Implementing a Data-centric Strategy & Roadmap

Transcription

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

2 Copyright 2015 by Data Blueprint 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 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

3 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 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

4 8 "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 7 Copyright 2013 by Data Blueprint Simon Sinek: How great leaders inspire action Why How What it s not what you do, it s why you do it Copyright 2015 by Data Blueprint

5 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 9 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

6 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 Wayne Gretzky s Definition of Strategy He skates to where he thinks the puck will be... 12

7 Copyright 2013 by Data Blueprint 14 The Importance of Strategy: Data Strategy in Context Organizational IT Strategy Data Strategy 13 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 Copyright 2015 by Data Blueprint

8 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 15 Copyright 2015 by Data Blueprint 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 data's importance Creates a vision for the organization Identifies the strategic imperatives Defines the benefits and key measures Describes needed data management improvements Outlines the approach and activities Estimates the level of effort and investment Copyright 2015 by Data Blueprint 16

9 Organization Mission Strategy & Objectives Organizational Structures Performance Measures Data Strategy Framework Business Needs Current State Organizational / Readiness Business Processes Data Management Practices Data Assets Technology Assets Business Needs Strategic Solution Data Imperatives Business Benefits Value & Success Targets Criteria Capability Targets Tactics Solution Architecture Data Organizational Strategy Vision Development Existing Capabilities Business Value Execution Road Map Leadership & Planning Project Dev. & Execution Cultural Readiness New Capabilities Copyright 2015 by Data Blueprint 17 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 18

10 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 19 Common Problem Me either! No, I don t see any problem with the data 20

11 Analyzing the Business Why a Company Exists Mission & Brand What a Company Produces & Sells Market Positioning Competitive Advantage How a Company Does It Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Operating Model Business Goals & Objectives Business Needs 21 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 from others. Brand Clues It must convey a compelling benefit It must be authentic & credible It must be kept, every time 22

12 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 23 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? 24

13 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 25 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? 26

14 Case Study: Operating Model Business Process Integration Low High Low Business Process Standardization High *Source: Gartner 27 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) 28

15 Business Goals Quick Examples 29 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 30

16 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 31 Case Study: Market Positioning Lower Cost Differentiation Broad Range of Buyers Narrow Buyer Segment Brokered Services Intermodal Outsourced Services Low Cost; Quality Service; Availability and Differentiated Equipment & Service Overall Market Positioning Blue Ocean Brand able to compete across multiple market positions Truck Load 32

17 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 33 Operating Model Framework Business Process Integration Low High Low Business Process Standardization High *Source: Gartner 34

18 Case Study: Business Goals & Objectives 35 Case Study: KPI s 36

19 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 37 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 38

20 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 39 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] 40

21 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 41 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 42

22 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) 43 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! 44 Copyright 2013 by Data Blueprint

23 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 45 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 Copyright 2013 by Data Blueprint

24 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 47 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 48

25 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 49 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 50

26 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 51 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 52

27 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 53 Current State: Cultural Readiness Culture is the biggest impediment to a shift in organizational thinking about data The Managing Complex Change model was copyrighted by Dr. Mary Lippitt,

28 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 55 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 56

29 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 Typical Thinking: Application-Centric Development 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 Problems with this approach: Ensures data is formed to the applications and not around the organizational-wide information requirements Process are narrowly formed around applications Very little data reuse is possible Strategy Goals/ Objectives Systems/ Applications Network/ Infrastructure Data/ Information Original articulation from Doug Walmart Copyright 2015 by Data Blueprint 58

30 Copyright 2015 by Data Blueprint New Thinking: Data-Centric Development 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 Original articulation from Doug Walmart Copyright 2015 by Data Blueprint 59 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, newsroom/ 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) 60

