7 Business Intelligence Challenges in 2022

Business intelligence

Businesses of all sizes encounter a range of business intelligence issues as they try to make sense of the huge amounts of data they’re gathering. These challenges make it more difficult to make BI operations efficient, effective, and helpful.

Various data architectures, data management concerns, new types of BI capabilities, and varying levels of information literacy in the workplace are all shaping the challenges. However, BI teams must guarantee that sufficient data governance and security safeguards are in place; on the other side, they must illustrate how BI can help workers, especially those with less data literacy.

Another set of BI difficulties revolves around changes in how business intelligence technologies are utilised to assist business choices in businesses.


Let’s take a look at some of the BI challenges that you can face in 2022.

1. Integrating Data from Various Source Systems

As the number of data sources grows, many businesses will need to gather data for analysis from a range of databases, big data platforms, and business applications, both on-premises and on the web. The most frequent method is to employ a data warehouse as a central repository for business intelligence data. Other options are more flexible, such as integrating data without putting it into a database system utilising the data virtualization technique or BI tools itself. But that, too, is a difficult process.

Hariharan stated that this restricts scalability and lengthens the time it takes to examine data. He suggested that customers create a data catalog that incorporates information about data origins and provenance to help speed things up.


2. Data Quality Issues

The accuracy of BI apps is only as good as the data they’re based on. Before starting any BI initiatives, users require access to high-quality data, according to Soumya Bijjal, head of product marketing at Aiven, an open-source database infrastructure platform supplier.

However, Bijjal pointed out that in the haste to gather data for analysis, many businesses overlook data quality or believe that problems can be fixed once the data has been acquired. A lack of knowledge among users about the necessity of effective data management might be the primary reason. When deploying BI technologies, Bijjal recommends creating a data-gathering process that engages everybody in thinking about how to guarantee data is correct, as well as a data management plan that offers a solid framework for tracking the full data lifecycle.


3. Data Silos with Inconsistent Information

Another prevalent business intelligence issue is siloed systems. Bijjal said it’s challenging for BI tools to get siloed data with variable permission levels and security settings since data completeness is a need for successful BI. To have the intended influence on corporate decision-making, BI and data management groups must disintegrate silos and unify the data inside them, she noted.

However, many businesses struggle with this due to a lack of internal information standards across departments and business divisions.

According to Garegin Ordyan, head of insights at data integration provider Fivetran Inc., contradictory data in silos can lead to different versions of the truth. Different outcomes for KPIs and other business indicators that are branded identically in various systems are then displayed to business users. Ordyan suggested starting with a well-defined data modelling layer and precise definitions for each KPI and indicator to avoid this.


4. End-User Training

Effective training and change management initiatives connected to BI projects also require the participation of corporate leaders and managers.

The new dashboard, which was completed in 2019, is automatically updated, replacing a time-consuming manual reporting procedure.

The dashboard was swiftly accepted by HR executives and Fielding’s team developed a short training program for managers in other divisions and business divisions to encourage a wider implementation.


5. Managing the Use of Self-Service BI Tools

Self-service without supervision business executives and other decision-makers may be confused by BI installations in multiple business units, which can result in a chaotic data environment with silos and inconsistent analytical outputs.

BI technologies are also frequently updated with bespoke extensions to satisfy unique corporate objectives, according to fielding. Such modifications block product improvements over time. To avoid this, she recommends that BI teams collaborate with end-users to better understand their requirements and create methods to offer necessary data and dashboards utilising out-of-the-box capability.


6 Low Adoption of BI Tools

End-users frequently choose the route of least resistance and return to familiar tools like Excel or SaaS services.

If you’re just getting started with a deployment, establishing a solid use case that immediately exhibits actual business advantages and motivates employees to use a new BI solution is critical.


7 Dashboard Design Practices

Data visualisations frequently go awry, making it difficult to understand the information they’re attempting to convey. Furthermore, a BI dashboard or analysis is only useful if end users can easily explore and comprehend the information offered. Organizations, on the other hand, frequently focus on getting BI data and the analytics process correct without considering the design and user experience.

BI managers should enlist the help of a UX designer to create a clear and uncluttered visual interface for reports and dashboards. In self-service BI settings, BI teams should also support effective data visualisation design principles. These measures are especially crucial for mobile BI apps on small-screen mobile phones and tablets.

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