Data Design Thinking: a method for understanding how to extract value from data

Access to quantities of data that would have been unthinkable only a few years ago has brought about a radical change in the way business is done. It is not only a question of new tools placed at companies’ disposal: the very modalities of the decision-making process are turned upside down in order to exploit to the full the potential that the data collected offers. Data design thinking is a new model of approach to running a business that starts from the need — inescapable today — to exploit the widest available base of data in order to elaborate accurate short- and medium-term scenarios while reducing the risk component to a minimum.

What is meant by data design thinking

Data design thinking is a decision-making process that exploits company data in earnest. Naturally a good database is required, archived in the form of smart data, that is, in such a way that it can easily be integrated. Often finding the data you need is simpler and less costly than one might think: much of it is available online in free databases curated by institutions (examples are the data portal of Istat, that of the government or of many Italian regions, as well as many databases from private projects such as Wikipedia’s). These are open-source platforms that make it possible, given the right tools, to integrate the data at one’s disposal with further data capable of widening the administrators’ vision of the many aspects that might influence the outcome of company policies.

The extract solution from LinkedData.Center — a company leader in the field of corporate data management — for example allows you to download data from any web source and structure it in a format compatible with the standards your company uses. Once the desired source and the type of information required have been identified, the tool is able to retrieve the useful data automatically, delivering a great saving in terms of time and, consequently, of company costs.

What is needed and how it works

The data design thinking model is structured around the work of compact teams of three to six people, preferably chosen from those with the greatest capability, experience and knowledge of company and market dynamics. Immersed in a creative environment, they must have at their disposal the tools and materials suited to developing forecasts starting from the available data and to measuring their outcomes according to the various models of operation of the market in which the company moves.

Figure: the Data Design Thinking process.

The process is structured around three key moments:

1. Interviews. In this first part the attempt is to identify the problematic aspect to be addressed, seeking to analyse what is not working and to understand which areas of marketing or of the product the problem affects. At that point it is possible to proceed to identifying particular classes of data that are judged likely to be useful in the following phase.

2. Workshop. Possible solutions are devised. With data-analysis tools at hand, alongside substantial masses of information, it becomes possible to evaluate the costs and benefits of the proposed solutions in real time and accurately. This gives a clearer picture of the boundaries within which solutions must be found, and allows the costs that bold solutions might entail to be anticipated as accurately as possible.

3. Follow-up. Finally it is necessary to provide for moments in which to sum up the hypothesised scenarios and channel them into an action plan. The most important aspect is that this must provide for continuous tests to verify the correctness of the models used in the Workshop phase. This leads to a final product — whether an industrial product or a new marketing policy — that is effective and above all certain.