A couple of weeks ago I attended Predictive Analytics World (#PAWCON), a multi-city conference focusing on how organizations are leveraging predictive analytics within their organizations. Much of the discussion surrounded “big data” and how to leverage analytics within environments that contain large, complex, and real-time related data.
A couple of weeks ago I attended Predictive Analytics World (#PAWCON), a multi-city conference focusing on how organizations are leveraging predictive analytics within their organizations. Much of the discussion surrounded “big data” and how to leverage analytics within environments that contain large, complex, and real-time related data. I really enjoined the conference because of the fact that most of the attendees were trying bridge the roles of technology and analytics with business insights through predictive modelling and most of the speakers made a point of speaking about the ways to use data to leverage business insights. For instance:
Jane Griffin from Deloitte talked about the ability to move beyond traditional software towards a more agile environment and understanding that managing big data requires the right people with the right skill sets to develop statistical realities while coupling them with the ability to ask the right questions- Emma Warrillow from the Data Insight Group and a marketing strategist, discussed churn modelling as applied within predictive models and the complexities that arise based on the variables of customer behavior through churn and general segmentation
- Johan Forman from MailChimp looked at leveraging data to achieve a singular goal – achieving compliance and mitigating risk. This involved shutting down bad users and developing a strategy to identify what that meant and how to identify what that means specifically and who qualifies.
All of these areas relate specifically to people – customers, decision makers, data scientists, etc. The reality is that the use of predictive analytics requires the ability to translate the breadth and depth of existing data into valuable insights in a way that extends beyond simple reporting or dashboarding applications. One of the areas that was very obvious is that the newer iterations of BI are touting self-service and data discovery in a way that is easy for business users to interact with. The reality for predictive models, however, is that statisticians, data scientists, and those willing to ask complex questions and delve deeply into an organization’s information structure are the types of resources required to develop and maintain a successful predictive analytics strategy.
As more organizations mature within their BI use, the push towards predictive analytics will become commonplace. Businesses need to understand the requirements to expand into this area and make sure that they have the relevant resources to support these initiatives. After all, built-in or out-of-the-box analytics will only help an organization go so far. Without the proper skill sets which include a mix of business and technical, it becomes quite difficult to build a strategy that supports a strong predictive analytics environment.
(image: predictive analytics / shutterstock)
