Implementing a Data-centric Strategy & Roadmap Focus on what really matters

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1 Implementing a Data-centric Strategy & Roadmap Focus on what really matters Presented by Peter Aiken, Ph.D. and Lewis Broome

2 Lewis Broome CEO Data Blueprint 20+ years in data management Experienced leader driving global solutions for Fortune 100 companies Creatively disrupting the approach to data management Published in multiple industry periodicals Peter Aiken 30+ years DM experience 9 books/ many articles Experienced with 500+ data management practices Multi-year immersions: US DoD, Nokia, Deutsche Bank, Wells Fargo, & Commonwealth of VA 2

3 We believe... Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia] Today, data is the most powerful, yet underutilized and poorly managed organizational asset Data is your Sole Non-depleteable Non-degrading Durable Strategic Asset Data is the new oil! Data is the new (s)oil! Data is the new bacon! Data Assets Available for subsequent use Financial Assets Can be used up Nondepletable Nondegrading Our mission is to unlock business value by Strengthening your data management capabilities Providing tailored solutions, and Building lasting partnerships Real Estate Assets Can degrade over time Inventory Assets Can be used up Can degrade over time Durable Non-taxed Strategic Asset Co 2015 by 3

4 A popular quote from Bill Gates Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose. Bill Gates Copyright 2015 by Data Blueprint 4

5 That quote in context Application design and business are now irrevocably linked. According to Bill Gates, Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose. How you use information may be the one factor that determines its failure or success or runaway success Bill Gates The Sunday Times 1999 Copyright 2015 by Data Blueprint 5

6 Outline Data Strategy Overview Determining the Business Needs Target Measurement & Success Criteria Current State Analysis Developing the Strategic Data Imperatives Business Value Targets Data Management Capabilities Tactics/Vision Developing a Roadmap Q&A 6

7 "The significant problems we face cannot be solved at the same level of thinking we were at when we created them." - Albert Einstein Einstein Quote Copyright 2013 by Data Blueprint 7

8 Simon Sinek: How great leaders inspire action Why How What it s not what you do, it s why you do it Co 2015 by 8

9 Simon Sinek: How great leaders inspire action it s not what you do, it s why you do it WHY HOW WHAT 9

10 What is a Strategy? Current use derived from military "a pattern in a stream of decisions" [Henry Mintzberg] "a system of finding, formulating, and developing a doctrine that will ensure long-term success if followed faithfully [Vladimir Kvint] 10

11 Strategy in Action: Napoleon defeats a larger enemy Question? How to I defeat the competition when their forces are bigger than mine? Answer: Divide and conquer! a pattern in a stream of decisions Copyright 2015 by Data Blueprint 11

12 Strategy in Action: Napoleon defeats a larger enemy 12

13 Wayne Gretzky s Definition of Strategy He skates to where he thinks the puck will be... 13

14 The Importance of Strategy: Data Strategy in Context Organizational IT Strategy Data Strategy Copyright 2013 by Data Blueprint 14

15 Organizational Strategy is Difficult to Perceive at the IT Project Level 1 Organizational Strategy 1 Set of Organizational Goals/Objectives Division/Group/Project If they exist... A singular organizational strategy and set of goals/objectives... Are not perceived as such at the project level and... What does exist is confused, inaccurate, and incomplete IT projects do not well reflect organizational strategy Co 2015 by 15

16 Innovation Enterprise Data Strategy Choices Q3 Using data to create strategic opportunities Q4 Both (Cash Cow) Only 1 is 10 organizations has a board approved data strategy! Q1 Keeping the doors open (little or no proactive data management) Q2 Increasing organizational efficiencies/effectiveness Improve Operations Copyright 2013 by Data Blueprint

17 Why Data is Creating a Competitive Advantage Adds value to products & Services Enhances the customer experience Creates transparency & efficiencies High-quality data enables more with less Creatively disrupts how we work Volume & velocity exerting pressure on operating models & infrastructure it s not what you do, it s why you do it Simon Sinek 17

