How Big Data Can be Used to Guide Innovation & Business Strategy

Transcription

1 How Big Data Can be Used to Guide Innovation & Business Strategy Presented by Al Adamsen Agile Performance LinkedIn: Al Adamsen Slide 1

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3 It ain't what you don't know that gets you into trouble... It's what you know for sure that just ain't so. - Mark Twain Slide 3

4 Analytics Maturity Progression Value/ Impact Analytics Hopeful Analytics Doer Analytics User Analytics Competitor Stage 1: Get Data Right Stage 2: Create Context Stage 3: Use Insight Stage 4: Operationalize Insight When Yes Do I have good data to work with? If not, what must I do to improve it? Improve it. Do I have the people in place to perform analytics? Do I have the necessary technologies? When Yes Can we aggregate and structure data in a usable way? Do we have people experienced in analytics? Do key metrics now have a story around them? Is there a growing demand for insight? When Yes Do we have an ondemand analytics platform? Are those doing analytics influencing change? Are insights being used in the field? Are dashboards w/ meaningful metrics (KPIs) being used? Insight is repeatedly used to formulate business strategy & allocate resources. Insight is repeatedly used by Managers in the field. Insight is obtained quickly & accurately from an on-demand platform. Time Slide 4

5 Big Data Machine Learning Artificial Intelligence Market Research Operations Research Data Visualization The Quantified Self Data Science Predictive Analytics Prescriptive Analytics Slide 5

6 The beginning of wisdom is to call things by their right names. - Chinese Proverb Slide 6

7 Big Data Defined "Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. Gartner Research, 2012 Insight A Volume BIG DATA Insight B The Story Velocity Insight C Variety Slide 7

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9 Research Agenda To-Do List Not To-Do List 1. Project A 2. Project B 3. Project C 1. Project D 2. Project E 3. Project F 4. Project G 5. Project H 6. Project I 7. Project J The governance body reviews potential projects, sets the research agenda, commissions projects, resources the projects, consumes the resulting insight, and makes decisions. Slide 9

10 Approaches to Analytics Deductive Reasoning e.g., Hypothesis Testing Inductive Reasoning e.g., Data Mining Slide 10

11 Optimize the Quantity & Quality of Information Value Competitive Advantage (just right) Optimal Quantity of Information Competitive Disadvantage (too little) Competitive Disadvantage (too much) Quantity of Information Slide 11

12 Value/Impact What Can Be Done Now? Predictive Analytics Correlation/ Regression Scenario Planning/ Simulation Optimization/ Machine Learning Reports Dashboards/ Scorecard Time/Effort Slide 12

13 Value/Impact What Can Be Done Now? Advanced Analytics What can be leveraged? Risk? What s the probability of success? Predictive Modeling/ROI Linkage Research Planning/Analytics Who do we need, when, & where? What is affecting staff and why? Forecasting, Scenario Planning Cluster Analysis, Data Mining Metrics & Reporting What is happening? Where is it happening? Scorecards, Recurring Reports Ad hoc Reports Time/Effort Slide 13

14 Value/Impact What Can Be Done Now? Time/Effort Slide 14

15 Aggregating & Analyzing Data Most organizations (nearly all) do not extract a level of value from their IT investments commensurate with the money, time, and effort devoted. Analytics & Advanced Analytics helps organizations maximize the value derived from data assets. In turn, the insight generated serves as a unique, hard-to-replicate competitive advantage as leaders are able to make more effective, less riskly decisions. Decisions are now based on what we know not what we think we know. Product or Market Insight context Metrics, Reports, & Dashboards Aggregating Finance Supply Chain Product- Merchandising Sales Customer (CRM) Market Research Competitor Info Economic Info Slide 15

16 Analytics Framework: The Mental Model The Enterprise Linkage Model TM Leadership Marketing & Sales Strategy Finance Strategy Prospects & Customers Financial Outcomes Leaders, Managers, & the Workforce Internal Operations Talent Strategy Operating Strategy Slide 16

17 The Enterprise Linkage Model TM Product Mgmt. Activities Customer Experience Balanced Scorecard Economic Environment Brand Equity Knowledgeable Salespeople Product Training Sales Increase Profitability Product Development Opportunity Market Research Needs/ Desires Purchase Intent Availability Satisfaction Purchase Sale-Ready Inventory Store Cleanliness Merchandising Production Planning Mindshare Product Placement Print Advertising Mobile Advertising Online Advertising TV Advertising Supply Chain Slide 17

18 Learning through Story Supply Chain Availability Customer Purchase Sales $8M 1M 900K units units in < 4 mos. $36M in < 4 mos. An initial $8M investment in Product A produced 1 million units. These units were available for purchase in-store and online beginning October The perceived availability of product among desired customer segments, emphasized by marketing efforts, elevated mindshare and purchase intent which, in turn, led to 200K units being sold in the first month, followed by 250K in the second, 300K in the third, 150K in the fourth, 125K in the fifth, and 145K in the sixth. Due to strong initial sales, an order of 1.2M units was initiated at the beginning of December, in time for the inventory to be replenished early in the new year. With an 80% degree of confidence, mindshare of product functionality and design (derived from marketing) and perceived product availability (derived from customer surveys) were the primary factors in driving actual purchase behavior. These factors proved to be collinear, thus potentially co-dependent. Slide 18

19 Example: A Product Lifecycle Sales R&D Introduction Growth Maturity Decline Socio-Economic Environment Marketing Design Sales Customer Behavior Supply Chain Competition Brand Equity Other? Inflection Point Profit Innovators Early Adopters Early Majority Late Majority Laggards Time Slide 19

