How machine learning improves business intelligence
Machine learning (ML) refers to a software application that learns processes from past data much like how a human would learn. ML integrates into and augments business intelligence (BI), an expansive software that collects and analyses business data to deliver better insight into company operations. BI is vast, comprising several applications like predictive analytics, data mining and performance management systems. Hence, integrating ML improves the function and capability of BI.
Machine learning expands BI capabilities
Reduces damage and injury
Certain industries like the oil industry depend on several factors, like worker safety and weather for continuing operations. If a worker gets injured, or a machine fails, it disrupts productivity. BI utilises analytics and predictive modelling to monitor machine operations in real time. Machine learning learns from past data to discover the cause of worker injuries and critical failures in machines. Using the data, ML then “predicts” the chances of a worker getting injured or a machine reaching a critical level before it happens.
Thus, with BI and ML, oil companies not only collect and analyse data but also take pre-emptive action to prevent a disaster before it happens. Therefore, BI can increase productivity, reduce worker injury rates and improve the lifespan of equipment using ML. It’s not just the oil industry that will benefit – any industry where workers are at risk reduce the chances of injury through ML and BI.
Boosting productivity
Machine learning receives a lot of attention because it boosts productivity significantly. BI collects and analyses data from several processes, but ML can streamline and automate several processes. The automation process takes place through intelligent automation, where systems can survey thousands of operations in a single day and flag exceptions. Human agents examine the flagged cases.
As a result of this, companies make better use of their human capital. Instead of having human agents examine thousands of processes, they only look at the most critical cases, which are beyond automated systems. A process using both automation and human intuition is useful in specific instances like fraud detection.
ML can also streamline processes like customer service, risk management, business capabilities management and more. The combined appeal of automation and streamlining means ML can boost productivity by a significant amount.
Boost sales and marketing
Businesses are using BI to gain deeper insights into customer purchasing habits. With machine learning, companies will know audience reactions to new products or marketing campaigns. BI collects information on customers from different sources like browser searches, purchases and much more. ML leverages this information, analyses the trends and predicts customer reactions.
Companies use technology to discover how their audience will take to new products or campaigns before either launch. With this capability, businesses increase their chances of success while also sidestepping any problems that damage the brand.
Improves research
Businesses are now working in a knowledge economy, which means research is important for success. BI and machine learning tools can improve research processes through a BI-search platform. Search platforms based on BI and ML are more responsive to consumers, providing suggestions that are change based on the questions asked. The search platform responds to the needs of the user and not the other way around. Thus, users can get more concise answers in less time with these new search platforms.
Better forecasting
Forecasting has evolved over the years from excel sheets to predictive modelling but will evolve even further with BI and machine learning. Forecasting is a huge part of improving productivity from predicting sales to optimising supply chains. Machine learning improves the process by taking terabytes of data and using it to predict trends. In the future, forecasting will be so sophisticated that algorithms will answer specific questions rather than generate models.
Find real-time anomalies
BI systems enable businesses to find anomalies in real-time, but ML builds and improves on this system. Fine-tuning of this system is crucial because it allows firms to sharpen specific processes like fraud detection. Finding real-time anomalies opens up several opportunities for businesses not seen previously. One possibility is the option is to see someone browsing your website in real-time instead of just knowing about the people who bought from you. It will give you better insight into what you have been doing wrong and reveal the best way to increase the conversion rate.
Key takeaways
Machine learning can improve the functionality of BI because the software collects and analyse terabytes of data to predict future trends. Anticipating what will happen before it happens is one of the best investments a business can make and it can only be obtained through machine learning.
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