18 What to Expect from a Data Strategy WHY A data strategy is important to the Org. HOW It will impact the organization WHAT The future look like (Paint a picture) WHAT It take to make it happen Forces an understanding of the importance of data Creates a vision for the organization Identifies the strategic imperatives Defines the benefits and key measures Describes the data management improvements needed Outlines the approach and activities Estimates the level of effort and investment 18

19 Data Strategy Framework Business Needs Organization Mission Strategy & Objectives Organizational Structures Performance Measures Current State Organizational / Readiness Business Processes Data Management Practices Data Assets Technology Assets Business Needs Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Existing Capabilities Business Value Execution Road Map Leadership & Planning Project Dev. & Execution Cultural Readiness New Capabilities 19

20 Common Problem Me either! No, I don t see any problem with the data 11

21 Outline Data Strategy Overview Determining the Business Needs Target Measurement & Success Criteria Current State Analysis Developing the Strategic Data Imperatives Business Value Targets Data Management Capabilities Tactics/Vision Developing a Roadmap Q&A 21

22 Analyzing the Business Why a Company Exists What a Company Produces & Sells How a Company Does It Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Mission & Brand Market Positioning Competitive Advantage Operating Model Business Goals & Objectives Business Needs 22

23 Mission & Brand Promises A mission statement is a statement of the purpose of a company; its reason for existing; a written declaration of an organization's core purpose and focus that normally remains unchanged over time. (Wikipedia: Mission Brand Architecture A Brand Promise is what you promise people will Underlying Values and Culture receive when they do business with you. It is based on what truly differentiates your company Brand Clues from others. It must convey a compelling benefit It must be authentic & credible It must be kept, every time 23

24 Brand Promises - Quick Examples FedEx - Your package will get there overnight. Guaranteed. Apple - You can own the coolest, easiest-to-use cutting-edge computers and electronics McKinsey & Company - You can hire the best minds in management consulting The Nature Conservancy - Empowering you to save the wilderness Data Blueprint Tailored Solutions, Strengthening Capabilities and Lasting Relationships 24

25 Porter s Market Positioning Framework Product Differentiation: How specifically focused are your products? Cost: Are you competing on cost? How costsensitive is your market? Market Scope: Are you focused on a narrow market (i.e. niche) or a broad market of customers? Broad Range of Buyers Narrow Buyer Segment Lower Cost Overall Low-Cost Leadership Strategy Focused Low-Cost Strategy Blue Ocean Brands Differentiation Broad Differentiation Strategy Focused Differentiation Strategy Note: (Typically) Can t be all things to all consumers where are you? 25

26 Market Positioning Example Lower Cost Differentiation Broad Range of Buyers Narrow Buyer Segment Overall Low-Cost Leadership Strategy Focused Low-Cost Strategy Blue Ocean Brands Broad Differentiation Strategy Focused Differentiation Strategy 26

27 Porter s Competitive Advantage Framework Given Market Positioning, how does your organization further compete? Bargaining Power of Buyers: The degree of leverage customers have over your company Bargaining Power of Suppliers: The degree of leverage suppliers have over your company Threat of New Entrants: How hard is it for new competition to enter the market? Threat of Substitute Products: How easy (or hard) is it for customers to switch to alternative products? Competitive Rivalry: How competitive is the market place? 27

28 Operating Model Framework Business Process Integration High Low Coordination Shared customers, products or suppliers Impact on other business unit transaction Operationally unique business units or functions Autonomous business management Business unit control over process design Consensus processes for designing IT infrastructure services IT application decisions made in business units Diversification Few, if any, shared customers or suppliers Independent transactions Operationally unique business units Autonomous business management Business unit control over business process design Few data standards across business units Most IT decisions made within business units Unification Customers and suppliers may be local or global Globally integrated business processes often with support of enterprise systems BU s with similar or overlapping operations Centralized management often applying functional/ process/business unit matrices Centrally mandated databases IT decisions made centrally Replication Few, if any, shared customers Independent transactions aggregated at high level Operationally similar business units Autonomous BU leaders with limited discretion over processes Centralized control over business process design Standardized data definitions but locally owned Centrally mandated IT services Low Business Process Standardization High *Source: Gartner 28

