BI tools are not perfect

The primary function of many BI tools is to put business information into action. In fact, it provides interactive dashboards for generating reports and displaying important business data. The problem is that these tools do little to help you analyze or point out specific problems. This weakness forced us to create separate jobs, such as deploying report filtering and dashboard management separately. These include general BI analysts and analysts whose careers have focused on specific software platforms.

As a result, many companies are forced to choose between hiring additional employees to actually make the decisions and perform the necessary work, or letting former employees take on two full-time jobs. System users who have to make decisions in the field every day are overwhelmed with hundreds of reports, leaving time for trained operations to contribute to the business. Some companies spend unnecessary capital on analyst salaries.

Also, the quality of BI dashboards and reports is completely dependent on the quality of the person who designs them. In most companies, the people who set up BI dashboards are part of the BI or analytics team and are experts in business analysis, but they rarely know the day-to-day struggles of users using reports in the field. . This is one of the reasons why we create so many reports and configure dashboards endlessly. The people writing the reports just provide as much information and detail as possible, with little thought given to how that information will be used in the field.

A prime example of this problem is the retail industry. Merchandisers and buyers can use reports to analyze KPIs over time, compare sales to the previous year or month, and identify anomalies such as a sudden drop in sales of bedroom slippers or children's bicycles. To do. At that time, you have to spend hours considering numerous reports and complicated dashboards in front of you before you can make useful proposals to the sales floor and suppliers.

Of course, system users give feedback to BI and analysis teams and try to improve the usefulness of reports and dashboards, but unfortunately most companies do not have a simple mechanism to improve the usefulness. Feedback is seldom fast enough to result in worthwhile changes, and differences between system users mean that the same adjustments may not benefit report users in all sites. not.

In short, most BI systems provide too many reports and dashboards for field reporting users to realistically incorporate business decisions. This can lead system users to draw inconsistent conclusions, rely on incomplete evaluations, commit serious business errors and lose profits.

How can AI help?

We have written before that AI is best used to help humans and do their jobs more efficiently, rather than replacing them.

What humans love most

On the other hand, what AI is best at is

By leveraging the strengths of humans and AI, we can create BI tools where humans do what they are good at and AI does the rest. Business leaders can leverage the above ideas to improve AI applications and envision a future where employees can work to their full potential.

Some of the best applications of AI include:

The last point is the most innovative. Instead of having employees handle the repetitive and time-consuming tasks of sifting through reports and configuring dashboards, AI processes terabytes of data at incredible speed. Transactions, product information, procurement, vendor data, inventory, and store-specific information are all organized and analyzed quickly and efficiently by AI.

Best of all, AI can break this information down into tiny insights. This is useful when actually making purchasing decisions and sales strategies for system users. Strategic use of AI enables system users to quickly respond to AI-detected anomalies and move organizations closer to their target business metrics without wasting time and money.

If business leaders could reframe how they think about BI and AI tools so that each of their BI, AI, and human employees could do what they're good at, businesses would be much more efficient and much more efficient. You will be able to innovate at high speed.

*This article was published in Forbes.

We introduce "AI that realizes data-driven merchandising" for retail merchandisers and buyers. We also have many AI use cases available. Please feel free to download and take a look.

▼ Business problem-solving AI service that utilizes the data science resources of 25,000 people worldwide
Click here for details

Source:https://www.crowdanalytix.com/how-ai-can-help-surmount-bi-shortcomings/
CrowdANALYTIX Resource Library