Why does the BI department need a data catalog?
Initially, business intelligence (BI) was developed to enable executives and managers to make data-based decisions. During the analyses carried out for this purpose, data from internal and external systems is collected and prepared, analyses are planned and executed and the results are visualized in tools and applications. For some time BI departments have expanded their responsibilities. Nowadays, they are in charge of not only strategic analyses, but they are also taking care of many various operational procedures, such as ad hoc-financial statements. These additional responsibilities require new tools. Traditional data warehouses, which used to be the central tool of the BI department, now are not anymore sufficient to efficiently cope with the current tasks. A modern data tool stack, which supports every employee to receive the right data at the right time in the right tool, will help to overcome almost all experienced challenges.
The rapidly increasing flow and amount of data and information has a significant impact on all areas related to data, especially on operational questions. The exploding number of queries and their variety requires the implementation and use of a wider variety of tools. One component of a modern data architecture to tackle these challenges is a data catalog. The implementation of a modern data catalog speeds up operational processes and enables far-reaching, comprehensive research and analysis. Moreover, it facilitates data access and the development of mutual understanding between different departments of the company.
In this article, we will discuss three key challenges of BI departments and present 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 issue to be discussed here is a lack of documentation and decentralized storage of reports.
Knowledge being siloed and not available to those who need it, is one of the most frequent challenges. Performed analyses and existing reports both are stored and documented either a) individually (in the worst case) or b) at a work group level (at least a little bit less siloed). The reasons are various: a high documentation effort, lack of guidelines, absence of suitable infrastructure, … . The consequences are duplicated analyses, low efficiency and errors in further asset use.
A modern data catalog offers a solution to overcome decentralization and the resulting challenges. It creates the central point of truth. By mapping relationships between data and data assets (data lineage), it becomes clear where the data comes from and how it was transformed. The automation offered by a modern data catalog is beneficial here: The reduced effort for documentation improves its quality enormously. All in all, this increases the confidence of all users in the data in several ways.
Broad added value - not only for the BI area
In the above-given examples, it was shown how modern data catalogs and a modern tool stack can tackle severe challenges of BI departments. Next to the discussed opportunities, the application of a data catalog results in numerous benefits for the whole company, such as an improved data quality, increased employee satisfaction, a rise of data democratization and so on.
In addition to qualitative added value the investment pays off in monetary terms. Correction costs, data replacement costs and process costs are up to 5 - 10 times higher than the costs of a well-designed and well-managed process. Further frequently cited advantages of a data catalog are a) an increased number of analyses performed with higher quality and b) more up-to-dateness due to increased employee productivity. Last but not least, a more comprehensive, holistic analysis on more up-to-date data is the basis for a management to make quick and adequate decisions.
A data catalog is a central building block of a modern data architecture. For the BI area, a data catalog plays an increasingly important role for a wide variety of use cases, far more than the three examples given. In order to ensure that such an investment is profitable, it is necessary to consider all essential features. Make sure to choose a provider which maps your individual use cases. Important aspects can be for example, integrations to existing tools, the degree of automation or the support of explorative work on data. If you want to discover more about potential benefits, check out the article “What is a data catalog?” and get in touch!