Big Data

Structured vs. Unstructured Data in Predictive Modeling

You have undoubtedly heard of Big Data. It has become something of a buzzword in recent times. Big Data refers to the vast quantity of data that the world generates nowadays. There is certainly considerably more data available now than there has been in the past, partly because there are many more ways of obtaining data than ever before.

Of course, an overload of data is not beneficial to anybody. It does not take that much data to cause a user’s eyes to glaze over as they feel plagued by sensory overload. Do you, like most people, have an email inbox full of unread emails that you will probably never get around to opening? That is just one area where you may be suffering from data overload.

People are only really interested in information. But what is the difference between information and data? Information is data that is useful and actionable to the person receiving it.

Structured Data vs. Unstructured Data, What’s the difference?

In some ways, things used to be simple. If you were in business, the bulk of the data that you received was structured. Structured data is organized data. It follows a consistent pattern and is easily stored and searchable. Accountants, in particular, love their structured data, nicely presented in a clean, orderly spreadsheet. Traditional databases were based on structured data, it made it easy to catalog and search for relevant data.

Advances in processing power and other technologies have dramatically increased the possibilities of collection and organization of unstructured data.  Thus creating the accountants’ nemesis.

Unstructured data lacks the neat, orderly, tidiness of its structured brethren. Unstructured data is the wild teenager of data – scattered, free-flowing, often defying easy description, frequently hard-to-access and query, and often in need of major preprocessing before it has any chance of being converted into useful information.

Despite all of these challenges, unstructured data has the greatest potential to provide the critical information necessary to result in optimal business decision-making.

Using Big Data to gain insight in the call center

Structured data is normally created in a relatively traditional way. In the case of call centers, somebody in operations would most likely capture data such as number of calls taken, average handle time, average hold time, etc. and that data would be entered into a database. This is really only an updated version of how people collected data a century ago. In those days, the file would have been on paper, and the details would have been typewritten, or maybe even handwritten, but the data passed between the client and the call center would have been similar.

More recently, however, the types of data added to electronic files have widened, creating big data. These now include audio files, video files, emails, attached text files and numerous other types. Initially, these were just items attached to structured data. For instance, for an audio file to be useful it would have had to be transcribed into structured data first.

However, we are well past that point now. We can preprocess the non-traditional files to extract useful information. We can even use this preprocessed data as the heart of a predictive analytics model.

Translating Call Center Big Data into Predictive Analytics

A predictive analytics model, using some combination of data mining, text analytics, audio analytics and video analytics can group and summarize data in ways never previously envisaged or imagined.

One example is with Rank Miner’s predictive voice analytics. This service would have been impossible before the invention of advanced computer processing power and systems to deal with vast quantities of audio information.

RankMiner’s predictive platform gives call centers the ability to target more profitable customers and improve their agents’ performance through better training and automated evaluations.  Its machine-learning algorithms have proven to be effective in multiple call-center business operations across multiple languages. Artificial intelligence is used to automatically analyze and predict Agent & Customer success for call center companies:

  1. Customer Insight
  • Predict which customers are likely to say “Yes” and which will say “No”
  • Prescribe where to place additional resources and where to stop wasting time
  1. Agent Insight
  • Quality Assurance Predictive Models to improve agent performance systematically
  • Employment Predictive Models in development to reduce employee attrition and improve training

A considerable quantity of unstructured data was used to create predictive models that determine what vocal intonations and tones are indicative of certain emotions and behaviors. Additional data is constantly being collected and collated to refine the accuracy of the process. Similarly, it is only today’s processing power and advancements in digital signal processing technology that enables non-structured voice data collected on the fly to be instantly compared to the existing data records to enable real-time predictive voice analytics.

Final Thoughts

It does make one wonder, however. If we have already come this far with our predictive modeling utilizing unstructured data that would have previously been impossible to collect, what does the future hold? What will our predictive models be like one or two years from now?   What potential value can your business gain by being able to predict human behavior?

Learn how to revolutionize the way your call center does business by using predictive analytics!

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