29 Business Goals & Objectives Definitions vary, overlap and fail to achieve clarity The most common of these concepts are specific of intended future results Most models refer to as either goals or objectives (sometimes interchangeably) 29

30 Business Goals Quick Examples 30

31 Case Study: Logistic Company Fortune Divisions Truck Load (OTR) Intermodal Outsourcing Service Broker Services Significant Growth over the last 10 years Enterprise-wide modernization program Recognized need to be data-driven to compete 31

32 Case Study: Mission & Brand Promises Reach $10 Billion in revenue by the year 2020 Mission: We compete with other transportation service companies primarily in terms of price, on-time pickup and delivery service, availability and type of equipment capacity, and availability of carriers for logistics services. Brand Promises 32

33 Case Study: Market Positioning Lower Cost Differentiation Brokered Services Intermodal Outsourced Services Truck Load Broad Range of Buyers Narrow Buyer Segment Low Cost; Quality Service; Availability and Differentiated Equipment & Service Overall Market Positioning Blue Ocean Brand able to compete across multiple market positions 33

34 Case Study: Competitive Advantage Buyer Power is moderate to weak 4 divisions at multiple price points ( Full Service ) High switching costs for some customers Threat of Entrant is weak High capital requirements Strong brand recognition Supplier Power is moderate to strong Limited # of drivers; Very Poor Retention Rates Limited railroad capacity (Intermodal) Threat of Substitutes is weak Railroads are a strong substitute; they lead in Intermodal 34

35 Case Study: Operating Model Business Process Integration Low High Low Business Process Standardization High *Source: Gartner 35

36 Case Study: Business Goals & Objectives Operate at a 98% on-time delivery service level Provide customers with real-time control and transparency over their orders Increase # of proactive order failures resolved by 20% GOALS #1 in on-time delivery Operational excellence thru a culture of entrepreneur & proactive behaviors Reduce cost per order execution by 12% Reduce # of accidents by 7% Increase driver retention rates by 20% Customer Experience Costs & Margin Logistics Business Agility Revenue Increase service offerings utilized per customer by 12% Reduce customer detected dispatch error rates by 14% Optimize asset capacity across business units by GOALS Capacity and flexibility to meet all our clients demands Increase revenue & margin thru integrated business units Increase asset utilization by 15% Increase order execution rates by 10% Increase Intermodal Revenue by 15% 36

37 Case Study: KPI s % of on-time pick-up and delivery % of customer reported service failures Time to respond to customer ad hoc queries # of customer order status inquires # of customer self-monitored orders GOALS #1 in on-time delivery Operational excellence thru a culture of entrepreneur & proactive behaviors # of Orders booked # of Auto-accept orders # of Load assignments # of Auto-assigns # of no-loads and am-loads # of Drivers per dispatcher # of Auto-dispatches Customer Experience Costs & Margin Logistics Business Agility Revenue # of errors for trailer pool data # of ETA errors for dispatched truck # of errors for train ETA data Time to view complete order history # of cross-selling orders GOALS Capacity and flexibility to meet all our clients demands Increase revenue & margin thru integrated business units # of solicitation calls and success rates # of turndowns % of allocation compliance Revenue per truck per day % of Driver utilization % of equipment utilization 37

38 Case Study: Example Findings to Create the Strategic Data imperatives Resolve operational bottlenecks that increase order execution costs or impedes on-time delivery Enable integrated workflows to optimize capacity and availability of assets across divisions to increase business agility and revenues from cross-selling Create real-time transparency and controls throughout the lifecycle of an order to enable customer self-service and proactive operational excellence Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Business Needs 38

39 Summary: Business Needs Analyzing and identifying the business needs is critically important for creating the strategic data imperatives Clarity about what is most important to the organizational mission, strategy and goals for the data strategy to address Forces a strategic and functional view of the data (and not a technical view) Provides the business language (and measures) needed to communicate the impact of the data strategy to leadership A Data Strategy must create real, measureable value to be adopted and sustained 39

