Advanced analytics is being deployed in a range of use cases across business units and industries, especially as data types and volume increase. One of the top use cases for advanced analytics we see at TDWI is predictive analytics to understand customer or operational behavior. Statistical as well as machine learning models are used to find patterns in this data. Marketing and operations are two departments where predictive analytics is used frequently. For instance, organizations are building models to predict churn or fraud. More often, we see predictive analytics used to detect issues in operational systems before they become problems. This can involve real-time analysis of big machine-generated data, aka data from the Internet of Things (IoT). These models are often put into production to automatically score new data as it becomes available.
We also see organizations using text analytics to understand unstructured data. In this scenario, text is viewed as another form of data. Organizations might use text analytics to extract entities, concepts, or sentiments to use in conjunction with other “structured” data sources to perform analysis. They might use machine learning techniques for natural language processing (NLP).
There is clearly value to be gained from advanced analytics. In fact, at TDWI we consistently see that organizations that become more sophisticated with analytics are more likely to measure top- or bottom-line impact. In many ways, the use of advanced analytics is part of a success cycle: As organizations see success with their analytics program, they start to do more. As they do more and as they gain more experience, they tend to see positive results. This success builds on itself and is perhaps one reason why those that are more analytically advanced tend to be more satisfied and measure impact. In other words, there is tangible value to having a mature program.
Of course, advanced analytics maturity is not just about the technology. It also includes the cultural and organizational processes that enable companies to become more data-driven. This includes organizational structures as well as the processes for a wide range of people to manage, govern, and utilize the data and analysis. I recently completed the TDWI Advanced Analytics Maturity Model. The Advanced Analytics Maturity Model can help guide business and IT professionals on their analytics journey. It provides a framework for companies to understand where they are, where they’ve been, and where they still need to go in their predictive analytics and machine learning deployments. The model can also provide guidance for companies at the beginning of their processes by helping them understand best practices used by enterprises more mature in their deployments.