ROLES AND BEST PRACTICES
Smart use of data is one of the most important ways to have your company grow. It sometimes appears as though ‘disruptors’ like Uber and Amazon have a monopoly on this. In the Netherlands, quite a few data-driven companies are already active as well. Coolblue, for instance, which maps customer needs very accurately. With more creative entrepreneurship and a structural competitive advantage as a result. But countless other companies also appear to be quite able to generate value by using data. Especially when it comes to the Internet of Things, devices that are used daily are becoming ever smarter and more communicative. This opens new doors for innovations and additional services.
Many managers rightly wonder how they can best convert their data into business value. How do they arrive at a convincing business case? How do you bundle the required data? And how do you use it to affect the business operations? The road to a data-driven organisation is a long one that stands or falls with culture. As well as with the right specialists and technology. Fortunately, many user-friendly tools for data analysis and visualisation are already available.
THREE (LEADING) ROLES
In previous projects, I noticed that three types of experts are actually needed: the Data Wrangler, Data Scientist and the Data Artist.
Data Wrangler – this is the one who takes care of identifying, qualifying and making the data accessible. They ensure that the data can be retrieved from systems so it can be translated into applicable insights.
Data Scientist – also known as ‘the sexiest job of the 21st century’. These are the people who connect large amounts of data from different sources and make sure the right conclusions are drawn. These insights help the business.
Data Artist – how can you share the obtained insights in an effective way so that employees can use them properly? This is where the data artist comes in: the specialist who creates the charts, infographics and other visual tools that help you understand complex data. And encourage you to take action!
A side note is appropriate here: domain specialists, such as marketeers and logistics professionals, are also increasingly expected to be able to derive value from data.
FROM TURNING ON TO USING
In addition to IT solutions, data-driven organisations are also about culture, specialists, leadership, processes, and of course, the courage to experiment. In other words, a tough challenge on the organisational side as well. At Abecon and Breinwave, we find it normal to help clients discover and experience the added value. Preferably in an environment in which it is possible to conceive and try new things in short iterations. But we can also take it further. With a good approach, you determine the difference between ‘turning on’ and actually ‘using’ data as the main asset of your company. After all, innovation is never over.
1. Leadership – The management must express a clear vision and also have relevant knowledge themselves. Data as a starting point rather than an end goal. This leadership cannot be delegated to the lower regions of the organisation.
2. Believe in data – It is important that the entire organisation is convinced of the importance of data (analysis). The belief that data must be at the heart of the organisation in order to provide customers, suppliers, employees, IT systems, ‘things’ and ecosystems with insights and information from there.
3. No silos – In order to have data become the beating heart of the organisation, you must connect systems, processes, people and ‘things’ in a smart way. This requires a system that can communicate with many other applications and takes on the controlling role in managing the databases. A major challenge appears to be thinking and working in solos: these hinder an integrated approach.
4. Architecture – A data-driven vision is based on a central data architecture and ‘one single point of truth’. This requires the basic information to be organised properly and be broadly available via integration with web services. All this with the data strategy as a roadmap for future data needs.
5. IT security & privacy protection – The protection of security and personal data is very important and requires continuous attention. The same applies to the ownership and the quality of data.
6. Start with the customer and the business issue – Make contact with the customer, sincerely and from person to person. Not a one-sided monologue, but a dialogue as part of personal relationships.
7. Experimentation – We often see that people first think of processes that directly add value to the business, such as marketing. But there are also less obvious processes where gains can be made, such as predicting maintenance to machines. That is why in our innovation workshops, we stimulate people to continue experimenting.