If you are not already digital today, go digital now. The focus is always: the data. Word has already gotten around “good” & “bad” data here – or relatively usable and unusable data. Of course, everyone wants the “good” data. But how do you get this and ensure that the people in the company can draw the correct conclusions from it?
On the one hand, the goal is a flawless database that provides the necessary decision-making basis for AI and every other business decision, from strategy to warehousing and product range management. Such a SPoT (single point of truth) can only be achieved if everyone in the company pulls together and submits to a uniform wording, set of rules, and data collection principles. On the other hand, the employees must also be enabled to use the data sensibly. And that requires data governance.
What Is Data Governance?
Data governance is a subcategory of data management. It deals with the distribution of responsibilities, the rules, and structures that have to be implemented to get to a SPoT and then be able to handle the data. This master data management is a responsible task because all data-based company decisions and thus the company’s success depends on it.
Data governance is required wherever data from multiple channels are brought together, where numerous employees are responsible for the collection, and where automation of high standards is necessary for a flawless database. Therefore, every company that works “multichannel” to make data-based decisions should deal with data governance.
Data Governance Belongs In The Boardroom
Data governance means that employees are assigned responsibilities and responsibilities while others are deprived of them. It prioritizes the relevance of different channels while keeping in mind the well-being of the entire organization and the various departments that work with data.
To create the acceptance necessary for implementation measures and get an overview of the needs within the various departments and company branches, the impetus for data governance and the definition of responsibilities must come from “above.” Data governance belongs in the boardroom; only she holds all the strings in her hands and can enforce responsibilities.
The End Is The Beginning: Set Goals
“Begin at the beginning and then continue until you come to an end!” – this recommendation from Lewis Caroll’s “Alice in Wonderland” could hardly be more wrong at this point because if you want to pull the strings in data governance, you have to think from the end.
What goals do I want to achieve? You determine which data is required, which channels are relevant, which departments are involved more and which less. You can choose the right path and orientate yourself if you know your destination.
Lean Data Management
In any case, master data management belongs at the beginning of data governance. After defining the goal, the selection is essential here. Which channels are relevant to my goal? What data is required? Which tools are necessary and which are superfluous? How can I relate and link these to each other?
Less is more here because I can only achieve my goals with high-quality data. But data seems to be available everywhere for free – high-quality and usable data takes work and requires maintenance and specialist knowledge.
Every tool, every channel, and every data record causes costs. It must therefore be checked for its benefit because it is only good if the added value justifies the charges if it is future-proof and contributes to the quality of the master data.
An essential part of data governance is “legislation.” It establishes the rules by which data is defined, collected, and aggregated. The employees responsible for the data supply from the various channels orientate themselves on it. It is a benchmark for quality assurance and a guideline for interpretation and analysis.
The starting point is your database and its categories, which must be precisely defined. If the database is to work, one language must be spoken company-wide. Because if sales are restricted before returns in online marketing, but after returns in the catalog business, then contradictions arise that can ruin the basis of all future action. Enforcing rules here is essential and serves as an orientation for the individual channels because the different, often non-transparent data systems pose new challenges to this uniform definition with every data query.
But who should operate which system, responsible for which area, and monitors compliance with the standards? Suppose data governance has to be initiated by senior management. In that case, they also need a government team that takes over the direction of the ministries and leadership that monitors quality in day-to-day business.
Only if you keep an eye on which area he is responsible for can you be sure that there are no gaps because a place was overlooked for which no one feels accountable or overlaps that block each other. Effective action is then possible, and the team can rely on each other when everyone knows where their tasks lie, and there is a clear contact point that can issue assignments and, if necessary, intervene to control them.
When everything has been defined, people know their areas of responsibility, and the goals are clear. Milestones must be defined: what is to be done by when, by whom, and who controls the implementation? Only those who know these points can check the successes of data governance and tie them to cornerstones. Classic project management is required here.
Only those who define structures and determine responsibilities have an overview of the collected data, locations, and duties of their administration. This alone enables reliable data protection of customer data – a fundamental prerequisite for the trust that customers place in companies. Reaping Success: The Benefits of Data Governance
The benefits of good data governance are extended, so it’s worth investing time and energy. Here is an overview:
Trust is another benefit of good data governance because Customers who can rely on their data being handled with care are more willing to entrust it to the company. And your employees must also be able to trust the data to make effective decisions.
This data is essential for further business development because less and less of it is freely available due to the cookie directive and the exclusive data policy of many platforms and companies – usually known as walled gardens.
If there is trust in the data, responsibilities are defined, the employees have the necessary data competence, processes run more efficiently, and goals are achieved more quickly. In addition, many unnecessary extra tasks are eliminated – such as B., to investigate why the online turnover is different than that in the backend.
Even if data governance initially seems expensive – after all, it needs those responsible who coordinate – it already saves costs in the short term. With the resulting data clarity, many decisions can be more targeted, and AI can function more smoothly. Unsuitable investments can be avoided due to incorrectly controlled advertising campaigns, incorrect stock management, and strategic planning errors.
Data governance is much more than just a new buzzword. It is a central part of the data strategy, a way to compensate for falling data volumes, and the lever that frees companies from the constraints of walled-garden data distress.