40 Outline Data Strategy Overview Determining the Business Needs Target Measurement & Success Criteria Current State Analysis Developing the Strategic Data Imperatives Business Value Targets Data Management Capabilities Tactics/Vision Developing a Roadmap Q&A 40

41 Measuring Business Value Define success criteria as specific metrics Not always intuitive and at first seems difficult Must be done in collaboration with your business partners If something is important to the business it can be observed. If it can be observed, it is measureable! Understanding measurement ; reducing uncertainty, not necessarily an exact value Object of Measurement; often too ambiguously defined Methods of Measurement; become familiar with multiple methods and apply in the right context 41

42 Great point of initial inspiration... Formalizing stuff forces clarity Special shout out to Chapter 7 Measuring the value of information ISBN: How-Measure-Anything- Intangibles-Business 42

43 The Correct Concept of Measurement As far as the propositions of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality Albert Einstein Measurement: A quantitatively expressed reduction of uncertainty based on one or more observations Not elimination of uncertainty This means: Measurements do not need to be precise Measurement is information [information theory] 43

44 Defining the Object of Measurement A problem well stated is a problem half solved Charles Kittering What do you mean exactly (mentorship)? Clarification Chain 1. If it matters at all, it is detectable/observable 2. If it is detectable, it can be detected as an amount (or range of possible amounts) 3. If it can be detected as a range of possible amounts, it can be measured For example: Measure the value of crime reduction Build me a business case for a specific biometric identification systems for criminals 44

45 Methods of Measurement Very small random samples Useful in the face of great uncertainty Populations you will never see all of: Number of attempts that go undetected Risk of rare events Decision makers can be informed through observation and reason Subjective preferences and values The value of art, free time, risk reduction 45

46 Enrico Fermi (Nobel Prize Physics 1938) How many piano tuners in the city of Chicago? Count them all (yellow pages, licensing agency) Current population of Chicago (3 million at the time) Average number of people per household (2 or 3) Share of households with regularly tuned pianos (1 in 3) Required frequency of tuning (1/year) How many pianos can a tuner tune daily? (4 or 5) How many days/year are worked (250) Tuners in Chicago = Population/people per household X % households with tuned pianos X tunings per year/ (tunings per tuner per day X workdays/year) 46

47 Example: Measuring Business Value-1 $1billion (+) chemical company Develops/manufactures additives enhancing the performance of oils and fuels to enhance engine/ machine performance Helps fuels burn cleaner Engines run smoother Machines last longer Tens of thousands of tests annually Test costs range up to $250,000! Copyright 2013 by Data Blueprint 47

48 Example: Objects of Measurement & Metrics-2 Test Execution: Number of tests per customer product formulation. Grouped by product types and product complexity. Customer Satisfaction: Amount of time to develop a certified custom formulated product; time from initial request to certification Researcher Productivity: Tested and certified formulations per researcher Note: Baseline measures were taken from historical data and anecdotal information 48

49 Overview of Existing Data Management Process 1.Manual transfer of digital data 2.Manual file movement/duplication 3.Manual data manipulation 4.Disparate synonym reconciliation 5.Tribal knowledge requirements 6.Non-sustainable technology 49 Copyright 2013 by Data Blueprint 33

50 Solution and Business Value Results Solution: Business process improvements Data Architecture Development Data Quality Improvements Integrated System Development Results: Reduced the number of tests needed to develop products Increase the number of tests per researcher Reduce the time to market for new product development According to our client s internal business case development, they expect to realize a $25 million gain each year thanks to this data integration 50

51 Summary Measuring Business Value If it s important to the business, it s measureable Learning to measure business value requires: Understanding fundamentally what it means to measure Being clear about what is going to be the object of measurement and the specific metrics Methods that will ensure the metrics captured are meaningful and consistent The old adage if you don t measure it, it can t be managed is true Next Step: Develop a holistic solution and approach to address the business needs identified in the data strategy 51

52 Outline Data Strategy Overview Determining the Business Needs Target Measurement & Success Criteria Current State Analysis Developing the Strategic Data Imperatives Business Value Targets Data Management Capabilities Tactics/Vision Developing a Roadmap Q&A 52

