7 Business Intelligence challenges to look at in 2022.January 4, 2022 2022-01-04 18:14
7 Business Intelligence challenges to look at in 2022.
7 Business Intelligence challenges to look at in 2022.
BI teams must ensure that adequate data governance and security controls are in place, as well as demonstrate how BI can assist stakeholders, particularly those with limited data literacy.
Another set of BI issues revolves around changes in how business intelligence techniques are used to help firms make better decisions.
Here are 7 BI challenges that you can face in 2022.
1. Bringing Data from a Variety of Source Systems Together
Many firms will need to acquire data for analysis from a variety of databases, big data platforms, and business apps, both on-premises and on the web, as the number of data sources expands. Using a data warehouse as a central store for business intelligence data is the most common option. 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.
According to industry experts, this limits scalability and lengthens the time it takes to analyze data. To help speed things up, experts proposed that consumers build a data catalog that includes information about data origins and provenance.
2. Problems with Data Quality
BI apps are only as good as the data they’re built on when it comes to accuracy. According to industry experts, an open-source database infrastructure platform supplier, consumers need access to high-quality data before they can start any BI projects.
However, experts pointed out that in their eagerness to collect data for analysis, many firms disregard data quality or feel that problems can be corrected after the data is collected. The fundamental explanation could be a lack of understanding among users about the importance of efficient data management. When implementing BI technology, it’s important to keep the following in mind. Experts recommend 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. Information silos with inconsistencies
Siloed systems are another common business intelligence challenge. Since data completeness is a requirement for successful BI, Industry experts say it’s difficult for BI tools to acquire siloed data with varied permission levels and security settings. In order to have the desired impact on business decision-making, BI and data management departments must dismantle silos and harmonize the data contained inside them.
Many firms, however, struggle with this because internal information standards across departments and business divisions are lacking.
According to the inside news, 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. Experts suggested starting with a well-defined data modeling layer and precise definitions for each KPI and indicator to avoid this.
4. End-User Training
Corporate executives and managers must also be involved in effective training and change management initiatives related to BI projects.
Companies must start creating short training programs for managers in other divisions and business divisions to encourage a wider implementation.
5. Managing the Use of Self-Service Business Intelligence Tools
Without supervision, self-service BI deployments in many business units may perplex corporate executives and other decision-makers, resulting in a chaotic data environment with silos and inconsistent analytical outputs.
According to Industry experts, BI tools are frequently upgraded with bespoke enhancements to meet specific corporate needs. Modifications like these stifle product improvement over time. To overcome this, she suggests that BI teams work with end-users to better understand their needs and develop strategies for providing relevant data and dashboards using out-of-the-box functionality.
6. Dashboard design practices
Data visualizations routinely go wrong, making the information they’re attempting to express difficult to comprehend. Furthermore, a business intelligence dashboard or analysis is only valuable if end users can easily examine and interpret the data. Organizations, on the other hand, frequently place a premium on getting BI data and the analytics process right while neglecting to consider the design and user experience.
To build a clean and simple visual interface for reports and dashboards, BI managers should seek the expertise of a UX designer. BI teams should also support successful data visualization design concepts in self-service BI scenarios. These safeguards are particularly important for mobile BI apps on small-screen phones and tablets.
7. Low adoption of BI tools
End-users typically take the easiest option and return to familiar tools such as Excel or SaaS services.
Tools like Google Data Studio, Power Bi and Tableau come with a lot of flexibility to connect to multiple data sources, simplify data preparation, perform ad-hoc analyses, and produce and publish reports online and on mobile devices.
Accurate data can help you see your business in the bigger picture. Choose Absolin BI services backed by Industry experts.