The ins and outs of data mining analytics on digital data
“Data Discovery: Tell me something that I don’t know” is the definition of data mining – discovering unexpected patterns and relationships in data. In this session Neil explores the approach to insight generation through data mining and predictive analytical technologies.
Using real world case studies he covers the ins and outs of data mining analytics on digital data, which types of techniques can be used to solve which kinds of problems and some of the challenges that you will inevitable face along the way. Discover what your data can tell you if you ask it the right questions.
Data Mining Process and Predictive Analytics
In Part I (out of three), Neil discusses the difficulty of extracting signals from all the noise present in the overwhelming quantity of data we deal with nowadays. While we now have tools for free and the cost of collecting data is diminishing, Neil questions: “what do we do with all this data?”
He introduces other tools that can help us to address some of these challenges:
- Data Mining is about discovering things we don’t already know. It is about uncovering patterns and relationships in data that we may not have already thought about.
- Predictive Analytics is using that understanding to think about what might happen in the future; it is about applying those historical patterns to predict those future outcomes.
Neil goes over the Data Mining Process and the steps needed in order to implement it and, more importantly, to get insights out of it:
- Business Understanding: what problem are we trying to solve? What is the business trying to achieve?
- Data Understanding: do we have the data to be able to answer this questions? If not, what is the cost of acquiring that additional information?
- Data Preparation: all data is dirty and needs to be cleaned and transformed. This is the heavy lifting stage.
- Analysis & Modeling: the tools must be chosen based on what the business is trying to understand and the data available.
- Evaluate Outcomes: how well does the model actually works from a statistical point of view (significance) and from a business point of view (actionability)?
- Deployment: driving the insight into the business.
Neil Mason joined Foviance as part of an acquisition of Applied Insights whom he was director and co-founder. With 25 years of in-depth industry experience in marketing analytics and strategy, Neil leads Foviance’s analytical consulting practice. This delivers an enhanced digital marketing analytics capability to both Foviance’s and Applied Insights existing and future clients.
Neil is one of the world’s leading analytics guru’s and he has a big reputation. He holds an MBA from Kingston Business School, a Diploma in Business and Economic Forecasting and currently serves on the Board of Directors of the Web Analytics Association, the global industry body for digital analytics professionals.