Top 10 Bad Business Intelligence Implementation Practices to Avoid

Business intelligence is transforming traditional workloads for global enterprises across all industries. Business intelligence practices enable enterprises to become more modern while effectively adopting digitalization or digital transformation.

Depending on the business goals and objectives, there are various business intelligence implementation practices for integrating with artificial intelligence. Leveraging artificial intelligence in your business will help increase customer engagement and aspire to earn adequate profits. Business intelligence implementation practices help gain a competitive advantage for millions of companies in the global technology market. The combination of artificial intelligence and business intelligence supports better, more informed decisions through automation. BI practices have become one of the key elements to help the decision-making process meet customer satisfaction in 2022 and beyond. People need to know some of the top ten worst business intelligence implementation practices to avoid when implementing artificial intelligence in business to eliminate potentially huge losses.

Top 10 Worst Business Intelligence Implementation Practices to Avoid in 2022

1. Collect Poor-quality data

Data is the most important element in business intelligence that is integrated into artificial intelligence models. Companies may not collect any poor quality data to implement into business implementation practices that solely leverage AI. Then, it will hinder the entire data management process such as real-time data tracking, data reconciliation, etc.

2. Ignore key data sources

Enterprises must not ignore key data sources as they continue to implement business intelligence practices. In addition to data warehouses, EROs, CRMs, and specific databases, there are multiple key data sources. Ignoring other key data sources such as network monitoring data or social media can lead to inaccurate decisions.

3. Complicating BI practices

One of the worst business intelligence implementation practices is to complicate BI practices for no reason. complex. Businesses need to remember that the integration of AI makes business intelligence practices much easier and simpler, with just a little understanding. There is no unwillingness to complicate BI practices.

4. Not providing practical business intelligence training

Organizations must find the right time to provide practical business intelligence training to employees. Avoiding training on business intelligence practices can lead to more confusion and complex issues for employees who don’t have a proper understanding of business intelligence and artificial intelligence. Avoiding proper training sessions is one of the worst business intelligence implementation practices.

5. Organizational Culture and Structure

Before implementing business intelligence practices, an organization must have a deep understanding of its culture and structure. Individual teams should have the freedom to choose their own BI practices rather than dictate what members need. This business intelligence implementation practice can slow down the process of adopting business intelligence practices that have inaccurate insights from data.

#6. Poor perception of business intelligence projects

One of the worst business intelligence implementation processes is the perception of business intelligence projects Poor cognition. The integration of artificial intelligence in business is to make business goals easier to achieve in a shorter time. The perspective on business intelligence projects should change to drive profits through informed decisions.

7. Treat Excel as the default platform for business intelligence practices

Organizations must not treat Excel as just a spreadsheet and make it Become the default platform for all business intelligence practices. Excel can provide some additional issues in the artificial intelligence management process in the business, such as error-prone processes, data errors, etc. Businesses should prevent critical data from accumulating in Excel worksheets.

8. Avoid defining KPIs for business intelligence

Implementing AI in business requires defining KPIs for effective business intelligence. Strategic business intelligence practices must include defining KPIs into different categories, such as project management metrics, marketing data, financial metrics, customer metrics, and HR metrics. Businesses should neglect to avoid defining KPIs as it is one of the worst business intelligence implementation practices.

9. Failure to find competent software vendors

Not finding competent software vendors is one of the worst business intelligence practices . To implement Artificial Intelligence in the business and integrate the combination of Artificial Intelligence and Business Intelligence, there is a need for Business Intelligence Infrastructure Architect, Database Administrator, Data Mining Expert, ETL Lead Developer and Application Lead and Data Quality Analyst and Project Manager . Therefore, it is important to find a competent software vendor to work on your business intelligence project.

10. Inaccurate estimates

Inaccurate estimates often delay some top business intelligence projects, hindering business processes and profits in the long term. This can lead to serious consequences, such as adjusting project scope and implementing AI in business processes.

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