Pieter Vorster Chief Analytics Officer. BIG data. & insights. analytics

Transcription

1 Pieter Vorster Chief Analytics Officer BIG data analytics & insights 1

2 English language 296,000,000 results (0.70 seconds) BIG data 283,000,000 results (0.48 seconds) BMW 209,000,000 results (0.69 seconds) Porsche 108,000,000 results (0.87 seconds) BIG data is still very popular and well searched 2 2

3 Business analytics

4 It takes work and skills and its not that easy

5 Besides dissolution, the World is investing? It s an evolution to driving a data economy

6 Are we on the right track? Insights Culture Traditional Analytics Big Data Data Economy 1950 s Today Today - Tomorrow Analytics is a to-do item on the strategy Experience and intuition decisions made first Monthly Weekly R-T Value seen in Big Data and is an action time in most areas Alignment of a few key data sources to process data requirements Agility in decisions made, driven by the speed of insight from data Analytics culturally evolved and embedded organisation wide (products and services) Analytics Analytical models usually developed outside the process and are time-consuming Periodic and descriptive outputs New computational capabilities (e.g. Hadoop) used to work with larger, wider and unstructured data sets Experiential and hacking ethos Rapid, automated and agile insight delivered at each business process >90% prescriptive analysis with active insight measurement Data Data sources are smaller and structured Stored in data warehouses and/ marts Complex, large and unstructured data sources Focused on finding new relationships in datasets (i.e. predicative analytics) Analytics integral and embedded to business processes, people and technology no decision is made without analytical reasoning Talent Analysts segregated from business and people See more as a support function than part of the process Data Scientists surface within various parts of the business Proactive, sometimes forced, learning around computing capabilities Hybrid, specialised teams function centrally and within the business units Chief Analytics Officers embedded and manage link between data and IT Technology BI/ MI Reports Dashboards Data Warehousing Data Marts Hadoop Cloud Storage R Machine Learning Open Source Python New Data Architectures Mobile Apps Visualisation Insight Push Internal Data Sources Internal and External Data Sources Auto Insight Generating Data 6Sources Source: International Institute for Analytics

7 Data Footsteps Everywhere and creating new data IoT

8 Customer analytics complements the business cycle The Customer Cycle Insight Customer analytics Action Customer operations The Business Cycle Data Centric Insights Integrated Consumer Experience Process Centric Action Target to Cash Source: IBM

9 Pregnancy The holy grail of life moments and events Understand customers normal behaviour Know where they live Cognitive computing : Delta Get sued Habits: 45% of choices Demographics, credit data, Triger habits 9 9

10 Spend Analytics The key to understanding your behaviour

11 Absa Pocket Flow Merchant spend analytics

12 Minimum Viable Product (MVP)

13 Minimum Viable Product (MVP)

14 Minimum Viable Product (MVP)

15 Minimum Viable Product (MVP)

16 Omni Channel Analytics The key to understanding your habits

17 Omni-Channel focus areas Customer Interaction Operational campaign to improve operational cost by moving customers to lower cost-to-serve channels Front-end channel measurement to evaluate customer interaction in order to optimise staff utilisation Tracking of Digital Assets A/B testing to optimise customer journey and enhance experience via a cloud based solution Authenticated and non authenticated across all digital assets Marketing Channel Behaviour ROI tracking of marketing on digital channels Enhanced content strategy based on customer channel interaction behaviour (internal and external) Overlay of customer behavior on channel behavior to improve campaign Profile customers based on behaviour Striving to build a single customer view 17

18 Omni-Channel in action Channels Big Data Platform Outputs Channel One Channel Two Channel Three Channel Four Channel Five Tagging and Log streaming Channel Interaction Reference Data Transactional Behaviour Channel Behaviour Customer Demographics Digital Propensity Reporting Marketing Ops. Costs Customer Behaviour A solid, scalable platform is the key to deliver actionable outputs 18

19 So how does it all fit together? Bankmed Google Play Twitter Branch Reuters ATM Apps Individual Mobile Banking Online Banking Facebook Omni-Channel empowers us to personalise execution to resonate with individuals through the most efficient channels Bloomberg Portals Instagram Forbes Wall Street Journal

20 Omni Channel to AI Ability

21 AI : Where is Africa in terms of ability

22 AI in practice : BankBot (video)

23 Delivering excellence in customer service through innovation 23