Data is the "lifeline of economic development" - this is the generally prevailing postulate in the manufacturing industry. But how well equipped are companies in Germany, Austria and Switzerland in this respect? A survey conducted by the market research company YouGov on behalf of Hewlett Packard Enterprise (HPE) among 803 managers - from CEOs to team leaders - got to the bottom of this question.

For example, 37% of all executives surveyed and 65% of board members and managing directors said that their company had no data strategy at all, not even as part of the IT strategy. Half of all respondents and 75% of board members state that their company does not systematically take care of bringing data-based products or services to market. And 66% of all respondents (85% of board members) state that no analytics or AI (artificial intelligence) methods are used in their company, but that analyses are carried out using spreadsheets.

The HPE maturity model

The survey is based on a model developed by HPE that evaluates the data value creation maturity level of an organization along six dimensions - taking into account strategic, organizational and technological characteristics. The lowest maturity level (1) is called "data anarchy" - here, business units manage their data inventories in isolation from one another and hardly analyze the data systematically. The highest level (5) is called "Data Economy" - at this level, there is uniform data management across internal business units and external ecosystems, and the company uses data effectively to create value.

According to the HPE maturity model, the companies and public institutions surveyed by YouGov have an average maturity level of 2.1 - although smaller companies with up to 250 employees have a significantly lower maturity level of 1.7. For companies with over 250 employees, the average is 2.5.

Source of economic growth

In particular, the exponential growth of professional and industrial data is seen as a key source of economic development in the coming decades. This affects all sectors. McKinsey, for example, has calculated that the utilization of data from connected vehicles could generate annual added value of 250 to 400 billion US dollars for players in the entire mobility system by 2030. Initiatives such as Gaia-X aim to ensure that the necessary data exchange between companies is efficient, compliant with data protection regulations and digitally sovereign for all participants.

However, companies can only realize this potential if they meet a number of strategic, organizational and technical requirements. Data value creation requires, for example, the linking and aggregation of data or data insights from different applications, areas or companies. For example, information from the service department can help the sales department to sell more effectively to existing customers. Usage data from sold products helps the development department to better align the next product generation with customer needs.

It is therefore a sign of a low level of data value creation maturity if the data, its analysis and utilization are restricted to individual areas. In the survey, for example, 42% of managers stated that their data is only available in the respective applications - i.e. it is not available in other applications or has to be transferred there via individual interfaces. Just under a tenth of respondents (9%) stated that their company has established a central data hub for all data (or a data-centric architecture) that also includes real-time data. And for 6% of respondents, this data hub also includes external data sources.

An aid

Rainer Peters, Head of the Business Solutions Group at Hewlett Packard Enterprise in Germany, Austria and Switzerland: "There is no shortcut on the path to the data economy, it requires a holistic approach that affects all facets of a company."

© HPE

HPE is now providing an online self-assessment with which companies can determine their own data value creation maturity level and compare themselves with the results of the survey. The detailed results for each maturity level dimension provide a differentiated picture of the company's own strengths and weaknesses. This can be used as a starting point to plan the next development steps for optimizing their data value creation.