Data is the basis for analytical decisions in companies and supports companies in the development of innovations. Which framework conditions companies have to create and which levers to apply. […]
According to Gartner’s market researchers, data analysis is one of the game-changer technologies emerging from the pandemic with 36 percent-well ahead of artificial intelligence with 24 percent. After many industries and companies had to cope with changing conditions last year, data promises more orientation in the corporate strategy. In IT, data paves the way for innovation, for example, in order to better understand the customer and his needs. However, the work with data is influenced not only by the information itself and the analysis technologies, but also by certain trends and framework conditions:
1. Chief Data Officer (CDO): While the CIO (Chief Information Officer) takes care of the technology assets, the CDO (Chief Data Officer) is responsible for the information assets. As the person responsible for data prioritization and orchestration, he advises decision-makers in the company with the insights gained from data and ensures that these are also reflected in the company strategy and all business-relevant decisions.
2. More data competence: Data literacy does not mean training every employee as a data scientist. It is more about a general understanding of data: what significance do they have for the company? What are the specific goals of data analysis? With comprehensive data expertise, a company ensures that all stakeholders have a common goal. This avoids individual departments forming data silos and pursuing different, possibly competing data strategies.
3. Data democratization: Access for all: With data competence, data democratization is also gaining in importance. If all departments of a company are to develop an understanding of data, it must be as easily accessible as possible for as many employees as possible. This also means that everyone always has access to the same current data. This is simplified by the Cloud. Visualization software makes the data understandable even for non-data scientists and makes it easier to handle.
4. Learn to learn with data: Successful companies have become accustomed to making their decisions based on data. This can start quite simply on the company website: Procedures such as A/B testing call for a “positive failure”, i.e. quick tests of what really matters to the customer and what does not. This supports permanent learning based on data: Which variant of the check-out process brings more sales, which configuration tool is preferred?
5. Data Privacy is more important than ever: Given the central role of data for business success, it is all the more important to protect it, as well as the privacy of those who provide it – customers, for example. Data leaks can not only damage the reputation of a company, but also seriously disrupt its operation, for example, when internal databases are exposed to external attacks and hacked. At the same time, breaches of privacy, which companies can easily undermine in the face of complex framework conditions such as the GDPR, often lead to costly warnings. Against this background, data privacy is a very complex and not an easy topic. Companies should therefore anchor it centrally in their strategy right from the start.
6. Post-Cookie: Own data is becoming increasingly important: Cookies are often used on company websites to record and analyze visitor data. However, due to stricter privacy policies and technological changes in web browsers, cookies will soon be history. This will fundamentally change the way companies advertise themselves and their portfolio. Self – generated data-i.e. data obtained outside the major search engines and social media channels-as well as information provided by business partners, for example, will become increasingly important for one’s own communication and sales channels.
7. Data scientists remain coveted: Even though data analysis software can help with the handling of data, data scientists are and remain extremely valuable for companies. Accordingly, they are courted on the labor market. Employees with expertise in data analytics are increasingly becoming a success and competitive factor for companies. In the future, managers in companies will increasingly base their decisions on the analysis and interpretation of existing data material, which is ideally also correlated with available external market data.
Oliver Henrich, vice president of product engineering at Sage, comments: “Data will replace the proverbial gut feeling. This is not just about analysing and forecasting developments in the financial markets. For manufacturing companies, for example, this also makes it possible to predict the demand for certain products very accurately. As a result, not only material and personnel requirements can be predicted accordingly, but also market trends can be anticipated unerringly. The result: competitive advantages over competitors. Intelligent software systems undoubtedly make an important contribution here. But it also requires the right specialists who draw the right conclusions for the strategic orientation and operational processes of your company from the automatically generated analysis results.“
* Bernhard Lauer is a freelance editor of dotnetpro and is responsible for the section Basic Instinct. With Visual Basic, he has been programming privately since version 1.0.