demand generation.
Complete, structured and up-to-date
The recruitment of young, talented data scientists, but also the partnerships with universities can easily start because Smart Profile has structured data. Data science is largely about the completeness, topicality and coverage of data. Everyone is talking about data science and the predictive behavior of artificial intelligence, but I think we should go back to basics: what kind of data are we actually looking at? What can we do with it? Is this data correct? That step is often skipped.
