Ensuring big data is supporting financial analytics

1. Ensuring big data is supporting financial analytics • Gaining a thorough understanding of big data in order to understand analytics • Transforming unstructured data into structured intelligence • Using big data to predict client behaviour BhavaniRaskutti@ Pacific Brands

2. Ensuring big data is supporting financial analytics • Gaining a thorough understanding of big data in order to understand analytics • Transforming unstructured data into structured intelligence • Using big data to predict client behaviour BhavaniRaskutti@ Pacific Brands

3. Agenda • Big data • Unstructured data • Framework for embedding financial analytics • data analysis leading to decisions that • impact company financials BhavaniRaskutti@ Pacific Brands

4. Big Data Big Data “Big data” is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. -- Gartner Inc. 2001 • Volume: Number of entries (rows) – NOT attributes (columns) • Velocity: Rate of change, usually rate of arrival of rows • Impacts volume • Variety: attributes (columns) of entries • Structured: • Transaction data: value, timestamp, type, location, … • Customer data: gender, age, occupation, … • Unstructured: • Text: Customer interactions, blogs& twittersabout the company … • Image: Customer signature, photo, ….

5. Big Data Predictive Analytics to Make DecisionsPredict fraudulent credit card transactions 1. Learn a model from historical data • Back-end process • Time consuming • Built from sampled data • Volume NOT an issue Classification Known Categories Clustering Classification NO Known Categories 2. Categorise new data to make decisions • Models simple • Quick process • Velocity & Volume NOT an issue New data

6. Big Data Impact of Variety on Analytics

7. Big Data Impact of Variety on Analytics

8. Big Data Impact of Variety on Analytics

9. Big Data Impact of Variety on Analytics

10. Big Data Impact of Variety on Analytics

11. Unstructured Data Structuring Unstructured Data 1. Sentiment Analysis 2. Topic Detection Get freeform source text Load stop words list: the , on, of, a, an, by, … Load sentiment word list love, best, … hate, worst, … Get freeform source text Create clusters with term frequency matrix Score sentiment: +1 for positive words -1 for negative words Normalise if needed Business input Determine clusters of interest and label Use in Business: For model building Competitor comparisons Mood change over time Addressing negative scores Learn cluster models for classification Use in Business: For model building Major customer issues Impact of initiatives

12. Unstructured Data Sentiment Analysis Output No sarcasm detection

13. Unstructured Data Topic Detection Output

14. Big Data Impact of Variety on Analytics Variety NOT an issue Financial Analytics NOT impacted by Big Data

15. Big Data Impact of Variety on Analytics

16. Big Data Impact of Variety on Analytics Do BIG analytics with enough data!!

17. Framework for Embedding Analytics Initiative Framework $ • Big $ impact • Many people • Timely • Sell to whom, what & $$ • Ordered by $$ • Deploy • Just the facts needed for decision making • Prioritise entries • Support actioning • Decision • Pilot Right People • Data • Insight • Decision Support Initiative for Wholesale Sales

18. Example of Additional Support for Actioning Framework • R1 • R2 • Possible reasons for difference • Competing product at R2 • Pricing at R2 vs R1 • Lack of stock at R2 Sell Rate Demand In-stock % • Sell rate vsConsumer Demand plot • Each point is a store • R1 & R2 are comparable retailers • Values for the same product Demand

19. Framework for Embedding Analytics Initiative Framework • Automate • Helpdesk • Training • POS feed from retailers • SKU & store master • Automated feed • Objective Data • Most specific • Complete $ • Pick champions early in the process • Develop pilot • Validate outputs from pilot • Iterate pilot with champions until it is accepted • Big $ impact • Many people • Timely • Sell to whom, what & $$ • Ordered by $$ • Deploy • Just the facts needed for decision making • Prioritise entries • Support actioning • Decision • Pilot Right People • Data • Insight • Decision Support Initiative for Wholesale Sales

20. Conclusion • Big data is not an impediment to analytics • Unstructured data can be structured using sentiment analysis & topic detection • Key success factors for doing BIG analytics is to have the right people to choose • Right decisions • Right insights • Right data • Right process to pilot & deploy

21. Questions?