It is a strong idea that if people start using insights and facts to direct their decisions in the right direction, their businesses and themselves would be very prosperous. But if we still look at rates of BI adoption, they are very low. By BI adoption, I mean the total users who have actual access to such a system. A report by Analytics State Adoption published in 2017 says that BI adoption dropped almost 20% in 2016.

If BI adoption can have such a good business impact, then why users with such data access are not employing it? This question has been haunting me for a long time now, but now I think we may know how to address this issue. I have seen Business Intelligence coin from two sides (as a solutions developer and a final user). It has made me realize the challenges on both sides of the coin, i.e., creating and then using these BI insights.

Is the merging of Business Intelligence and Machine learning the silver bullet we were always looking for in our corporations and businesses?

The Limits of Business Intelligence

The business intelligence tools have succeeded mostly in providing data to the users. It involves tracking, utilizing the key performance indicators for improvement, scrutinizing data in visual form, working together, and sharing the data with other clients.

Surprisingly, many corporations and businesses have BI, but they choose merely not logging in. BI developers are in control of creating solutions that have low user rates for the companies. I have always thought about this behavior’s reasons, and ever since, I have been trying to comprehend this problem closely.

The developers of solutions and products believe that salespersons of large businesses would love to use BI tools. But the fact is that most people do not have the inclination or time to utilize BI tools. Most business people just look for the best and proper insights into the user data to aid them in making better and informed decisions. The reason behind this is they want to make quick and efficient decisions at the proper time.

However, business intelligence tools have some chief limitations, even when they significantly influence exploring, interacting, and viewing the incoming data. BI tools are not very efficient or effective in discovering hidden information in the data. It is a manual job to identify veiled insights in the data and not the work of BI tools, which is why it requires both skill and significant time.

Customers may see something while viewing the dashboard that they can explore further. And the point is that viewers may interact with a dashboard in several ways. For instance, altering pivoting, filtering, chart types, or drilling in the hope that they discover the insight they require. That kind of data discovery or exploration is usually done based on the right solution’s intuition and not subjected to human prejudice. And if the data still eludes the users, then information is carried on to Excel, after which the query is directed towards a data professional.

Can Machine Learning Change Business Intelligence?

Machine learning models are very much operational in discovering the hidden insight and patterns in the data set. For several years, the data analysts have used these methods for solving specialized and complicated business issues. With progress in machines’ processing power, there have been considerable developments in this field, and now they have more access to solving complicated mathematical models. Previously, data models required costly and high maintenance hardware, but cheap platforms are available to everyone these days with advancement.

Lately, we have seen many salespersons dealing in BI tools coupled with machine learning abilities bringing ingenuity to the platform along with augmented efficiency and hidden insights. Soon, the combination of business intelligence and machine learning would be the most effective tool for running large businesses, and that too in a creative way. As soon as users become addicted to BI’s capability, they will always expect more and more. The example is of GPS and other similar technologies, which we cannot even imagine living without.

Combining all these abilities with BI can effectively automate those procedures of discovering insights that business people previously did not understand. Looking at a conventional dashboard, a business client looking at the top sales may agree that the trends look okay and may not search deep into it. But there may be a reason for concern which is not readily available to see, but it will be there in the composition of sales numbers. In this case, some products are performing decently while other products are showing a rapid decline. But if these pertinent insights are not available at that moment, then it is destined to doom.

Automating a process means that the hidden insights are found and also carried on and made available at a rapid rate, which in turn permits that businesses to act promptly and with improved information. This automation task should make available some more time for the analysts to work on other things in the organization.

Routine tasks like finding anomalies, doing commentary, or doing variance analysis in reports are being done by several analysts. But if such duties are brought to automation, then analysts can have some more time at hand, and they would be able to do higher-value work at that time.

What Challenges Does Machine Learning Face?

For automation of insights like these, extra platform abilities are needed to help clients understand what they see at the site. Even people who are digital data literate sometimes do not know what they are looking at if it is too complex or a complicated chart that is difficult to decipher. Therefore, it is even more difficult and a big challenge for those who have low digital literacy.

The utilization of natural language has been a giant leap forward in the tech industry, which helps its users understand and interpret what the algorithm is telling them. These systems can produce interpretations of written form results, which adds some more knowledge to the users’ interpretation.

The second main challenge is the trust that needs to be worked on.  For a customer or user to act on specific information, he must be capable of trusting whatever he is being told.  Numerous aspects can arise out of this fact. Firstly, the said system should be as transparent as it can be. It should share what procedure, algorithms, and techniques it is using. Secondly, the system should be able to reduce the noise factor. If the user gets confused because of meaningless correlations thrown out at the user, it will erode the user’s trust in a breath. The system should choose the best possible algorithm and analyze data appropriate for the issue being faced. Moreover, the system’s analysis should be polished with time after receiving feedback from clients or users.

In the end, these procedures should be utilized appropriately in businesses and employed creatively. For instance, selecting data points in a chart and then doing analysis using a tool is the right way, or you can ask the client to give you more context instead of general assumptions.

A Data-Driven Approach

With the combination of machine learning and business intelligence, there can be a crucial development in the tech industry that can further empower the industry with data insights, boosting adoption stages and hence augmenting the organizations with the customer at the center of everything.

Assisted insight capacity has also been announced now in the latest releases in the industry. This approach can help different users in unveiling valuable insights by utilizing machine learning and business intelligence.

The hour’s need is to keep thinking in the most intriguing and best ways and think about them hard in business intelligence workflow. You also need to find out the context and maintain it making sure that noise is decreased. Building trust and confidence is also pertinent. This industry demands to keep the requirements and needs of diverse clients as the top priority. Quick and straightforward insights ability can provide businesses with their growth if their dashboards are easy to use. In the case of expert data analysts, assisted discovery abilities can be helpful.

Developers can allow externally produced data models to be brought in or brought live from professional data science sectors like LogicPlum. If we are to find new avenues in businesses in this age, business intelligence is the answer.