Which topics related to data, business intelligence and analytics will be in particular demand in 2022? In its annual study, BARC (Business Application Research Center), Europe’s leading analyst firm for enterprise software, once again identified the “Data, BI & Analytics Trends”. What is striking about the results is that there are only minor shifts in the top rankings compared to previous years. We present the top 5 in more detail.
The success of a company depends to a large extent on making the right decisions. And the prerequisite for making the right decisions is, in turn, consistent data that can be used as a reliable basis for decision-making. Much stands and falls with master data and its quality. It provides the structure for understanding and using data. Only through master data do transaction data, IoT data and clickstreams acquire their meaning and context. Harmonized master data contributes significantly to a uniform understanding of data and the interaction of business units, as it enables consistent reporting and data-driven processes. Digitalization is increasingly turning data into a production factor. Accordingly, the need to generate high-quality data and use it flexibly is increasing – not least for new services and products.
The importance of clean, high-quality data is as undisputed as the fatal effects of low data quality. Nevertheless, many companies continue to face enormous challenges in implementing uniform master data management, increasing data quality, and maintaining the level achieved over the long term. The extent of the need for action is demonstrated by the fact that master data and data quality management is ranked #1 in BARC’s “Data, BI & Analytics Trends” for the fifth year in a row.
Ideally, data use in companies today is not project-oriented, but is much broader and more pervasive. A company is data-driven when it makes as many decisions as possible within its own organization based on data. The prerequisite for a data-driven company is that as much data as possible is available to as many employees as possible. The term “information democracy” often comes up in this context. The particular challenge here is that in order for the transformation to a data-driven company to succeed, it must not only take place at the level of individual processes or employees, but must be anchored in the corporate culture.
Quantitative and qualitative data alike should improve decision-making processes and support both operational and strategic decision-making. The goal is to empower employees to actively use data and in this way achieve improvements in their daily work. This results in successful decisions, effective and efficient processes, and new competitive advantages.
Data governance offers an overarching view of data maintenance in the company – independent of the individual systems in which the various data are applied. To achieve this, it is necessary to create a central point within the organization.
As a governance mechanism, data governance defines how business strategy is to be translated into data and analytics, and designates the necessary policies and frameworks for managing, monitoring and protecting corporate data, taking into account people, processes and technologies. This enables companies to derive value from their data. Behind the introduction of data governance must be a clear management decision on how and for what purposes data is to be used in the company.
Data discovery is about identifying relevant regularities, patterns, developments and tendencies from the multitude of existing data in the most efficient way possible. In this context, it is relevant that users can access data from numerous source systems as well as cleanse and enrich the data prior to analysis. Visual analysis or guided advanced analysis is used to explore data records.
Data discovery enables scalable data discovery based on a governed platform so that users can access each other’s data and produce reliable results across the enterprise. Advanced analytics is becoming increasingly important in data discovery. By generating automated insights and pointing out hidden patterns and deviations, the quality of analytics results increases.
Self-services continue to play a central role in the analysis and visualization of data. With the help of intuitive tools, users are able to compile and prepare the information they need on their own so that they can provide their superiors with solid facts as a basis for decision-making. What is changing, however, is the goal associated with the provision of self-service functionalities: Companies no longer just want users to better meet their departmental needs, but also see self-services as a way to democratize data access (see trend #2), create analytics efficiently, and ensure consistent results.
However, the increase in BI self-services should not mean that business users no longer need to rely on designated IT, analytics and BI experts at all. They continue to play a key role in supporting, optimizing and monitoring successful analytics and BI environments.
Digitization once again reinforces the importance of powerful data maintenance, analysis and visualization in enterprises. After all, data and analytics are the elemental core of being able to digitize processes and business models. In practice, companies repeatedly struggle with insufficient data quality. Against this background, for example, the promotion of a data-driven culture is a decisive factor in enabling companies to exploit their full data potential.
The “Data, BI & Analytics Trends” combine organizational and technological aspects. They form a solid foundation for companies to sustainably improve their data handling and analysis. “The overall picture shows that companies are focusing on the fundamental aspects of using and managing their data (...),” says Dr. Carsten Bange, CEO and founder of BARC on the results. “Companies are getting to the root of their challenges (...) and working to build a holistic data-driven culture.”