As businesses get more sophisticated the dash for that mindshare has become much more complicated. Businesses have become much more competitive thanks to the advances in technology. Supply chain networks have grown to the point that the whole world is right up our alley. In this world of super competitiveness surviving is a merit in itself. However, you have to stay up to pace with what your competitors are doing to even have a shot at that.

Data drives the world now. This is one of the largest reason behind the rise in competition in every business sector. Large organizations have been using Business Intelligence software to their advantage for a long time. For the past couple of years, the technology has become available to small business owners and entrepreneurs too. Those who are making good use of it are winning the race.

To understand how Artificial Intelligence is changing data analytics for businesses, we first need to understand how data analytics relates to business intelligence. Business Intelligence software has become a huge aid for managers all around the world. This software help executive managers make better decisions regarding the company policy direction, help marketing managers engage their customers well and help operating managers make better decisions to reduce costs and overheads. However, Business Intelligence software is more of a descriptive analysis software where it only tallies the relevant data together and puts them in one place. Rest of the work which consists of analyzing and interpreting lies with the manager.

How AI Is Changing Data Analytics

The trend over the last couple of years in the business world has been pointing towards self-servicing. Business Intelligence software first emerged in the late 1980s and due to its heavy reliance on specialists only large players could take advantage of it. After a decade passed, the technology reached a point where small businesses with non-techy managers could start to take advantage of it.  However, it was still dependent on experts. The technology of the past generation was focused on descriptive analysis, to be even simpler it mainly focused on the process of business reporting.

From 2005 onwards the trend shifted more towards data discovery, data prep and data visualization. However, data prep and visualization is not really sufficient to make managers truly independent. The goal of taking in business data is to ultimately make better decisions. Two more steps are required to get to the point of good business decision making: prediction and suggestion. The latest trends have been moving in the right direction as self-sufficiency seems to be around the corner. To understand how self-sufficiency can be achieved by using Artificial Intelligence and Machine Learning, we first need to understand these terms more vividly.

What Is AI?

Artificial Intelligence originally focuses on emulating complex human actions and interactions. However, from a business point of view AI focuses on creating systems that use the data available to tackle specific problems. In business, AI professionals build intelligent systems based on machine learning algorithms that can solve specific problems. As a result, the implementation of AI in different organizations is different.

AI and Data Analytics

The working field of AI in businesses and data analytics have overlapped through the last few years. Increasingly the roles of data engineers, data scientists, and AI experts are getting mixed up and convoluted. A brief overview might help.

Data engineers are tasked with the job of building systems that can group together relevant data to solve the problems the organization seeks to solve.

As for data scientists, they are tasked with the job of manipulating the data brought in through the system built by the data engineers. Data scientists have to find meaningful connections in the available data and recommend solutions to problems of productivity and identify market trends. Both descriptions are very generalized and both these professions hold a lot more to it.

As I have already mentioned, the trend in the business world is shifting towards self-servicing. Data engineers are now focused more towards creating scalable data infrastructure that can be easily used to prep data and visualize it. Data scientists are now focused on building data products for non-techy mangers which can help minimize the time to make critical decisions. Here is exactly where Artificial Intelligence comes into play.

From Descriptive to Predictive Analysis

The big leap that Artificial Intelligence is helping the data analytics industry to take is the leap from descriptive to predictive analysis and even further to providing suggestive actions. From a business perspective, AI is the creation of intelligent systems that can use Machine Learning principles to continuously evolve to provide faster and more efficient business solutions. For too long Business Intelligence has been only focused on providing a descriptive analysis of transactions and other business activities. The introduction of AI and Machine Learning in the world of data analytics has resulted into products that can take into account large sums of data and make predictions, find patterns and help minimize lag in making critical business decisions boosting productivity and reducing costs.

What the Future Holds?

As I have already future trends dictate that all business entities from large conglomerates to small businesses will move to use predictive analysis and programs that create suggestions to solve the problems at hand. AI professionals are creating products that can account for natural language queries and analyze sentiments behind words. As the data keeps on growing, the systems will only get better at recognizing the things that bring in customers and keep them engaged. Data analytics will become more than just a tool to visualize data to help make decisions. Tools will be available to even suggest the best possible solutions to certain situations. The future holds much more streamlined business decisions but also fierce competitiveness.

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