Machine learning – The missing piece in Business intelligence (BI) puzzle

Automation is more useful if it gives you the business insights you need to make smarter decisions as opposed to trends and data about things that happened within your company or industry in the past. Up until recently, business intelligence was an umbrella term that was used for page after page of reporting on facts, figures and numbers. Today, the advances in BI means that company users across the board are able to drill down into relevant information as they need it in order to make better decisions, faster. It allows them to strike while the iron is hot and take on new opportunities as they arise and before market conditions change.

Gartner asserts that machine learning that enables predictive and advanced analytics is the fastest growing area in the industry. According to the research entity, predictive and prescriptive analytics will attract 40% of enterprises’ new investment in business analytics by 2020. Entire enterprises are predicted to be disrupted as over 50% of large companies across the globe will be competing with advanced analytics and proprietary algorithms by 2018.

Looking to the future is becoming easier thanks to new BI advances

Instead of reporting on past findings and data, predictive analysis does the complete opposite: it picks up on data to calculate future opportunities. The ability to calculate trends and actually point out opportunities in the market that can be exploited take these types of analytics to an entirely different level than has been achieved by other BI tools such as SQL reporting and Tableau.

Some of the recommendations that advanced, predictive analytics can make often relate to information that a marketing department or risk assessment team would find useful. It enables a proactive approach so that pitfalls can be avoided before they happen and advantages can be seized before windows of opportunities close. A risk analysis team, for example, could use these tools to pick up on fraud risks before transactions are processed. Marketing teams, on the other hand, can use predictive analysis to gain a better understanding of the sales funnel that their various target audiences follow, cross selling opportunities in the market and the reasons for a prospective lead to fall off the radar.

Healthcare companies are also adopting machine learning systems that track everything from air quality to atypical behavior to minimize accidents or threats in hazardous environments.

The implications of machine learning

In the past, if you wanted a machine to do something new you would have to program it. Programmers would have to lay out – with excruciating detail – every single step that you want the computer to execute in order to achieve your goal. If you want a machine to do something that you don’t know how to do yourself, then programming it to achieve certain goals is going to be a great challenge.

The first commercial example of successful machine learning is Google. Google showed that it is possible to find information by using a computer algorithm, which is based on machine learning. Since then, there have been many examples of commercially successful machine learning systems and tools. Companies like Netflix and Amazon use machine learning to suggest products that you might be interested in buying or movies that you might want to watch. Machine learning algorithms can often borderline on mysterious or magical (a good example of this is the ‘people you may know’ suggestions on LinkedIn and Facebook). Algorithms have learned how to make suggestions based on data rather than being programmed by the hands of a developer.

From googling to driving, machine learning is becoming a part of our everyday lives

Machine learning is also the reason why we are able to see self-driving cars today. To be able to tell the difference between a tree and a pedestrian is important and while programmers don’t know how to write these programs by hand, algorithms from machine learning systems make it possible for Google’s self-driving cars to travel thousands of miles and continue to distinguish between trees and humans.

Computers can learn to do things that we sometimes don’t know how to do ourselves, or even do things better than what we can. Computers can also listen and understand. There have been multiple examples of natural language programming systems, where a person is able to speak in English and have it translated, in real time, to Chinese, for example. Another thing that many people didn’t expect is that computers can see. In a competition in Germany, called The German Traffic Sign Recognition Benchmark, a machine learning system was used to identify traffic signs. Not only could this system recognize traffic signs better than any other system, but the competition’s leaderboard showed that it was about twice as good at recognizing traffic signs than humans were.

Machine learning can also work at unprecedented speeds that would be unachievable by humans. Last year, Google announced that they had mapped every single location in France in two hours. They did this by feeding images into a machine learning algorithm to recognize and read street numbers. It would have taken many human beings many years to map all of this information in order to cover the same amount of space.

Machine learning and automation in industry

The companies who are succeeding at the moment are the ones who are investing in the resources that are needed to drive digital transformation. Innovating in a way that makes companies more resilient and efficient means changing the way we think about big data and business intelligence. According to Herain Oberoi from Microsoft’s Cortana Analytics team, clients are looking for systems that do much more than provide information – they want to know what logical steps they should be taken. “The fundamental customer challenge hasn’t changed; how do I go from the data that I have, to getting some insights, to actually enabling an action or driving something forward?” Oberoi told

Extracting knowledge from data

The interest in machine learning capabilities and advancements in this industry segment is at an all time high. Machine learning, which refers to a computer’s ability to extract knowledge from data without necessarily being programmed to do so, is enabling companies to solve complicated and data-intesnsive problems with a lot of success. While machine learning models may seem to be focused on eliminating human input through automation, talented data scientists are still needed to implement and execute these tools effectively.

Across the globe, machine learning is being used to improve customer service, risk and compliance, marketing as well as develop new business areas. As a discipline that enriches virtually every industry within which it is applied, machine learning is challenging accepted boundaries wherever it goes. To learn more about how Tellius is past pushing past perceived boundaries with the power of search-driven analytics, contact us.

About the Author

Ajay Khanna is  CEO and Founder of Tellius with vision to re-define data intelligence by combining power of search with predictive analytics. Ajay has background with building and growing successful innovative startups. He is a passionate Tech innovator with experience in building new technologies and disruptive business models.


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