Originally, Business Intelligence (BI) was developed to enable executives and managers to make data-based decisions. The analyses performed for this purpose involve collecting and processing data from internal and external systems, and planning and performing analyses. The results are then visualized in tools and applications.
For some time now, the scope of BI departments has expanded. Today, they are not only responsible for strategic analyses, but also take care of many different operational processes, such as ad-hoc financial reports. These additional tasks require new tools. Traditional data warehouses, which used to be the central tool of the BI department, are no longer sufficient to efficiently handle the current tasks. The exploding number of queries and also their diversity requires the implementation and use of different tools. A modern data tool stack supports every employee in getting the right data at the right time in the right tool. This is the only way to master all of the challenges experienced.
One component of a modern data architecture to address these challenges is a data catalog. Implementing a modern data catalog accelerates operational processes and enables far-reaching, comprehensive research and analysis. It also facilitates data access and the development of mutual understanding between different departments of the company.In this article, we present three key challenges faced by BI departments and solutions.
Typical challenges of the BI department
Departments talking at cross purposes
One common problem is a lack of understanding between the BI team and different departments. Without a central point of information, BI analysts struggle to understand department specific' approaches, requirements and business terminologies. One typical example for a problem-causing, highly ambiguous term is "KPI," which has various meanings. Additionally, the business department often cannot autonomously explore the data basis for the requested analysis. These aspects not only cause a time-intense analysis preparation but also misunderstandings, delays and avoidable meetings and calls.
By creating transparency for all departments about relevant technical and business information, a data catalog offers an ideal solution to decrease the lack of understanding. Using a catalog, analysis preparation will be enormously simplified. For example a business glossary (part of a data catalog) contains the central definitions of relevant terms, so employees can look up terms there if they are unclear. Direct data access via the catalog will further reduce the time required for data preparation by up to 70%.
Interdisciplinary collaboration as a mammoth task
The second problem which companies frequently face is a lack of efficiency while conducting comprehensive analyses, especially when several BI departments are involved. Such analyses become enormously time-consuming and cost-intensive due to the different tools which are used. Many of these tools do not offer open interfaces and make use of lock-in effects. Data integration therefore becomes more complex. In general, there is often a total lack of awareness about the infrastructure of all parties.
During the analysis planning and preparation, a modern data asset catalog provides the user with an overview of used tools and databases. By open APIs, it facilitates the collaborative use of data sets and data sharing. Especially for testing processes, which play a significant role in cross-system analyses, the combination of a data catalog with a flexible data integration environment boosts the efficiency.
No one wants to document!?
The third point that should not be missed here is the lack of documentation and decentralized storage of reports.One of the most common challenges is that knowledge is in silos. It is not available to those who need it. Both analyses performed and reports available are either.
a) individually (in the worst case)
b) stored and documented at workgroup level (at least somewhat less siloed).
The reasons for this are manifold: high documentation effort, lack of guidelines, lack of a suitable infrastructure, ... . The consequences are duplicated analyses, low efficiency and errors in the further use of assets.
A modern data catalog offers a solution to overcome the challenges that decentralization brings. It creates a central point of truth. By mapping relationships between data and data sets (data lineage), it becomes clear where data came from and how it was transformed. The automation offered by a modern data catalog is an advantage here: the reduced effort required for documentation improves its quality enormously. Overall, this increases the trust of all users in the data in several respects.
Broad added value - not only for the BI area
The above examples clearly show how BI departments can overcome their challenges with modern data catalogs and a modern tool stack. In addition to the possibilities shown, the use of a data catalog results in numerous advantages for the entire company, such as improved data quality, increased employee satisfaction, increasing data democratization and so on.
In addition to the added qualitative value, an acquisition also pays off monetarily for companies. Correction costs, data replacement costs, and process costs are 5-10 times higher than the cost of a cleanly designed and well-managed process. Other frequently mentioned advantages of a data catalog are
- a higher number of analyses performed with higher quality
- increased timeliness due to increased employee productivity
- management decisions based on comprehensive, holistic and up-to-date data and its analysis
A data catalog is a central building block of a modern data architecture. For the BI sector, a data catalog plays an increasingly important role for a wide range of use cases, far more than the three examples mentioned.
To ensure that such an investment is worthwhile, we recommend that you consider all the key features of a data catalog when making your selection. Be sure to choose a vendor that maps your individual use cases. Important aspects may include integrations to existing tools, the level of automation, or support for exploratory work on data.
If you want to learn more about possible benefits, read the article What is a data catalog and what is it used for? and get in touch!