Advanced Analytics is still a trend topic. It can form the basis for new business models, more attractive products and services or be used for process optimization. However, it is often unclear what is already feasible and what is unfortunately only desirable. But how are advanced analysis methods actually used today? Which companies predominantly use Advanced Analytics? How far along are the individual industries? Which use cases are actually implemented? The results of the latest BARC Advanced Analytics user survey provide answers to these questions. The annual study analyses the feedback from more than 250 participants from the DACH region. This time, differences between companies that are in the prototyping stage and those that are still predominantly in the operationalization stage will be highlighted.
Different software used in prototyping and operationalisation
The most commonly used tools for implementing advanced analytics are business intelligence platforms and data discovery tools (69%). On the one hand, these offer integrated statistical libraries, but on the other hand, the direct integration of open source development languages such as R or Python is also possible. 43% of companies rely on open source development languages such as R or Python for modeling and 20% on advanced analytics platforms. If the tools are grouped into commercial and open source tools and the users are segmented into those that are mainly concerned with operationalization and those that are mainly in prototyping, clear differences in use are discernible. While companies that are predominantly involved in the prototyping of advanced analytics solutions use over 70% of open source technology, only about 50% of companies do so in operationalization.
Errors in data management are the main technical problem
Personnel development, implementation and external consulting are a priority
As the implementation of advanced analytics projects progresses, the shortage of business analysts, data scientists and data engineers becomes clearer. Companies that are already in the process of operationalization are investing more heavily in their staff expansion (42% compared with 31% of companies in operationalization). Companies still in protyping invest more in implementation (41% versus 32% of companies in operationalization). This is understandable as these companies are still predominantly in the first half of the data mining cycle and have to solve the challenges of setting up data access, preparing data and developing data mining models.
Get to know further strategic, technological and organisational approaches as well as best practices for the productive use of advanced analytics at the Data Festival on 20 and 21 March 2019 in Munich (https://www.datafestival.de) and take the opportunity to network with other participants, exhibitors, experts and like-minded people and exchange ideas in moderated workshops.