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If BIG DATA is the answer, then what was the question?

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Presentation on theme: "If BIG DATA is the answer, then what was the question?"— Presentation transcript:

1 If BIG DATA is the answer, then what was the question?

2 2 Strictly confidential and proprietary; Not for circulation What has changed with Big Data Zetabytes* Global Data Growth, 2009-2020Data Engineering Technologies Processing power Social feeds Machine-to-machine real time updates (e.g. from smart grids) Real-time data from transaction networks Faster Data Availability Big Data Databases Big Data Storage & Processing Streaming Data Collection & Processing Data Visualization Search Technologies Schedulers Relational Databases 2009201120152020 0.8 1.9 7.9 35.0 Data Sciences EvolutionDiversity of Data Complexity Time Reports & Alerts Statistical Analyses Forecasting & Predictive Modeling Stochastic Optimization VideoAudioText Messages BlogsWeb-panels based MR Geo-spatial data * 1 Zetabyte = 1 Billion GB Source: IDC, Crisil Data Scientists (K) 300 450 2.5 4.0 Data Savvy Managers (million) 150 1.5 GAP Need for Data Savvy Talent

3 3 Strictly confidential and proprietary; Not for circulation What has not changed with BIG DATA How to price? When to discount? How much inventory to stock? Why are certain customers buying more and others buying less? Which channels are most effective? Is my marketing campaign working? How do I cross-sell to the same customer? Business Questions Available Resources And Capabilities? Data “savvy” team Awareness of availability or use of ideal data sources (including new ones such as social data) Organizational skills to crunch and draw actionable insights from data Budgets to procure, scrub, and analyze data Relationships with data savvy experts + Your Experience and “Intuition”: Data should provide insight, prove or disprove hypotheses and eventually help you make better and faster DECISIONS

4 4 Strictly confidential and proprietary; Not for circulation With increasing sources and types of data, the ability to source, organise, model and use it remains a challenge Owned Data Sales transactions Customer feedback Product usage tracking Financial data Earned Data Daily “visible” product feedback Daily “competitive” reports Digital/ Social Interactions Purchased Data News “clips” Syndicated surveys / studies Custom surveys Competitive information Ratings & recommendations Lack of analytical resources Lack of business context Manual processes don’t scale Proliferation of apps and data systems Falling short on execution The “Typical Organizational” Challenge Too many decisions tend to be driven by intuition alone…

5 5 Strictly confidential and proprietary; Not for circulation Bridging the gap between Intuition and Decision Analytics as a Subscription Service Decision Data Strategy INTUITION Hypothesis Bias toward quick action Risk? DECISION (Make a business call) Informed choice Constantly learn from feedback Adapt, refine, sharpen, and succeed Owned Internal databases Explicit/ Implicit knowledge base Owned Internal databases Explicit/ Implicit knowledge base Paid Purchased reports Industry Information Paid Purchased reports Industry Information Earned Interaction Social / Digital Collaboration with supplier/ customer Earned Interaction Social / Digital Collaboration with supplier/ customer Decision Data Lakes


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