20 Time Bayes Theorem What s the likelihood we ll sell 50,000 units of Product A this year? New Data/ Information The Prior Probability of an Event (%) The Likelihood of an Event Now given all available Data/Information (%) The Probability of a Related Event Apply Bayes Theorem Posterior Probability Slide 20

21 How to build the capability to leverage Big Data? Slide 21

22 Clarify what analytics is all about: Improving Performance Reducing Risk Innovating The Deliverable? Insight Slide 22

23 Analytics is a Process to Drive Change. How? Direct the Rider = Data + Context Motivate the Elephant = Visualization + Communication Shape the Path = Where we re going + How we going to get there Slide 23

24 The 10 Essentials to Generating Actionable Data-based Insight Over Time 1. Executive Involvement 2. Dedicated Professionals 3. Governance 4. Research Agenda 5. Analytical Diversity 6. Measurement Strategy 7. Systems Strategy 8. Analytical Tools 9. Ample Budget 10. Unwavering Commitment Slide 24

25 Supplier Partner Customer 1) Information & Insight Context & Communication Consumption & Decisions 2) 3) Confidence The feeling or belief that you can rely on someone or something to perform in a certain way at a known time in the future; firm trust Slide 25

26 What s going on? Why? And what to do? Decisions Actions Knowledge/ insight The Truth: What s really going on Knowledge/ Insight Slide 26

27 What s going on? Why? And what to do? Knowledge/ We Insight think The Truth: What s really going on Decisions Actions Slide 27

28 What if we, as leaders, possessed better clarity into the future and how best to respond? had the insight we needed when we needed it? Innovated product, service, & pricing strategies that optimized sustainable profitability? inspired the individual, team, and organizational change we desired? Slide 28

29 Strategic Foresight Process Futuring Strategic Planning Trends & Mega Trends Assess Impact Scenario Plan Business Vision Create Roadmap Measure & Manage* Financial Relationships Health/ Wellness Wealth/Budget Work/Volunteer Future One Business Plan Customers/ Market Internal Operations Employees/ Workforce Environment/ Community Recreation Future Two Decisions Vendors Environment etc. Future Three Communities * Stakeholder Relationship Management Slide 29

30 The Data-to-Change Process TM Do Actions Reflect Data-Driven Insight? Systems/ Data Data Staging/ Aggregation Analysis/ Reporting Analytics/ Insight Packaging/ Story Telling Communication/ Facilitation Action/ Positive Change Creating Knowledge Bridging the Gap between Knowledge and Action Action Slide 30

31 Governance Analytics: The Three Workstreams Strategic Who wants what and when? Understand & Make Decisions Operational How do those questions get answered? Analyze, Package & Communicate Insight Technical Stage Data & Access Analytical Tools Slide 31

32 The Data-to-Change Process TM Do Actions Reflect Data-Driven Insight? Systems/ Data Data Staging/ Aggregation Analysis/ Reporting Analytics/ Insight Packaging/ Story Telling Communication/ Facilitation Action/ Positive Change STRATEGIC OPERATIONAL TECHNOLOGICAL Governance Slide 32

33 What do we look like? What will we 2016 Product related decisions based on lagging indicators such as NPR and sales Perception of Product Dev s ability to predict future demand is suspect Not delivering product insights that guide decisions doing basic reporting Decision-making is based largely on experience and gut feel, not data-based insight Generating basic metrics most are lagging indicators Challenging to generate meaningful product insight Basic systems for planning, analytics, and reporting 2018 Have easily obtainable insight that informs strategic product &/or service decisions Leading indicators help guide product & service strategy and related marketing The systematic, recurring use of proven metrics helps prioritize and focus action Department heads anticipate and act on recurring metrics, insights, and scenarios Generate recurring reports that are eagerly anticipated Produce insight that s valued at all levels and functions Manage an analytics toolset that enables the above Slide 33

34 VUCA World Volatile Uncertain Complex Ambiguous Millennials Fairness Respect Significance Growth Slide 34

35 Roughly 75.4 Million living Boomers Homogonous: 28% minority More rural backgrounds Willing to tow the line Over 80 Million Millennials Diverse: 44.2% minority More urban experiences Willing to leave when unfair US Census Bureau, June, 2015 TSI Millennial Research, 2015 Slide 35

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37 Key Questions/Concerns: Where to put all that data? What to do with it once its there? Are insights noise or of value? How to package the valuable insight? How to facilitate change? Slide 37

38 A measurement strategy works in conjunction with an analytics strategy. Measurement Analytics Change Strategists Research Agenda, Process Improvements, System Decisions, Measurement Strategy Strategists ( ) Slide 38

39 Attributes of Analytical Organizations Learning Mindset Systematic Thinking Probabilistic Scenarios Expert Communicators/Facilitators Intake Express Slide 39

40 We are Explorers Creators Slide 40

41 Analytics Maturity Progression Value/ Impact Analytics Hopeful Analytics Doer Analytics User Analytics Competitor Stage 1: Get Data Right Stage 2: Create Context Stage 3: Use Insight Stage 4: Operationalize Insight Do I have good data to work with? If not, what must I do to improve it? Improve it. Do I have the people in place to perform analytics? Do I have the necessary technologies? LEARNING PRACTICE! Insight is repeatedly used to formulate business strategy & allocate resources. Insight is repeatedly used by Managers in the field. Insight is obtained quickly & accurately from an on-demand platform. Time Slide 41

42 How Big Data Can be Used to Guide Innovation & Business Strategy Thank You! Presented by Al Adamsen Agile Performance LinkedIn: Al Adamsen Slide 42