Data, data, data! IBM estimates that 90% of the data in the world today has been created in the last two years. When we use our computers, tablets and smartphones, immense volumes of data are being generated. Companies are tapping into this data as a potential source of competitive advantage. After harnessing this data, predictive analytics provides a means to create new insights so that more informed judgements can be made by decision makers throughout the organization. As data becomes more accessible, several trends will be transforming the way organizations are using data and analytics:
- New Generation of Analytics Tools
Traditionally one of the first questions asked when undertaking an analytics project has been the kind of data that was being analyzed: structured or unstructured? Conventional organizational data is of the structured variety and lives in databases supporting ERP, CRM and like systems. However, with the rapid creation of data about 80% of the data is now unstructured. Examples of unstructured data include posts on social media (tweets, blogs, posts, etc.), human created notes, images, and open-ended responses to survey questions. Today’s trends indicate that increasingly structured and unstructured data are being presented together in the same analysis. The dilemma is that legacy tools often can’t easily analyze unstructured and structured data together. Thus there has been a greater demand for more powerful analytics tools.
- Time Required to Deliver Analytics Value will Decrease
The times of 60-90 days to show value are over. Increasingly the timeline to show analytics value will compress. We are talking about analytics projects taking 30-45 days more frequently, due to the many technical advances. Increasingly in the future we will be talking in terms of days rather than weeks for analytics projects.
- Larger Computing Trends will Manifest in Analytics
Apple’s Siri is an example of an early stage Natural Language Processing application. Analytics tools, such as IBM’s Watson technology platform, use natural language processing and machine learning to reveal insights from large amounts of unstructured data. The ability for computers to run learning algorithms in near real time with the increase in computing power will present itself in more applications. Eric Schmidt, co-founder of Google, has stated that algorithms have always been there, but it is the computing power that has caught up. I couldn’t agree more.
How widely is predictive analytics being used? A short answer is over all organizational sectors because of its potential for changing competitive dynamics. Some common uses of predictive analytics are as follows:
- Fraud detection and cybersecurity. Health insurers, insurance companies and credit card companies all use predictive analytics to quickly identify abnormalities.
- Managing operations. Airlines determine how many tickets at particular price points to sell for specific flights. Hotels try to maximize daily occupancy rates by adjusting prices. Credit scoring organizations try to assess the likelihood of default.
- Marketing. Companies are using predictive analytics to attract and retain the most profitable customer segments.
- Sports. Teams are using predictive analytics to maximize revenue, to scout players and even to make game time decisions.
These examples provide just a few ways in which predictive analytics are being used. Manufacturing companies, retailers, media and entertainment companies, public sector organizations and utilities among others are all using predictive analytics to increase revenues and improve performance within their organizations.
If you’re in the Chicago area and interested in continuing this discussion, you can join J-P Contreras in DePaul University’s Business Analytics Certificate Program where he will introduce you to the field of Big Data and Analytics. You will gain insights into what is possible with analytics, how you can benefit and how you can take advantage of the data that is now available to you. The next program begins Friday, February 26. For more information, go to http://cpe.depaul.edu/businessanalytics.