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Business intelligence is transforming the traditional workloads of global businesses across all kinds of industries. BI practices have made businesses more modern while adopting digitalization or digital transformation efficiently and effectively. There are multiple business intelligence implementation practices with the integration of artificial intelligence, according to the business goals and objectives. Leveraging AI in business will help to enhance customer engagement with the urge to drive sufficient profit. Business intelligence implementation practices help to gain a competitive edge in the global tech market among millions of companies. The combination of artificial intelligence and business intelligence is supporting in making better and smarter decisions with automation. BI practices have become one of the key elements to help in decision-making processes to meet customer satisfaction in 2022 and beyond. Let's be aware of some of the top ten worst business intelligence implementation practices to avoid while implementing AI in business for removing the potential huge losses.
Data is the most important element in business intelligence to integrate into an artificial intelligence model. A company must not collect any poor-quality data to implement into the business implementation practices for just leveraging artificial intelligence. Then, it will hamper the entire data management process from real-time data tracking, data harmonization, and many more.
Companies must not ignore key data sources in the continuation of implementing BI practices. There are multiple key data sources other than data warehouses, ERO, CRM, and particular databases. Ignoring other key data sources like web monitoring data or social media can create inaccurate decisions for a business.
One of the worst business intelligence implementation practices is to make BI practices complicated without any reason. Businesses need to remember that the integration of artificial intelligence has made BI practices easier and simpler with a bit of understanding. There is no such thing as complicated BI practices to be reluctant about.
Organizations must look out for accurate time for providing training for BI practices to employees. Avoiding training for BI practices can lead to more confusion and complicated issues among the employees who do not have a proper understanding of business intelligence and artificial intelligence. It is one of the top worst business intelligence implementation practices to avoid proper training sessions.
It is important for an organization to have a deep understanding of its culture and structure before implementing business intelligence practices. There should be freedom in respective teams to choose their own BI practices instead of dictating what the members need. This business intelligence implementation practice can slow down the process of embracing BI practices with inaccurate insights from data.
One of the worst business intelligence implementation processes is having a poor perception of BI projects. The integration of AI in business is to make business goals more achievable in a short period of time. The perception regarding BI projects should be changed to drive profit through smart decisions.
An organization must not consider Excel to be just a spreadsheet and make it the default platform for all BI practices. Excel can offer some additional problems during the management of AI in business such as error-prone processes, data errors, and many more. businesses should prevent accumulating critical data in Excel sheets.
Implementing AI in business needs to define KPIs for effective business intelligence. Strategic BI practices must include defining KPIs into different categories such as project management metrics, marketing data, financial indicators, customer metrics, as well as HR indices. Businesses should ignore avoiding defining KPIs as it is one of the top worst business intelligence implementation practices.
It is one of the worst BI practices to not seek a competent software vendor. To implement AI in business and integrate the combination of artificial intelligence and BI, one needs to have a BI infrastructure architect, a database administrator, a data mining specialist, an ETL lead developer, and an application lead with a data quality analyst as well as a project manager. Thus, it is important to find a competent software vendor to work on BI projects.
Inaccurate estimates tend to delay some of the top BI projects to hamper the business process and profit in the long term. This can lead to serious consequences like moderating the project scope as well as the implementation of AI in the business process.
