Pretty much everyone I talk to has heard the term "business analytics." Business analytics refers to the skills, technologies, applications and practices for continuous, iterative exploration and investigation of past business performance to gain insight and drive business strategy.

In short, business analytics helps companies turn data into actionable information to drive performance. It encompasses virtually all data analysis activities a company performs and includes predictive analytics and modeling, performance management, BI and data management.

Most readers are very familiar with BI, as well as performance and data management. However, many are less familiar with new, more sophisticated tools and technologies supporting predictive analytics and modeling. It's crucial to become familiar with these technologies, because most businesses will need them soon to unlock the hidden potential in their fastest growing data sets - those that are built of unstructured content.

Unstructured content is information contained in non-database sources, including e-mail, HTML/XTML content, SMS, blogs, audio, video, scanned documents, etc. These types of data don't lend themselves to modeling with traditional business analytics tools. It's been my experience that the volume of unstructured data is growing incredibly quickly within most organizations. As more customers make use of different channels of interaction, this volume can only continue to increase. To mine this data gold, companies must use more sophisticated predictive analytics and modeling analytical tools.

The need to remain competitive compels companies to invest more in advanced analytics technology solutions to help them gain deeper insight into business and market information. To gain a competitive edge, companies are using these advanced technology solutions to more effectively monitor and manage performance, deliver better information to decision-makers and improve insight and outcomes.

One term for using predictive analytics technologies to apply complex analysis to unstructured data is deep analytics. Simply put, deep analytics supports meaningful analysis of data from everyday customer interactions and business events, no matter the source, which drives more effective performance management and decision-making.

You can't just buy deep analytics software off the shelf and start using it. Well, you can, but you probably won't be able to use it very well. More practically, you can follow a disciplined approach to implementing a deep analytics solution. There are several core principles that, if followed, can support an effective implementation of deep analytics technologies. They are:

Knowing which questions matter most, and where to find the answers. Asking the right questions of the right sources requires three things: intimate understanding of the business strategy, broad functional capabilities and an understanding of how to make the best use of technological capabilities to support knowledge acquisition.

Signal detection. Every business event sends signals. Learning how to quickly recognize and respond to these signals allows companies to use deep analytics technologies to gain more insight into their business performance. They can use this insight to improve their ability to meet and exceed customer expectations.

"Right fit" analysis. To make the most of deep analytics technologies, it's crucial to use the right tools for the right purpose, i.e., to apply the correct statistical and analytics techniques to the job at hand. Using the wrong tools wastes time and money, and can cause your business to miss important insights.

Visualization. The results of analysis must be understood by the business. In other words, it's critical to get the right information to the right people - in whatever forms they need, in a format they can understand.

Analytics automation. It's crucial to automate (as much as possible) the delivery of information to people who need it to do their jobs. It's also worthy to strive to get that automation implemented at a low cost, so that funds can be spent analyzing information, responding to that information and growing the business.

Commitment and culture. A fact-driven culture drives a commitment to higher levels of discipline and accountability in decision-making. Using facts in decision-making doesn't replace gut instinct, but it does reduce the need to fly by the seat of your pants.

These key principles can only be met with a disciplined approach to implementation. It starts with a roadmap focused on understanding the challenges and defining strategies to meet those challenges. It's also crucial to discover and address data-related issues and solve those issues as the implementation progresses. Finally, it's incredibly important to champion high quality information across the enterprise and to help business users understand how newer, deep analytics technologies will facilitate better performance and decision-making.

It's a simple equation: more sophisticated analysis supports more effective decision-making. Better decisions lead to better performance. Better performance will likely lead to increased competitiveness and growth. That's not a bad deal.


Deloitte is not, by means of this article, rendering business, financial, investment, or other professional advice or services. This article is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business.

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