Merging AI & Business Analytics

With the arrival of artificial intelligence (AI) and big data tools, business analytics is changing. Now, businesses integrate these technologies to gain a competitive edge as they come to understand consumers like never before. 

We have all the tools and capabilities to gather useful data that can guide all kinds of business decisions. With AI technology to aid in processing and filtering data, business analytics becomes easier and more precise. 

By merging AI with business analytics, data gathering, decision-making and customer engagement dramatically improves.

Here’s what AI means for your business.

Gathering Data

Data is a necessary component of AI within the context of business analytics. The more data the better, as artificial intelligence processes like machine learning can scour vast amounts of information, becoming smarter, the more it is fed. 

Modern companies are implementing technologies that collect a broad range of data on business and consumer practices. These technologies have AI and machine learning tools built in to effectively formulate increasingly accurate predictions and recommendations. Where a human might be overwhelmed with the task of compiling and analyzing/interpreting data, many intelligence tools are built for just that. 

Useful data for business analytics can be virtually limitless, but here are some of the common metrics that smart tools can use for recommendations and prediction:

●      Customer shopping habits

●      Movement data through stores and buildings

●      Expenses

●      Tax considerations

●      Competitor pricing

●      Product demand

●      Inventory

 A wide spectrum of data is always useful to have on your radar, but you’ll want data points that directly tie into your business needs. AI tools will learn over time how numbers and systems correspond and correlate to give you the best possible output, increase your revenues, and cut your costs. There’s a good reason data has become so essential in modern business.

Ohio University reports that 78% of marketers center their campaigns around customer data. Business professionals understand the value of data in every aspect of their standing, from production and marketing to day-to-day managerial and staffing tasks. The more data you have, the better a machine learning AI system can function. 

Making Better Decisions

AI may just be more important in business analytics than it is in any other field. Since analytics is all about data, no tools are more essential than those that can help interpret information for practical and reliable business decisions.

With AI and machine learning, business analysts can determine descriptive, predictive and prescriptive solutions to a wide variety of problems.

Descriptive Capabilities

One of the most valuable components of AI in business analytics is the capability of systems to harness raw data and translate it into more comprehensible forms. Business analysts rely on a host of information—sale times, numbers, and finance data being the usual focus—to paint a reliable picture of a company’s status. 

Business analysts in the age of artificial intelligence need to be able to tell a story. AI helps by creating useful graphs, charts and images to comprehend exactly what has occurred over a period so that analysts can more easily disseminate that information company-wide.

Predictive Tools

AI powered by big data is capable of predictive techniques to a degree no human can achieve. From examining supply and demand, competitor pricing, and a host of other factors, these tools crunch past data and apply it to real-time situations to give reliable predictions of future trends. While no human or machine could be expected to be 100 percent correct, artificial intelligence is more capable than a human in setting expectations for inventory, quarter growth, or supply versus demand. 

Some companies are even using AI in the search for ideal candidates for open positions. With the right data, this technology helps human resources predict which candidates will be best suited to the job based on cumulative company needs. 

In the future, nearly every business process stands to be influenced by some form of AI or predictive technology. 

Prescriptive Solutions

The ability of AI to offer analysts real-time information on the market gives them more power than ever in prescribing the right solutions to enhance business practices. Machine learning ensures that these predictions keep gaining accuracy as more and more data is fed to the system. Analysts are then free to compose recommendations and experiments based on data.

Keeping up with trends can be difficult, but AI offers solutions through comprehensive data that allow for simple interpretation. Now, analysts are free to understand the story the data tells and communicate that with coworkers and customers to enhance business processes and customer satisfaction. 

Bettering Customer Engagement

Effective use of AI tools in business analytics creates a cyclical effect of improved customer engagement and in return, better data. Through social media and other online platforms where customers can interact, engage and express themselves, analysts can gather the type of data necessary for a continuously streamlined business model. 

The usefulness of AI in instant customer response—be it email reply, chatbot or instant social media answer—is essential to both customer satisfaction and data gathering. When are your customers online? What problems are they having with the product? What are they saying about the product? All this and more can be generated from successful social media and customer engagement, powered and automated to some degree with AI.

The more analysts merge their processes with AI tools, the more descriptive, predictive, and prescriptive solutions can be found to any business problem. In today’s artificial intelligence-powered world, analysts can’t afford not to have the edge this technology provides.

With a cyclical and ever-growing system for streamlining a business, AI looks to be the best friend of tomorrow’s workers, regardless of its current reputation.