top of page

How ML Can Help With Your BI Insights

Companies in all industries must stay up to date with the latest tech to survive in this digital world. This is especially true in the case of machine learning (ML), which has the potential to transform the way businesses process and use their data.

While ML has a number of useful applications in the business world, applying it to business intelligence (BI) insights can help you optimize your processes and make even better decisions. Thirteen members of Forbes Technology Council shared some creative ways to combine business intelligence with machine learning to produce the best results for your company.

1. Identify Fraud With Variations From 'Normal'

One of the most unique ways to combine business intelligence and machine learning is the identification of fraud indicators. Data analytics should be utilized to feed back into the business intelligence system for a change of policies when a fraud indicator has been located. Utilization of machine learning algorithms can assist organizations in improving processes. - Maria Clemens, Management and Network Services

2. Expose Gaps In Your Data Strategy

A machine learning model can fail because of bad data or bad construction. Once you rule out methodology, a failure of a model (lack of precision, missing data) can expose areas of your data strategy that you should be focused on. A more robust data acquisition strategy fixed our model at Kenna Security, and it had immense positive externalities on the business. - Michael Roytman, Kenna Security

3. Personalize Email Marketing

One key area where machine learning impacts the top and bottom line directly is personalized marketing. Machine learning tells you which customer is more interested in which product/category, and business intelligence can make decisions on what that customer is getting in their emails or app notifications. It's good for both businesses and consumers. - Vikram Joshi, pulsd

4. Customize Promotions And Discounts

While there are some ways companies customize promotions and discounts, it hasn't been truly possible to make deals for specific customers until the advent of machine learning combined with analytics. We can now match excess inventory with customers likely to buy those items and then deliver a promotional incentive to buy and reduce the inventory for a company. - Chalmers Brown, Due

5. Improve Financial Insights

In the financial industry, ML and AI will automate tasks and create better quality data. Any time manual entry is used, there’s a higher risk of error. For SMEs and those who serve them, this means better, more accurate information will be available in near real time. As technology advances, this information will also be easier to share with multiple stakeholders. - Nick Chandi, PayPie

6. Recognize Employees In Real Time

Business intelligence platforms paired with machine learning can evolve an Employee of the Month award to Employee of the Moment, which can increase productivity and engagement. By aggregating and analyzing terabytes of data from multiple sources, managers can recognize top-performing employees in real time while simultaneously learning from the way they work to drive process improvements. - Michael Ringman, TELUS International

7. Reduce Equipment Costs With Smart Maintenance

Machine learning and predictive analytics can help brick-and-mortar businesses save millions of dollars by analyzing historical data from equipment such as heating and refrigeration units and recommending smarter maintenance. The data informs decisions such as whether to replace or repair an asset or accept or challenge an invoice. The savings could add up quickly for multi-location businesses. - Tom Buiocchi, ServiceChannel

8. Combine AI/ML Technologies For Maximum Optimization

Like many technologies, AI -- and machine learning, in particular -- is more powerful when used in combination with other new technologies. We see that in the shift toward intelligent automation. Instead of having a bot perform a routine task like calculating data, have it pass that data to a machine learning algorithm that can learn what’s happening and suggest the optimal course of action. - Matthew Lieberman, PwC

9. Monitor And Manage Your Applications More Effectively

With applications becoming data-driven, one has to address their non-deterministic nature. One creative idea is to introduce inert features, which allow you to monitor an app’s performance in a much more detailed way. You can apply this for all AI- and ML-based software to initially test against historical data and then learn how it responds to conditions in real-world production scenarios. - Ashok Reddy, CA Technologies

10. Gain Better Future Insights With Predictive Analytics

We are now using BI tools to find patterns and trends. When combined with machine learning, it enables applications to make recommendations, target sales efforts, increase revenues, and increase operational efficiencies in manufacturing and supply chain management. - Naresh Soni, Tsunami ARVR

11. Enhance Cybersecurity Intelligence

New cybersecurity tools are leveraging machine learning to help C-suite executives and board members better understand and assess an organization's resilience against cyberattacks. These tools automate predictive risk calculations to provide clear business intelligence on where the enterprise’s most pressing vulnerabilities are and help focus resources on breach avoidance. - Gaurav Banga, Balbix

12. Preemptively Respond To Customer Needs

Many environments can be monitored with cameras, bluetooth beacons or other IoT sensors and then be preemptively serviced instead of reactively repaired. For example, in an assisted-living facility, staff could be given recommendations on who hasn't been visited in a while and who might be too cold or need more interaction. With more data points, they could provide better, more customized service. - Luke Wallace, Bottle Rocket

13. Tap Into Smart Data Sources Based On ML Recommendations

Machine learning can enable organizations to continuously gain context from collective wisdom. Just like retail recommendations based on purchase history, ML-based recommendations can recommend data sources to use to answer questions or recommend ways to clean data automatically based on how others have used the data in the past. This means less time discovering data and more time analyzing it. - Francois Ajenstat, Tableau Software

bottom of page