Business Intelligence challenges are influenced by numerous elements, such as various data infrastructures, problems with data administration, difficulties adjusting to new capabilities, and shifts in the workforce’s data literacy levels.
Business Intelligence (BI) teams need to demonstrate how BI can help all employees, even those unfamiliar with data-driven techniques, while ensuring that the proper data governance and security measures are in place. The utilization of BI technologies in enterprises has changed, which presents another set of Business Intelligence problems.
Business units that use self-service BI, data preparation, and data visualization technologies to collect insights are at the forefront of current BI initiatives. Traditional BI consists of IT-driven apps and curated data. The conventional method frequently gives business users well-defined workflows and information via reports and personalized portals.
What Exactly Is Business Intelligence (BI)?
The main goal of BI is to transform data into meaningful and valuable information. This includes data that can be used to increase sales, get rid of unnecessary procedures, and spot business expansion prospects. To put it another way, it is crucial to reach fact-based business judgments.
These presumptions are incorrect due to significant technological advancements in recent years. Everyone now has access to effective BI. However, many small businesses are reluctant to deploy BI because they are concerned about the expense and effort.
The price of analysis platforms has decreased, data is increasingly stored in the cloud, implementations are quicker, computers can now self-learn, and interfaces are so simple to use that even those who aren’t analysts can uncover connections and trends.
BI Challenges That You Can Face In 2022
BI challenges depend on how Business Intelligence technology is applied to aid organizations in making better decisions.
The following are some challenges you can encounter with Business Intelligence in 2022:
1. Consolidating Data From Multiple Source Systems
As the number of data sources increases, many businesses will need to gather data for analysis from various databases, big data platforms, and business apps, both on-premises and online. The most popular choice is to deploy a data warehouse as a central repository for Business Intelligence data.
Other approaches are more adaptable, such as integrating data without putting it into a database system via data virtualization or BI tools. But this is a difficult task as well.
2. Information Silos With Inconsistencies
Another frequent problem with corporate analytics is siloed systems. It is challenging for BI tools to obtain siloed data with varying access levels and security settings because data completeness is a need for successful BI. Therefore, BI and data management departments must break down silos and harmonize the data they possess to have the intended influence on corporate decision-making.
However, due to a lack of internal information standards across departments and business divisions, many businesses struggle with this.
Contradictory data in silos might result in distinct interpretations of reality, claims Garegin Ordyan, head of insights at data integration provider Fivetran Inc. Business users are then shown numerous KPI and other indicator results branded identically in various systems. To prevent this, starting with a transparent data modeling layer and detailed definitions for each KPI and indication is a solution.
3. Issues With The Data Quality
Regarding accuracy, BI applications are only as good as the data upon which they are based. An open-source database infrastructure platform provider cites industry experts as saying that before users can begin any BI projects, they must have access to high-quality data.
However, experts noted that many companies overlook data quality or believe issues may be resolved once the data is obtained in their haste to collect data for analysis.
The main factor for this situation can be consumers’ ignorance of the value of effective data management. The creation of a data collection procedure that encourages everyone to consider how to ensure data accuracy and a data management strategy that provides a strong foundation for tracking the whole data lifecycle are advised by experts.
4. Dashboard Design Practices
Data visualizations frequently go awry, making it difficult to understand the information they’re trying to convey. Additionally, the value of a business intelligence dashboard or analysis depends on how simple it is for end users to view and understand the data.
On the other hand, organizations frequently overlook design and user experience to correct BI data and analytics processes.
These security measures are crucial for mobile BI programs on diminutive smartphones and tablets. BI managers should enlist a UX designer’s expertise to create a clear and straightforward visual interface for reports and dashboards. BI teams should also offer practical data visualization design approaches in self-service BI scenarios.
5. Low Adoption Of BI Tools
End customers frequently choose the most straightforward route and return to well-known products like Excel or SaaS services.
Accurate data can assist you in gaining a larger perspective on your company. Many data sources may be connected using tools like Google Data Studio, Power Bi, and Tableau. These tools also make it easier to prepare data, run ad-hoc analyses, and create and publish reports online and on mobile devices.
6. Managing the Use of Self-Service Business Intelligence Tools
Self-service BI implementations across numerous business units could confuse corporate executives and other decision-makers if they are not closely supervised, creating a chaotic data environment with silos and erratic analytical results.
Industry experts claim BI systems routinely receive customized updates to match unique corporate requirements. These kinds of changes prevent products from getting better over time. She proposes that BI teams collaborate with end users to understand their needs better and create plans for delivering pertinent data and dashboards utilizing the out-of-the-box capability to get around this.
Conclusion
You can use the great tools available in our digital age to address these innate issues. Innovative solutions are about efficiency and simplicity, and current BI provides both in spades.
Self-service data-driven platforms have made it possible for SMEs and startups to access a variety of quick, actionable Business Intelligence solutions even though they don’t have the same financial resources as larger companies.
