Transformation into a data-driven media company

Through our consulting projects in the media sector, we gain deep insight into how media companies deal with data issues. Of course, we often encounter problems that can be traced back to poor data quality and inadequate tools. But increasingly, even in media companies that have a solid data infrastructure and a team of data experts, we observe that working with data does not produce the effects we had hoped for. This is often because the strategic, organizational and cultural prerequisites for this are lacking. Only where strategy, culture, organization and technology interlock with regard to the use of data can data unleash its full added value for the media company.

The efficient use of data has become increasingly crucial to the success of media companies in recent years and will continue to become more important in the future. It is therefore essential to integrate data aspects into the corporate strategy, as they play a central role due to their relevance. The strategy should provide clear guidelines on how data should be used consistently. In this context, the corporate strategy specifies whether the media company should work Data-Informed, Data-Driven or Data-Led and what exactly is meant by this. This creates a uniform objective for working with data.
However, it is not only the strategy that determines the use of data. In turn, data helps to implement the corporate strategy in a data-driven media house. To do this, a data model is used to translate the corporate strategy into a KPI tree that integrates the corporate goals into the daily work of all departments. Strategy and KPI structures go hand in hand here. Only if the corporate strategy is made measurable with the help of data can it also be implemented in a data-driven manner. In addition, data models are only successfully applied and accepted if they adequately reflect the corporate strategy.

Automating decisions based on data is becoming increasingly important in media companies. For example, recommendations for editorial content or the playout of subscription offers are increasingly automated on the basis of data. Nevertheless, the vast majority of decisions in media houses are still made by people. To ensure that these decisions are based on data, it is crucial to embed a data mindset in the minds of employees.
Often, we see teams looking at data analytics, but ultimately still relying on their gut instincts. To trigger these situations, a certain level of data literacy is also required. Employees must gain a basic understanding of data collection and analysis in order to make reliable data-based decisions. In a data-driven media company, it is not enough for only individual experts to have this knowledge. Instead, the knowledge should be available to all relevant decision-makers.
Data-driven decision-making also requires completely different decision-making processes. A testing culture must be established in the media house, in which hypotheses are tested in A/B tests and backed up with clear figures. It is no longer the best-argued ideas that prevail, but those that can show a demonstrable improvement in tests. This approach can significantly accelerate development processes by replacing long evaluation processes with short tests. Finally, the automation of selected decisions mentioned at the beginning is something that only works if it is culturally accepted in the media house.

How efficiently a data team can work depends to a large extent on where it is organizationally suspended. When a new data team is set up, it is often located where there were already data experts before. This can be either in IT or in the most data-driven business unit (often marketing). If the data team is located in the IT department, it quickly falls into an internal service provider role. In this role, the requirements and use cases are defined by the business units and the data team merely implements them. However, because data topics are complex, the best solutions are developed in joint cross-functional teams of business experts and data experts. These teams achieve the best results with agile ways of working.
For a Data team to be most efficient, we recommend that it be organizationally suspended directly under the executive management team as a central strategic business unit. As the data team grows, this evolves into a hub-and-spoke organization. Here, data experts are added to the business units as permanent contacts for the central data team.

Of course, the best strategy, culture and organization are of no use if the foundation is missing - the data itself. For this, it is important that all relevant data is collected and made accessible in a central location. Data lake, data warehouse, data lakehouse, data platform are terms that describe this place (with minor differences in the specific technical setup). As long as different data is distributed in data silos in the media house, no overarching analyses can be made.
In addition, different departments in the data silos look at different data and measure their contribution to the success of the company differently. This inevitably leads to a situation where the work of different departments is not coordinated. To avoid this, a data model for the entire media company should be developed based on the central data warehouse. Based on this data model, all departments look at the same key figures and the goals of the individual departments interlock in the overall model. Only the dovetailing of the various data models and key figures enables efficient cross-functional collaboration.
Finally, it is crucial that access to data in the media company is as broad as possible for all employees. Only if the analysis and use of data is intuitive and uncomplicated will the use of data become established in the media house. Therefore, the development of self-service analytics tools such as interactive dashboards is essential. These tools enable employees to analyze and use data independently.

Highberg has a unique combination of strategic competence with experience from over 1000 consulting projects in media houses and the technical expertise of an in-house data team. This makes Highberg the ideal partner for the implementation of data strategy and organization projects in the media sector. Here, we not only create strategic concepts, but also implement the technological basis directly ourselves. Together with our colleagues in Highberg, we also have an expert network for agile business transformation.

We are happy to be at your disposal for an exchange.