53 Analyzing the Current State Business Needs Organization Mission Strategy & Objectives Organizational Structures Performance Measures Current State Organizational / Readiness Business Processes Data Management Practices Data Assets Technology Assets Business Needs Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Existing Capabilities Business Value Execution Road Map Leadership & Planning Project Dev. & Execution Cultural Readiness New Capabilities 53

54 Analyzing the Current State Why we are analyzing the current state Identify existing assets & capabilities Identify gaps in assets & capabilities Identify constraints & interdependencies Measure Cultural Readiness Measure what is achievable 54

55 Analyzing the Current State (ACS)-2 People & Organization Enables Business Processes Delivers Business Goals and Objectives Provides Context Enables Informs Creates Enables Enables Enables Data Assets Data Mgmt. Practices Technology Assets Measures 55

56 Current State: Organization Organizational Structures Understand roles, responsibilities, authority & accountability Reporting Structures Governance Structures Matrix (e.g. Project) Structures Assess Skills Across Business, Data & Technology Foundational Data skills (CDMP) Subject matter expertise (SME) Technology skills Business process skills (Six Sigma) Change management skills 56

57 Current State: Cultural Readiness Culture is the biggest impediment to a shift in organizational thinking about data 57

58 Current State: Business Process What we are looking for Process flow diagrams Process actors, including data creators & consumers Pain points Existing performance measures Why we want to look at business processes Where business value is realized Most important events in the life of data (Dr. Tom Redman) Describes the activities underpinning the competitive advantage 58

59 Current State: Business Process A CRUD Matrix captures current state processes and their impact on data. Specifically, data creation and consumption. How well this process is known & managed tells everything rqmts rqmts Data Supplier input feedback Data Creation Support Tech output feedback Data Customer 59

60 Current State: Data Management Practices Why we want to look at Data Management Practices Where the data management practices are deficient, surely the data will be as well Published by DAMA International The professional association for Data Managers (40 chapters worldwide) DM BoK organized around Primary data management functions focused around data delivery to the organization 47

61 Typical Thinking: Application-Centric In support of strategy, organizations develop specific goals/objectives The goals/objectives drive the development of specific systems/applications Development of systems/applications leads to network/ infrastructure requirements Data/information are typically considered after the systems/applications and network/infrastructure have been articulated Strategy Goals/Objectives Systems/Applications Problems with this approach: Ensures data is formed to the applications and not around the organizational-wide information requirements Network/Infrastructure Process are narrowly formed around applications Very little data reuse is possible Data/Information 61

62 New Thinking: Data-Centric In support of strategy, the organization develops specific goals/objectives The goals/objectives drive the development of specific data/information assets with an eye to organization-wide usage Network/infrastructure components are developed to support organization-wide use of data Development of systems/applications is derived from the data/network architecture Advantages of this approach: Data/information assets are developed from an organization-wide perspective Systems support organizational data needs and compliment organizational process flows Maximum data/information reuse Strategy Goals/Objectives Data/Information Network/Infrastructure Systems/Applications 62

63 Top Data Job Top Job The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your Most Valuable Asset Peter Aiken and Michael Gorman Top IT Job Top Operations Job Top Data Job Top Finance Job Top Marketing Job Data Governance Organization Dedicated solely to data asset leveraging Unconstrained by an IT project mindset Reporting to the business There is enough work to justify the function and not much talent The CDO provides significant input to the Top Information Technology Job 25 Percent of Large Global Organizations Will Have Appointed Chief Data Officers By 2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, id/ ? By 2020, 60% of CIOs in global organizations will be supplanted by the Chief Digital Officer (CDO) for the delivery of IT-enabled products and digital services (IDC) 63 Copyright 2015 by Data Blueprint

64 ACS DM Practice Areas Manage data coherently Maintain fit-for-purpose data, efficiently and effectively Manage data assets professionally Data architecture implementation Data lifecycle implementation Organizational support Co 2015 by 64

