The strategic importance of Business Intelligence (BI) is growing for more and more companies. BI methods and technologies allow organizations to better plan and control their business processes and the market through greater transparency. Many companies have now defined a BI strategy and taken organizational measures to improve its implementation within the organization. Current trends come from very different areas. Some can be traced back to a mega-trend: The stronger consumer orientation (consumerization) of IT. This phenomenon describes the alignment of applications and access paths to applications with the experience of users in their private lives. BI applications are influenced by this in very different aspects, e.g. interface design, expectations of query speed and provision of possibilities for collaboration as known from social networks or the use of mobile devices to access BI systems.
The mobilization of business intelligence applications currently concerns in particular the presentation of dashboards for management on smartphones and table computers. For users, the support of the specific operating options of the respective devices and the adaptation of the user interfaces to the usual standards are of particular importance. However, the implementation of these possibilities currently requires the development of specific applications for the respective end devices. An interesting option on mobile devices is the linking of the business intelligence application with other applications such as map services for geographical analyses. Business Intelligence thus becomes more operational, i.e. more closely linked to actions and transactions.
The traditionally observed use of BI for tactical and strategic decision situations has been supplemented in recent years by the increased use of business intelligence solutions for operational purposes. The aim here is to present and analyse information at short notice that relates to a specific process and requires a short-term decision. In terms of process-oriented BI, key figures from running processes thus become the object of monitoring or analysis. The prompt delivery of information, but also the immediate reaction by the user or by automated rule-based decisions are essential here. Technical innovations such as event processing, streaming databases or very fast analytical databases often allow the implementation of operational BI on a larger scale.
The buzzword "self-service BI" is one of the currently strongest trends towards the increased assumption of tasks by users. The driver for this is not only the consumer orientation of IT, but often the sheer necessity for more autonomy and flexibility at the user's workplace in order to cope with the dynamics of market events and business processes. Most frequently requested are possibilities to modify reports and dashboards or to build them yourself. Advanced users still appreciate the possibility to modify data models themselves, e.g. to simulate structural changes in the aggregation of data. The ability to add your own local data to the reports provided in the BI system is also an increasingly common requirement. Traditionally, all these tasks have tended to be performed by central IT or BI departments. There is a clear tendency here for users to do more of it themselves in order to increase their flexibility and agility.
In many companies, reporting is now well supported by BI tools. Enhancement through advanced data analysis should now help to create added value from the often extensive and costly data collections. While reporting mainly compiles and distributes existing key figures, analysis methods aim to generate new information from the available data. The classic OLAP analysis (Online Analytical Processing) has been supplemented by other forms of analysis in recent years. While OLAP analysis looks at the key figures of a company in their hierarchical structures and dimensions, the user is supported in quantity-oriented analysis by options for restricting result sets using the descriptive attributes of data records. The visual analysis facilitates the acquisition of the properties of data sets and the identification of interesting data areas by means of various graphical display formats. Methods of data pattern recognition (data mining) are also increasingly finding their way into operational data analysis. In addition to methods for segmentation and association of data, prediction and simulation (predictive analysis) are increasingly in demand. Predictive analysis plays a special role especially in connection with the expansion of BI towards planning.
Planning complements the view of the historical development of the company in reporting as well as the timely consideration of currently running processes in operative BI by looking at the future development of the company. Planning processes, content and tools must be adapted to the increasingly dynamic and unpredictable market and business development, which is currently the biggest challenge.
Big Data refers to the highly scalable integration, storage and processing of polystructured data. The core idea is the use of very different data sources for analysis, also and especially of data that does not accumulate in traditional financial or ERP systems. New forms of technology such as analytical databases, the Hadoop Framework with its various components around the file system HDFS and NOSQL databases ("not only" SQL as a collective term for all non-relational databases) are used here.
Many trends change the technical implementation form of BI or extend the methods and procedures of data analysis. Report visualization focuses on the presentation of data in reports. The aim here is to increase the informative value and information content of reports by using standards in the presentation of data. Rules of information design such as those propagated by Rolf Hichert, for example, play an essential role.
Organizational measures are to coordinate the various initiatives for more BI in the company and institutionalize the absolutely necessary cooperation between IT and business departments. In terms of organizational structure, many companies have now set up a BI Competence Center (BI CC) or Center of Excellence (BI CoE), which are already being expanded or restructured on the basis of experience. In terms of process organization, a more agile BI is to be achieved by interlinking technical and professional competence. Agility, i.e. a high speed to react to changes, is an essential goal for organizational changes in the BI area. However, trends such as self-service BI and Big Data cause some difficulties in ensuring information governance in the company. Consistent and quality-assured processes and content for reporting, analysis and planning can only be achieved with appropriate governance. Rules for data access and processing must not only be defined, but also possibilities for enforcing and monitoring compliance must be created. Organization and governance thus form the foundation and the framework for action to be able to implement the various trends in Business Intelligence.