By Nisha Sharma - September 19, 2022 4 Mins Read
Business intelligence (BI) is imperative for organizations today, and enterprises that work with a lot of data have better opportunities for much more relevant business insights
While smart BI solutions can help businesses identify potential growth areas, gauge the viability of their products, evaluate supply and demand, and assess their market position, there are still challenges and issues that organization face with business intelligence deployment.
The data quality on which BI apps are built is the basis of the accuracy of the insights. Enterprises need to ensure access to high-quality data before starting any BI activities. Many enterprises overlook data quality or think that issues may be resolved after the data has been gathered in their haste to collect data for analysis. The main cause could be that people don’t understand how important proper data management is.
When implementing BI technology, it’s important to have a data collection procedure that gets everyone thinking about how to ensure data accuracy and a data management strategy that provides a sound foundation for monitoring the whole data lifecycle.
Businesses today need to collect data for analysis from a range of databases, big data platforms, and corporate applications, both locally and online, as data sources have evolved. This is the approach that data warehouses use most frequently, to collate a centralized database for business intelligence information. Other, more flexible approaches to integrating data without storing it in a database system, include the use of BI tools or data virtualization. But even that is a difficult process. Customers are urged to create a data catalog that comprises information on the provenance and sources of their data in order to streamline the process.
Another common issue with business intelligence is siloed systems because it can be difficult for BI tools to pull data from siloed systems with varying permission levels and security settings. Complete data is required for successful BI. Data management and business intelligence departments must break down silos and harmonize their data in order to have the desired impact on corporate decision-making. However, business struggles with this silos breakdown because of the absence of internal information flow among departments and business divisions. This data conflict in silos leads to various interpretations of the truth. Business users are then shown these numerous KPI and other indicator results that are branded identically in various systems.
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Business executives and managers also need to take part in training and change management initiatives related to BI projects. There is a need for adequate skills and training for running business intelligence tools. But this is a drawback that many firms face, even in times where data and data intelligence are the most sought-after technical skills. This leads to the demand for extra training in data administration programs. Therefore, organizations must focus on upskilling and creating more skill groups that can effectively mine the BI data and draw insights for strategies. After choosing the appropriate BI stage, the most crucial training that businesses require is becoming accustomed to the visual interface, which is another layer of skills requirement.
End users can only view reports through the soft serve and cannot access or modify data from the data warehouse. The use of the soft serve BI model has expanded as businesses have been able to standardize data, indicators, metrics, visualizations, and reports. End users are now empowered to customize reports as necessary to meet their specific needs.
Nisha Sharma is working with OnDot Media as a Tech Journalist. She has done Post graduation in Journalism & Mass Communication. Her prior experience was in B2B as a Content Writer. This is her first stint with a technology publication and she is on the high curve of learning about the IT sector, it's challenges, and the trends that move it forward.
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