Data & Analytics

What’s behind the term “data & analytics”?  

The term “data & analytics” generally refers to data processing for operational purposes as well as data analysis

• for managing and controlling business processes
• for optimizing business results
• for improving the user experience on an ongoing basis
• as the basis for effective decision-making

In the context of the continuous digitalization process, numerous other terms relating to the topic of “data & analytics” have become commonplace and are frequently used together or as synonyms, although they sometimes differ significantly with regard to what they cover.

Data management

Data management covers all measures, procedures, and concepts where data is prepared and made available in order to optimally support the various business processes of a company. Data management is therefore also a precursor to data analysis. For efficient data management, aspects such as data quality, data security, data protection, data governance, and data lifecycle management (DLM) play an especially important role.

Data analytics

In the field of data analytics, multifunctional analysis systems are usually used, which allow a tangible depiction of the correlations and interdependencies. Identifying information from the selected datasets serves the purpose of verifying models and hypotheses. Through the targeted analysis of data, informative statistics and visualizations can be compiled, thereby making accurate interpretations possible and creating a solid foundation for business decisions. Data management and data analytics are frequently subsumed under the term “business intelligence”.

Data mining and data science

In contrast to data analytics, data mining describes a process where a computer is used to search large amounts of data for patterns, correlations, and cross-connections that have been hidden so far. The aim of data mining is to analyze the existing data from different angles and collate it into useful information using clustering or classification techniques.

Data science also deals intensively with classification and pattern recognition. However, unlike data mining, its focus is on statistical and algorithmic methods. Based on the statistical evaluation of large datasets using AI or machine learning, a data scientist tries to derive detailed statements about probabilities and make reliable predictions about future occurrences. Through such well-founded and data-based forecasts, data science provides a better foundation for recommended actions and business decisions.

Data & analytics for successful digital marketing

Especially in digital marketing, the efficient use of data & analytics is an important leverage factor for positioning your company on the market in a much better way and gaining major competitive advantages. Whether used to identify target groups, for programmatic advertising, or to create personalized offerings, data & analytics gives you an almost 360-degree view of users and customers so that you can understand their behavior and needs in much greater depth. By drawing on this practical information, not only can you optimize your marketing campaign efforts, but you will also be able to tailor your marketing measures to the personal preferences of your customers.

E-commerce particularly benefits from data & analytics

The user experience and customer journey play an incredibly important role in e-commerce. More often than not, even the smallest of details can tip the balance here in terms of whether customers opt for your products or those of your competitors. Dynamic pricing based on real-time analyses is just as worthwhile an approach for this as the optimization of the various touchpoints, for example by analyzing website traffic or navigation patterns along the customer journey. Thanks to data & analytics, modern competitive analysis tools also provide you with valuable insights into the strengths and weaknesses of your competition and let you gauge suitable market opportunities as well as possible market risks.