Data analytics as an integral part of the business model
Companies usually collect and store customer data for a specific, predefined purpose in order to then process it exclusively for the same. In addition, IT systems independently generate further data that is not a priori aimed at later processing, so-called metadata. Thus, classic log data is not processed productively as a matter of course, even though a great deal of additional, implied information is hidden in it. With data analytics, it is possible to extract this implied information, interpret it, and use the resulting insights. For this, in addition to a clear data strategy, the necessary methods and expertise are of particular importance. Only with a clear idea of the intended goal can data analytics be used in a targeted manner. The following applies: the more precise the better.
Data analytics already in versatile use
Established methods in the area of predictive analytics are already in widespread use here to better understand customer wishes and thus be able to offer suitable and tailored products to both strengthen customer relationships and increase sales per customer.
Away from customer interaction, data analytics enables the calculation of business metrics based on data that has already been collected. In times of stricter regulation and disclosure requirements, this is crucial to be able to generate reports cost-efficiently. For example, it is possible to calculate carbon footprints from receipts and invoices found in accounting records.
In addition, the data available to systems across all industries continues to increase in the course of digitalization, while its analysis is still under development at many companies. Yet it is precisely this data that offers the opportunity to meet increasing regulatory requirements more efficiently, especially with regard to sustainability aspects, and to strengthen the business model.
While data analytics is usually already part of the core business model of young startups and fintechs, the potential is usually not yet fully exploited by established companies. The main reasons for this are the lack of expertise required for the analysis of complex data structures as well as security/data protection aspects when using personal data or cloud solutions, which are usually the most cost-effective option for analysis.
In this context, data protection aspects are often discussed in connection with digital ethics (big data ethics). This is understood to mean the question of the limits of what is morally acceptable. An artificial intelligence must also be “taught” to act in a value-oriented manner by clearly defining both the selection of data and the intended goals and, in particular, limiting them on the basis of a code. Companies that do not take this so seriously are often negatively criticized and often violate legal framework conditions such as those that have applied in Europe since 2016 through the GDPR. In the end, a higher turnover stands in contrast to the social values. At the same time, it is no longer possible to pursue a “sales at any price” strategy, especially through the application of ESG, which also includes social issues in addition to the environment and governance.
Classic Data Analytics and Neural Networks
In general, there is no analytical method that can be applied equally successfully to all scenarios. Rather, one must distinguish which data form the basis and which goal one is pursuing. If one understands the data or the data model well enough, classical statistical methods are suitable for many analyses, as they are faster and more reliable in the context of prior knowledge. The calculation of average values with standard deviation or interpolation using regression are the simplest examples. If one is looking for new correlations or is dealing with large data sets that update at short notice, one can use modern multivariate methods such as neural networks. In this way, for example, clusters of interest can be formed, which are necessary for efficient cross-selling.
Data governance of the highest priority
The biggest challenge in data analytics is the quantity and quality of the available data. While the former increases by itself over time, the quality can even deteriorate in the worst case and thus reduce the informative value of the analysis results. A clear data governance concept that ensures data quality in the long term, defines standards for effective use within the organization, and minimizes risks in the process is the key prerequisite for successful data analytics.
Consileon offers data analytics from a single source
Consileon was founded with the attitude of being a reliable, sustainable and thus long-term partner for all its customers. This is reflected, among other things, in the fact that we do not develop strategies that others have to implement. Instead, we are so convinced of our results that we implement them ourselves at any time. Particularly in data analytics, where many different aspects come together, from strategy and IT development to governance and compliance issues, we at Consileon can create real added value for our customers and enable a transformation toward digital business models.
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