As they try to make sense of the massive volumes of data they're collecting, businesses of all sizes face a variety of business intelligence difficulties. Making BI operations efficient, effective, and useful becomes increasingly challenging as a result of these issues.
The issues are shaped by various data architectures, data management concerns, new types of BI capabilities, and variable levels of information literacy in the workplace. However, BI teams must ensure that adequate data governance and security controls are in place, as well as demonstrate how BI can assist workers, particularly those with limited data literacy.
Another set of BI issues revolves around changes in how business intelligence technology are used to help firms make better decisions.
Let's take a look at some of the business intelligence difficulties you might 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.
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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 Soumya Bijjal, head of product marketing at Aiven, an open-source database infrastructure platform supplier, consumers need access to high-quality data before they can start any BI projects.
However, Bijjal 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. Bijjal recommends building a data-gathering strategy that involves everyone in thinking about how to ensure data is correct, as well as a data management plan, while introducing BI tools.
3. Information silos with inconsistencies
Siloed systems are another common business intelligence challenge. Since data completeness is a requirement for successful BI, Bijjal said it's difficult for BI tools to acquire siloed data with varied permission levels and security settings. She highlighted that in order to have the desired impact on business decision-making, BI and data management departments must dismantle silos and harmonise the data contained inside them.
Many firms, however, struggle with this because internal information standards across departments and business divisions are lacking.
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.
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4. End-User Instruction
Corporate executives and managers must also be involved in effective training and change management initiatives related to BI projects.
The new dashboard, which was finished in 2019, is automatically updated, eliminating the need for time-consuming human reporting.
Fielding's team designed a brief training session for managers in other divisions and business divisions to encourage a wider use of the dashboard, which was quickly embraced by HR executives.
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 fielding, 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. Low Adoption of Business Intelligence Tools
End-users usually take the easiest approach and return to familiar tools like Excel or SaaS services.
If you're just starting started with a deployment, creating a good use case that shows immediate business benefits and encourages staff to utilise a new BI system is crucial.
7. Best Practices for Dashboard Design
Data visualisations 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 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 visualisation design concepts in self-service BI scenarios. These safeguards are particularly important for mobile BI apps for small-screen phones and tablets.