As organizations try to make sense of the massive volumes of data they are collecting, businesses of all sizes face a range of business intelligence issues. Making BI operations effective, efficient, and useful becomes increasingly challenging as a result of these issues.
Data management concerns, various data architectures, new types of BI capabilities, and varying levels of information literacy in the workplace are all shaping business intelligence challenges. However, BI teams must ensure that adequate data governance and security controls are in place, as well as demonstrate how BI can helps the workforce, particularly those with limited data literacy.
Another set of BI issues revolves around changes in how business intelligence technology are used to help companies make better decisions.
Let’s take a look at some of the business intelligence challenges to watch in 2022.
Bringing Data from a Variety of Source Systems Together
Many companies will need to collect data for analysis from a variety of big data platforms, databases, and business applications, both on-premises and on the web, as the number of data sources grows. Using a data warehouse as a central store for business intelligence data is the most common method. Other solutions, such as using data virtualization or BI tools to integrate data without putting it into a database system, are more flexible. However, this is a challenging task as well.
This limits scalability and increases the amount of time it takes to analyse data. It’s a good idea to build a data catalogue that includes information about data origins and provenance to help speed up the process.
Issues with Data Quality
BI apps are only as good as the data they are built on when it comes to accuracy. Users must have access to high-quality data before they can begin any BI activities.
In their haste to gather data for analysis, many companies overlook data quality or feel that flaws may be resolved once the data has been gathered. The primary reason could be a lack of understanding among users about the importance of efficient data management. Creating a data-gathering process that engages everyone in thinking about how to ensure data is correct, as well as a data management plan that provides a solid structure for managing the complete data lifecycle, are both essential when introducing BI technology.
Data Silos with Inconsistent Information
Siloed systems are another common business intelligence challenge. Because data completeness is a need for successful BI, it’s difficult for BI tools to obtain siloed data with varied permission levels and security settings. In order to have the desired impact on business decision-making, BI and data management teams must eliminate silos and unify the data contained inside them.
Many companies, however, struggle with this because internal information standards across departments and business divisions are lacking. Different versions of the truth can emerge as a result of contradictory facts in silos. Business users are then shown different outcomes for KPIs and other business metrics that are branded identically in separate systems. To avoid this, it’s a good idea to begin with a well-defined data modelling layer and clear definitions for each KPI and indicator.
Managing the Use of Self-Service Business Intelligence Tools
Without supervision, self-service BI deployments in many business units may perplex executives and other decision-makers, resulting in a chaotic data environment with silos and inconsistent analytical outputs.
BI tools are frequently updated with bespoke enhancements to meet specific corporate needs. Modifications like these stifle product improvement over time. To avoid this, BI teams should work with end-users to better understand their needs and develop strategies for delivering required data and dashboards using out-of-the-box functionality.