Discussions with consumer and market insights teams often reveal a certain duality between insights from market research data in the narrower sense (primary data from surveys) and insights from other data sources (social media monitoring, panel data, online performance, etc.). In times of data-based value creation, it seems cumbersome and inefficient to keep different data sources and thus also knowledge reservoirs separate from each other and available for analytical approaches. However, processes and structures that have developed over decades cannot be replaced by a single, definable initiative.
What is needed is an innovative approach that takes into account the specific circumstances of market and consumer data. How can this be achieved, especially in medium-sized companies with limited resources?
In large companies, the activities and results of linking the added value that can be generated from data are evident: for example, by pooling expertise and methods in data intelligence centres, where analysts, data scientists and AI specialists work together on tools and methods for the systematic use of data in all areas of the company. The Consumer and Market Research department is also rapidly differentiating itself in this respect: Data scientists are becoming and are permanent members of teams, and the skills and fields of activity of these departments are evolving so that the duality mentioned above is weakening, data sets and knowledge are converging and value-adding processes are being standardised.
Establishing and expanding such structures is resource-intensive:
Anyone who wants to support and manage such processes responsibly is aware of the uncertainties associated with setting up a data lake or warehouse and implementing analytical solutions, for example. Without a targeted initiative, there is hardly any capacity in day-to-day business to drive forward projects of this kind stringently and with the necessary strength and resources.
Market researchers are inherently part of data-driven teams. They are responsible for and capable of leading these projects and implementing change. They are ambassadors for data literacy and data democratisation, i.e. they create access to data and analyses for many people from different areas of the company. The aim of these processes is to gain knowledge and, on the basis of data, to create value so that as many people as possible with different perspectives can contribute and utilise it in the best possible and useful way for companies. Hackathons are a good example: Giving access to people with expertise can lead to creative and efficient solutions.
At the same time, democratisation requires companies to have sovereignty over their data. What may seem obvious can actually be a hurdle. This is because providers of external data are currently trying to bind companies to their own analytics platforms (and thus keep them in a “closed loop” or “lock-in”) by imposing restrictions or obstacles on the use of data. In collaboration with other internal stakeholders, such as category management, it makes sense to develop a sustainable strategy that preserves independent use, i.e. data sovereignty. How else can flexibility be created that ensures adaptation to future requirements, predictable expenditure and scalability?
Break down silos: In many organisations, data cannot yet be used efficiently because individual departments own it or the quality of the data makes it difficult to access.
It therefore cannot be emphasised often enough that data harbours a potential that goes far beyond what the current (‘traditional’) owners generate in the company. Collaboration between teams and different data sources can often generate significant added value.
A single point of truth: The creation of a uniform and binding database ensures that all stakeholders have access to the same data and that the analyses and processes based on it, e.g. for reporting, are transparent and comprehensible. This also includes automated data cleansing and harmonisation (pre-processing). This increases data quality and work efficiency.
Automation of data processes: In order to generate findings with added value, processes should be digitalised/standardised where possible (database queries, recurring reporting). On the one hand, this guarantees the validity of the analyses. On the other hand, market researchers can concentrate on content-related and advisory work.
Scalability: Organisations, markets and technologies are in a state of flux. Data applications and strategies should therefore always bear in mind that future requirements and needs can be met.
Market and consumer analysis teams can be given targeted support to set up new systems. The focus is on empowerment throughout: the existing content expertise is optimally complemented by the (automated) consolidation of various source data, standardised processes and on-the-job analytics training. As a result, the team emerges from the transformation phase stronger, more creative and more efficient and can develop its full potential. Working with data is finally fun again.