65 <- CMM Level Copyright 2013 by Data Blueprint 3

66 Assessment Components Data Management Practice Areas Data program coordination Organizational data integration DM is practiced as a coherent and coordinated set of activities Delivery of data is support of organizational objectives the currency of DM Capability Maturity Model Levels 1 Initial 2 - Repeatable Examples of practice maturity Our DM practices are ad hoc and dependent upon "heroes" and heroic efforts We have DM experience and have the ability to implement disciplined processes Data stewardship Designating specific individuals caretakers for certain data 3 - Documented We have standardized DM practices so that all in the organization can perform it with uniform quality Data development Efficient delivery of data via appropriate channels 4 - Managed We manage our DM processes so that the whole organization can follow our standard DM guidance Data support Ensuring reliable access to data 5 - Optimizing We have a process for improving our DM capabilities Copyright 2013 by Data Blueprint 4

67 Maslow's Hierarchiy of Needs Co 2015 by 67

68 Data Management Practices Hierarchy You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: Take longer Cost more Deliver less Present greater risk (with thanks to Tom DeMarco) Advanced Data Practices MDM Mining Big Data Analytics Warehousing SOA Foundational Data Management Practices Data Governance Technologies Data Quality Capabilities Data Management Strategy Data Platform/Architecture Data Operations Co 2015 by 68

69 Avoid One Legged Stools over relying on technology Governance is the major means of preventing over reliance on one legged stools! Copyright 2013 by Data Blueprint 69

70 Current State: Data Assets What we are looking for. Inventory of assets Shadow data solutions Organization of data assets (Architecture) Specific pain points Information capabilities (through a business lens) Methods for data integration Controls for data sharing 70

71 Current State: Data Assets Eating the data inventory Elephant Id what s important De-prioritize the Data ROT (Redundant, Obsolete, Trivial) Organize thinking into data roles Event Data Sensor Data Messaging Transactional Master Data Data Inventory Reporting Unstructured Metadata Temporal 71

72 Business Entity Inventory Example Transactional Data Front Office Transactional Business Entities Customer Request Order Order Plan Capacity Provides a broad view of the data assets Load Load Plan Warehouse Inv. Claim Dispatch Invoice Metadata Back Office Transactional Business Entities Credit Equip. Maint. GL Payroll 72

73 Current State: Data Asset Example 73

74 ACS: Data Assets Data Quality Considerations Prevention at Source Find and Fix Ad-Hoc Processes An interpretation from Dr. Tom Redman s Three Approaches to Data Quality 74

75 Current State: Technology Assets & Practices Technology Assets Systems & System Flows (Architecture) Shadow Systems Technologies, Platforms, Language Standards What s Legacy, what s permanent temporary, what s new Traceability to data and business processes Technology Management Practices System Development Lifecycle Governance & Production Support Practices Project and Program Management Practices 75

76 Current State: System Flow Example A view of systems mapped to functions and data 76

77 Outline Data Strategy Overview Determining the Business Needs Target Measurement & Success Criteria Current State Analysis Developing the Strategic Data Imperatives Business Value Targets Data Management Capabilities Tactics/Vision Developing a Roadmap Q&A 77

78 Strategic Data Imperatives Business Needs Organization Mission Strategy & Objectives Organizational Structures Performance Measures Current State Organizational / Readiness Business Processes Data Management Practices Data Assets Technology Assets Business Needs Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Existing Capabilities Business Value Execution Road Map Leadership & Planning Project Dev. & Execution Cultural Readiness New Capabilities 78

79 Strategic Data Imperative Framework Business Needs Current State STRATEGIC DATA IMPERATIVES Data Value Imperatives Id Business Value Opportunities Define Value Targets for Each DM Imperatives Net-Net DM Needs Define Capability Targets for Each Data Mgmt. Needs Data Mgmt. Practices Organizational & Leadership Data Assets Tactics Data Mgmt. Program Requirements Roadmap Project Requirements 79

80 Finding Data Value Opportunities Transparency Inefficiencies Checking & fixing Finding & Accessing Sharing & Controlling Proactive Workflows & Decision Making Measuring Outcomes & Performance Optimizing Asset Utilization Predictive and what-if Planning 80

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