An Introduction to Advanced analytics and data mining

490 views

Published on

This presentation introduces the basic ideas of advanced analytics, data mining and the data mining process

Published in: Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
490
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
23
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

An Introduction to Advanced analytics and data mining

  1. 1. An Introduction to Advanced Analytics and Data Mining Transforming Data Dr Barry Leventhal April 2014 © Copyright BarryAnalytics Limited All Rights Reserved
  2. 2. Agenda • What are Advanced Analytics and Data Mining? • The toolkit of data mining techniques • Some issues to keep in mind Transforming Data • Which technique should you use?
  3. 3. A process of discovering and interpreting patterns in (often large) data sets in order to solve business problems What is Data Mining? Transforming Data Converts Data into Information Pattern Information ActionData
  4. 4. What is Advanced Analytics? “Any solution that supports the identification of meaningful patterns and correlations among variables in complex, structured and unstructured, historical, and potential future data sets for the purposes of predicting future events and assessing the Data Visualisation Web Analytics Text Analysis Transforming Data purposes of predicting future events and assessing the attractiveness of various courses of action. Advanced Analytics typically incorporate such functionality as data mining, descriptive modelling, econometrics, forecasting, operations research optimisation, predictive modelling, simulations, statistics and text analytics.” (Source: Forrester Research) Data Mining Contact Optimisation Simulation Social Network Analysis
  5. 5. How can Advanced Analytics help? • By helping companies to increase revenues or reduce costs increase revenues reduce costs Transforming Data improve profit Tom Davenport: “Companies have long used business intelligence for specific applications, but these initiatives were too narrow to affect corporate performance. Now, leading firms are basing their competitive strategies on the sophisticated analysis of business data.”
  6. 6. Where can Advanced Analytics add Value? Store location Product management Customer Transportation /Fleet management Resource planning Transforming Data Customer management Web site management Anywhere else where I have large numbers to manage planning
  7. 7. The Toolkit of Data Mining Techniques Transforming Data Traditional Statistics • Regression Models • Survival Analysis • Factor Analysis • Cluster Analysis • CHAID Machine Learning • Rule Induction • Neural Networks • Genetic Algorithms
  8. 8. Two main types of Analytical Model Type 1: Models driven by a Target Variable e.g. Which customers to cross sell? - Implies building a Predictive Model - ‘Directed’ Data Mining Techniques Transforming Data - ‘Directed’ Data Mining Techniques Type 2: Models with no Target Variable e.g. What are our most important customer segments? - Implies a Descriptive Model - ‘Undirected’ Data Mining Techniques
  9. 9. The Data Mining Process Transforming Data Data Cross Industry Standard Process for Data Mining Reference: Step-by-step data mining guide CRISP-DM 1.0
  10. 10. Some issues to keep in mind: Issue 1: Use an appropriate technique • Some years ago, the DMA Targeting & Statistics Group held a seminar to explain and compare four analytical techniques: – Cluster Analysis – Decision Tree – Neural Network (supervised) Transforming Data – Neural Network (supervised) – Regression Model • The four techniques were applied to a sample of lifestyle data in order to predict private healthcare cover
  11. 11. Comparison of private healthcare targeting via four analytical techniques 300 350 400 Some issues to keep in mind: Issue 1: Use an appropriate technique Transforming Data 100 150 200 250 10% 20% 30% 40% 50% Cluster Analysis Decision Tree Regression Model Neural Net Source: CMT/ DMA Targeting & Statistics Interest Group
  12. 12. Issue 2: Modelling and Deploying are separate stages in the data mining process Historical Data + Known Outcomes Modelling Model Deploying Transforming Data • “Modelling” is ‘one-off’ – until model requires rebuild • “Deploying” takes place repeatedly Recent Data + Deploying Model Predictions
  13. 13. Issue 3: Do not forget your data! Your data is the key to gaining value from analytics and modelling – essentials to consider: • Data quality Transforming Data • Data predictivity • Data integration • Data governance
  14. 14. The Importance of Data Integration Customer Transactions Customer Attributes Integration • Business value increased by integrating complementary datasets • New insights may be created by data integration, e.g. Transforming Data Market Research Online Behaviour Integration Many applications of data integration... • Predictive models to target behaviours identified by research • Integration of web, email and traditional offline channels • Tracking across channels, e.g. Attribution of media effects
  15. 15. Which analytical technique should you use ? The choice generally depends on… • business problem • whether problem is predictive or descriptive • underlying data environment • variables to be predicted or described Transforming Data • variables to be predicted or described • ability to implement solution • whether key statistical assumptions hold • Obtain help from a Statistician or Data Mining Consultant • All about the problem, not the technique • Combination of approaches works best
  16. 16. Thank you! Barry Leventhal Transforming Data +44 (0)7803 231870 Barry@barryanalytics.com

×