The year 2020 began a few weeks ago. It is the perfect time for companies that want to develop digitally, to find out about trends around data and to make a successful start in the new year. Here you will find my estimation of which impulses will determine the data world this year according to new projects, events and experts.

Strategic importance of data

In my view, the most important development that has already emerged in various industries in previous years is the increasing strategic importance of data that more and more companies are recognizing. This recognition is a decisive factor in being able to make the best possible use of corporate data within the company. The trend here is clearly moving away from a number of isolated applications towards a strategically valuable company-wide data solution. With the help of a holistic strategy for the use and exploitation of data, companies can use data in a targeted manner, for example to minimize risk, improve competitive advantages or their market positioning.

Data literacy and a data-driven corporate culture

Strongly linked to the first trend of the increasing strategic importance of data is the second trend: data literacy and a data-driven corporate culture. As a strategic pillar, the correct use of data and data-driven decisions based on it must be firmly anchored in the company and in the minds of employees. Data literacy means that employees build up competencies around data, such as collecting, adapting or analyzing it, and that the handling of data becomes a matter of course. “Self-service” and independent work with the data are the ultimate goal here.

It is therefore important that employees are trained accordingly and understand the role of data in the company – a task for management. After all, data literacy is a decisive factor for the success of digitally operating companies and should be available to the responsible employees of the departments concerned.

Data governance and data catalog

With the help of data governance, a holistic management of the data used is to be achieved in companies. Defined guidelines ensure the quality and security of the data. In addition, data governance helps to better understand compliance with legal requirements throughout the company.

The main purpose of written guidelines is to ensure the quality, security and processing of data according to specific standards. There are clear rules on how data is handled and who works with which data. The costs for the administration and storage of the data can be optimized and penalties and violations can be prevented by the structured concept.

The introduction of data governance in the company is a continuous process that should be reviewed regularly. Part of this process is the definition of the roles of the responsible employees and the definition of access rights. In addition to terminology, a so-called data catalog helps to record who is responsible for which data and what access rights they have. This creates a company-wide view of the data and a central control mechanism for the data strategy.

Data management and data quality

Stable data management is the basis for high-quality data. The need for efficient data management solutions is also constantly increasing in importance. Because only when based on correct data can the right decisions be made in companies. Data from different systems and in different formats must be merged correctly throughout the company to ensure consistency.

Data quality plays a decisive role in every step of the process, and the specifications for data acquisition, processing and evaluation must be adapted accordingly. With targeted solutions, existing data warehouses – which are already in place in the majority of companies – can be optimized and set up with significantly higher performance.

In addition, applications such as data warehouse automation are becoming increasingly important in order to accelerate and automate the entire process of data extraction, transformation and loading. In this way, data quality can be increased and costs and time expenditure can be reduced significantly.

Data science

Data management forms the basis for the correct use of data science applications such as machine learning or artificial intelligence in companies. These terms continue to occupy the various management levels of companies. At the same time, these buzzwords are increasingly turning into clear use cases that make the added value of the applications concrete. For example, data quality can be greatly enhanced using machine learning. Incorrectly recorded data can be automatically recognized and corrected accordingly. And in the new year, too, new fields of application will emerge and the continuing trend will show where artificial intelligence brings what added value in terms of data.

2020 will be an exciting year. The trends show that important fields of action in the fields of data management, data analytics and data science are increasingly improving and offer ever more concrete added value. I am curious to see which trends turn out to be the strongest and most profitable.