How to Use Content Analytics to Make Better Business Decisions

By Jason Mitrow | September 25, 2018

Making a decision is never easy. But it can be especially challenging to do so with confidence in the business world, where executives are frequently called upon to make fast calls with less than optimal information, and often millions of dollars and customer expectations riding on the outcome.

Fortunately, technological advances are turning the art of decision-making into a science, using thousands of data points to help you more precisely and accurately determine your next move. One of the advances helping enterprise-level executives make decisions is the growing field of content analytics, which uses advanced data extraction to exponentially increase the depth and breadth of analyzed content decision makers can access before making their final call on a given matter.

Keep reading to learn how content analytics can improve both the efficiency and results of your decision-making process.

How technology is changing decision making

Whatever kind of decision you are trying to make – and whatever the means you are using to try to make it – decisions all come down to this: using the information at hand to make what you believe is the best choice in a given situation. Even when we use our intuition, we are still using information that we’ve processed, albeit unconsciously, and that is now presenting itself as a gut feeling.

With the advent of automated content analytics, a precursor to machine learning technologies, businesses can now lean on technology to analyze vast amounts of qualitative and quantitative data in order to more accurately model and predict outcomes. While previously, executives were limited to the amount of information they themselves could access and process in making a business decision, content analytics can leverage a broad portfolio of data in order to inform an action or strategy. The reason content analytics is so powerful is that it enables the analysis of vast amounts of data, more than could ever be explored manually, in both a quick and cost-effective manner.

Before businesses can leverage content analytics, they must first take a look at the state of their data. Though enterprises create large amounts of rich data, the vast majority of it – up to 80 percent, according to industry analysis – is unstructured. This means it exists in varying formats – some digitized, some not – and cannot be readily analyzed. Content analytics looks within this “dark data”, analyzing the insights it contains. The end result is information that is transformed into clean, universal formats, ready for the application of intelligent automated models and algorithms.

The two types of decisions that count: the ones that make you money

It’s commonly estimated that the average person makes 35,000 conscious decisions a day. Of course, not all of these decisions carry the same consequences: while opting for a salad as your choice over fries may benefit your health, your choice of a side isn’t going to have the same broad impact as, say, deciding to pursue or discontinue a key strategy within your organization. And while there are many types of decisions you may have to make within the enterprise sphere, the ones of importance all eventually come down to revenue. As such, there are two types of decisions that count: the ones that make money, and the ones that save money.

How can content analytics help you make money? While a McKinsey concluded that the pharmaceutical industry as a whole has yet to fulfill the potential of advanced analytics, one example of how content analytics could improve bottom lines is by making it easier to integrate multiple data sources, connecting previously explored and unexplored dots within separate clinical trials. Such analyses could then reveal potential new uses for existing drugs, opening new markets for products that have already been developed. Within the financial services industry, another McKinsey report cites a consumer bank in Asia that sought to improve its metrics on products per customer. After using content analytics to identify 15,000 microsegments within its customer base, the bank was then able to develop a next-product-to-buy model that increased the likelihood of adding a product three times over.

…And the business decisions that save money

But content analytics can do more than help companies grow revenue – it can also help to identify and decrease the risk of compliance issues, costly redundancies and other issues.

Within the financial sector, large banks and financial institutions are now required to deliver annual Resolution and Recovery Plans, a massive undertaking that is part of the Dodd-Frank Wall Street Reform and Consumer Protection Acts, aimed at preventing future economic crises. This reporting requires the analysis of millions of pages of data, a task that is virtually insurmountable. One problem, however, with failing to get through all that information? The fines for non-compliance can be astronomical.

In life sciences, McKinsey estimates that there is $100-billion in savings to be had within the U.S. healthcare system simply from better data utilization. One area where these savings can arise is research and development. Though the data created during product development may reach the petabyte level, once trials are concluded, much of that data is shelved. However, given that many drugs have multiple applications, content analytics could take any data generated and make sure it’s better classified in order to be applied to future studies. This could help to reduce redundant research and costs associated with developing drugs that don’t ultimately make it to market.

Wrap up

Content analytics is improving the ease and efficiency with which businesses are able to turn data into decisions. Whether these decisions help to grow revenue or prevent losses, there’re big impacts to be made from better data management. If you want to read more about the benefits of analyzing your existing data, check out this article on our blog: Contract Analytics: The Missing Piece in Recovery & Resolution Planning.

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