Why data governance is so important
“Data is the raw material of the 21st century,” German Federal Chancellor Angela Merkel pointed out in 2016. And she was certainly right. Yet the crux of the matter is that high quality data must be available (data governance) in order for companies to optimally use this raw material. Only then can experts analyze and interpret the data in order to make strategically relevant decisions (data science). At least, that is the theory. Prof. Axel Polleres, Academic Director of the recently established Professional MBA Digital Transformation & Data Science at the WU Executive Academy, explains why companies will not be able to do without either, data governance and data science, in the future and how these two very similar phenomena differ in practice.
There is no way around a rather recently coined concept when talking about digitization, data use, and information management in an enterprise: data governance. The topic was even ranked among the most important business intelligence trends for 2020. But what exactly is data governance?
Data governance can support companies by forming a framework for the strategic management of business intelligence and providing rules for data management. This way, data governance substantially enhances data quality, helps ensure compliance with legal and other stipulations, and is the prerequisite for successful risk management. This means that data governance is a must if a company seeks to effectively and purposefully collect, analyze, and utilize data across all departments. Such a process facilitates daily routines, prevents risks, and lowers costs. In other words: data governance is a route planner revealing a path through the data jungle. It makes sure that roles, responsibilities, processes, standards, and rules with regard to data management in a company are defined.
Slowly but surely, we can see the differences between data governance and data science: data science is the interdisciplinary field exploring how new findings can be drawn from data. Among other disciplines, it encompasses IT, mathematics, and statistics. Data governance and data science complement each other. For Prof. Axel Polleres, Academic Director and Head of the Institute for Information Business at WU Vienna, there simply is no way around data governance in corporate environments anymore: “Often, data science projects fail because the companies are not adequately prepared. The data quality is lacking, or sometimes nobody knows where data is stored.” Even outstanding data scientists cannot clear such hurdles. What it takes, is data governance.
Prof. Axel Polleres
For this reason, we will offer a new short program on this topic at the WU Executive Academy starting in spring of 2020. A short program on data science is already available. Moreover, students in the Digital Transformation & Data Science MBA will in the future be able to elect data governance, blockchain, strategic foresight, and other topics as additional modules.
While training in data science rather focuses on competences and skills required for the analysis and interpretation of data and the decisions based on analysis results, education in data governance is about developing the skills to control data ahead of these analyses and establishing an adequate framework for successful data management in companies. “Frequently, merely identifying the data landscapes and the aspects covered by them already makes a huge difference,” Prof. Polleres explains.
Data governance can boost quality and security in multiple ways. For one thing, it guarantees that data is actually used to guide corporate decision-making instead of merely serving to confirm decisions already taken. A process of continuous scrutiny and control ensures an objective evaluation of data as well as its adequate use. So how do data science and data governance complement each other in daily business life? “Successful data governance is a prerequisite for data science – many times, data governance creates the very basis enabling an analysis that can produce verifiable results in the first place,” Prof. Polleres explains.
Data governance, he further elaborates, is important in all industries, but it is particularly key in highly regulated fields such as the financial sector. Talking about data privacy: handling private data is particularly sensitive, which is why rules governing this field are so important. Moreover, data minimization is an important principle of the General Data Protection Regulation (GDPR), i.e. data may only be collected and processed to the extent required for the respective purpose of use and must subsequently be deleted. “This also requires suitable procedures that need to be set up,” Prof. Polleres says.
And what will experts for data governance be called in the future? “So far, there is no established name; some call them data stewards,” Prof. Polleres says with a little smile. What is key is that companies realize how important data governance is, he adds. “It is not necessary that one person alone handles this task.” Right now, it is all about creating awareness of this crucial topic.
For more information about the Professional MBA Digital Transformation & Data Science, please click here.