Innovation in the Field of Data Analysis

Data analysis has become an indispensable cross-sectional technology. In addition to the real evaluation of data, the acquisition of knowledge, the recording of contexts and abnormalities as well as the support of decisions play a central role. In this way, you can use data analysis to help companies optimize their business processes, plan, and risk and impact assessments.

Thus, the field of application of these methods extends beyond the mere evaluation of business data and now includes forecasts relating to requirements and possible profits (based on raw materials and goods, finances and personnel) as well as recommendations for pricing.

Analytical methods are increasingly forming the basis for data-driven business models. An example of this is the model of a product as a service based on a usage fee.

Closer customer loyalty

The most important application areas include, for example, sales and marketing. The analysis of data from CRM systems, information about the behavior of visitors on web pages, emails from customers and the content of social media form is the basis for a comprehensive understanding of the customer, his requirements and his needs. Those findings can be complemented by information on how customers use products and services. The more extensive the image of the customer is, the more targeted and individualized companies can make their offers across different channels. The use of cross sources data analytics is an exciting topic for companies, especially for the retail sector.

Basis for applications of the Internet of Things

Data analysis is also the base for applications within the Internet of Things (IoT). Data from machines as well as sensors are collected, prepared and evaluated. Other data sources that are relevant in this context are events such as messages about faults or continuous data streams within production plants or other video or image data. Those data can be used to monitor the quality of production and predictive maintenance of machinery and equipment.

Also, the Internet of Things creates a new form of data analysis: instead of centrally collecting and evaluating the data, local analysis takes place in the networked device or the machine (edge analytics).

Evolution of data analysis by AI

Artificial Intelligence (AI) techniques are driving innovation in data analysis. Automation in the integration, evaluation, and visualization of data is just as important as the independent recognition of patterns and contexts (part of machine learning). On the one hand, this opens up new possibilities in the application of data analyzes for different business processes (higher automation and reduction of human interaction) and at the same time opens up new application scenarios. An example of this is autonomous driving, which is based on AI-based analysis in real time.

Who drives the use of data analysis in the company?

Often specialized departments within a company drive the applications of data analysis. These include, for example, financial controlling, which has always been one of the most intensive users of reporting and data analysis.

In terms of industries, the departments focused on the energy or media industry excel as drivers. Compared to other sectors the manufacturing industry has a unique characteristic. In this sector, the company management is more involved than in other industrial segments when it comes to the use of data analysis.

As the study results show, the larger companies are already working on evaluations of information about the use of their products and services. Also, large companies use geodata more often than smaller companies do. Such data can be useful when it comes to the control of vehicle fleets as well as for the planning of transport routes.

Find the appropriate approach

The fields of application for data analysis are broad. But not every company finds it easy to identify suitable application scenarios. There is no shortage of data at all, but rather a bottleneck in ideas about how existing information can be used to the benefit of the business. For this reason, it can be useful if the IT department does not work out deployment scenarios on its own, but does so together with the departments and the management and maybe even with support by external consultants. A good starting point for this is the areas in which the challenges and the competitive pressure are highest, for example in terms of increasing the efficiency of processes and customer loyalty.

In addition to the technology, the prerequisites for successful implementation include the qualification and further training of employees. The latter must be able to use the systems, but also to adapt or expand to ever-changing requirements. Also underestimated is the importance of data management, which includes ensuring data quality. 

PAC study of innovation fields in Germany

Cloud, Big Data, IoT, Artificial Intelligence, Blockchain - how far are German companies in the implementation?

The market analysis and consulting firm Pierre Audoin Consultants (PAC) asked the above question on behalf of Arvato Systems during 2018 within a telephone survey on application areas, investment plans and attitudes to the currently dominant topics of digitization - cloud, big data, IoT, Artificial Intelligence and blockchain. In addition to IT decision-makers, the management and specialist departments of German companies were interviewed to look at different perspectives.