Never before our economy has produced such an enormous amount of data. An increasing number of companies want to exploit this digital resource: in order to analyze their markets, for example, or to make their customer service or factories more efficient. In the field of Business intelligence (BI), a lot has happened in recent years. Modern BI tools such as Tableau, Power BI or Sisense are capable of combining and evaluating data of different types from different sources, including artificial intelligence (AI). This has great advantages:
- Boosting knowledge: Production data, delivery processes, customer preferences and purchasing behavior – along the entire value chain, a massive amount of data is being accumulated. Companies can utilize this information, for example, to reduce their electricity consumption or to make more attractive offers. But often this isn’t done consistently. BI tools make it possible to tap and link internal and external data sources in a comparatively simple way. For example, a manufacturer can include weather data into an analysis in order to optimize energy consumption.
- Fast overview: Let's assume department A uses PowerPoint for internal reports, department B uses Excel and department C Word tables. On quarterly basis, everything has to be merged, which is rather tedious and time-consuming. BI tools put an end to this patchwork process and replace it with standardized reporting. Management can access relevant key figures in the form of dashboards. The tools even offer real-time analyses.
- Uncomplicated use: Once a BI solution has been set up, any and all authorized persons in the company can use it to create analyses on their own, without extensive training (self-service reporting).
- Minimizing errors: The BI tools usually import data directly from the primary sources, which minimizes duplication errors. Optionally, AI ca be used to identify and correct input errors, such as misspellings.
- Greater efficiency: Preparing data manually produces a high workload, reducing time for actual analyses. Standardized, largely automated BI reports can free up capacity, for example in IT: Instead of supporting the business departments, developing elaborate action plans, IT specialists can turn their attention to more productive tasks.
Four success factors
To deliver the desired results right from the start, the following aspects are decisive from our experience:
Convince leaders: Probably not all managers will be enthusiastic about making changes to their reporting. The IT project will consume resources and employees will have to be trained. You should therefore define the added value very carefully, ideally per department. One of these benefits, for example, is the option to view current reports whenever it suits you – e.g. while traveling or between meetings with customers.
Involve stakeholders: The job of top management is to define the reporting objectives. In the next step, the project team and the reporting departments have to determine which key figures are best suited and which data can be included. Without this alignment, the specifics of different areas may not be reflected in the analysis. Here’s a negative example: Department X only reports projects in the unspecific category "other", as other categories do not fit their activities. Another reason for close alignment: Automated BI systems also allow much more detailed analyses that can be programmed individually for different departments.
Agile implementation: Together with key users, the project identifies data sources that can be quickly integrated and visualized. An important goal is a "wow-effect", which will make users in pilot areas enthusiastic for the tool. Step by step, the project team increases the analytical complexity; test users from different areas provide feedback at short intervals. This way you ensure that development is heading in the right direction and that the tool will be quickly available.
Thorough training: BI software manufacturers usually offer standard training only. Such lessons are usually not sufficient as they do not reflect specific user needs: How are terms within the tool defined? What data is being used? Ideally, the training is organized by specialists from the project team who will also provide user support after the go-live.