31 61 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 Copy 2015by Data right Blueprint DMM Capability Maturity Model Levels DM is strategic organizational capability, most importantly we have a process for improving our DM capabilities Optimized (5) We manage our data as a asset using advantageous data governance practices/structures Our DM efforts remain aligned with business strategy using standardized and consistently implemented practices Defined (3) Measured (4) One concept for process improvement, others include: Norton Stage Theory TQM TQdM TDQM ISO 9000 and focus on understanding current processes and determining where to make improvements. Managed (2) Our DM practices are defined and documented processes performed at the business unit level Performed (1) Our DM practices are informal and ad hoc, dependent upon "heroes" and heroic efforts Copyright 2013 by Data Blueprint 62

32 Copyright 2013 by Data Blueprint 64 Assessment Components Data Management Practice Areas DM is practiced as a Data Management coherent and Strategy coordinated set of activities Data Quality Data Governance Data Platform/ Architecture Data Operations Delivery of data is support of organizational objectives the currency of DM Designating specific individuals caretakers for certain data Efficient delivery of data via appropriate channels Ensuring reliable access to data Capability Maturity Model Levels 1 Performed 2 Managed 3 Defined 4 Measured 5 Optimized 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 We have standardized DM practices so that all in the organization can perform it with uniform quality We manage our DM processes so that the whole organization can follow our standard DM guidance We have a process for improving our DM capabilities 63 Maslow's Hierarchiy of Needs Copy 2015by Data right Blueprint

33 65 Data Management Practices Hierarchy You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Advanced Management Practices however this Data will: Practices MDM Take longer Mining Big Data Cost more Analytics Warehousing Deliver less SOA Present greater risk (with thanks to Tom DeMarco) Foundational Data Management Practices Data Governance Technologies Data Quality Capabilities Data Management Strategy Data Platform/Architecture Data Operations Copy 2015by Data right Blueprint 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 66

34 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 67 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 68

35 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 69 Current State: Data Asset Example 70

36 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 71 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 72

37 Current State: System Flow Example A view of systems mapped to functions and data 73 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 74

38 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 75 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 76

39 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 77 Define Data Value Imperatives & Targets Transparency Transparency and control across the lifecycle of an order Amount of time to find and access the complete history of an order Difference between the amount of time being reactive vs. proactive in a crisis Efficiency Maximize straight-through-processing from order capture thru dispatch # of order processed per account rep # of auto-dispatched loads Optimized Asset Utilization Optimize equipment capacity across divisions Revenue per truck per day # of errors for truck dispatched ETA data Proactive Workflow Improve customer experience KEY IMPERATIVE VALUE TARGET % of on-time deliveries # of customer self-monitored orders 78

40 Data Management Capability Needs Function of Value Imperatives & Targets At the core Architecture, Quality & Leadership Dimensions of Foundational & Technical Capabilities Think about DM needs broadly follows current state assessment framework 79 Capability Needs: Data Management Practices Foundational Data Management Practices create infrastructure that enables long-term DM capabilities Technology Data Management Practices deliver focused solutions in direct support of tactics 80

41 Foundational Practice Capabilities Governance: Little g approach - where it matters the most. Data Strategy: Top-down approach. Cannot dabble, must commit! Data Architecture: Organizing data assets based on business needs, not systems or applications. Data Education: Changing organizational thinking about data. 81 Technical Practice Capabilities Data Quality: Focus on most important data Address root cause issues Data correct first time Data Integration: Support multiple data uses Requires a common language and semantic understanding Data Platforms: Engineering/architectural & holistic systems thinking Decouple functionality No one data platform can do it all Business Intelligence: Highly dependent on quality, metadata & integration Exploratory in nature Small failures and on-going learning Often exists in spread-marts and shadow IT solutions 82

42 A Practice not a Project Requires new organizational structures Changes in existing roles and responsibilities Continuous practice improvement Constant investment KPI s Enabled through technology Practice Project Data Mgmt. Capability Needs (1) Data Value Imperatives DM Imperatives Data Mgmt. Needs Tactics Transparency Transparency and control across the lifecycle of an order New Data Assets: Event Data to describe lifecycle of an order Enterprise Data Architecture: Defining & relating transactions and events Data Quality: Quality controlled Transaction ids to maintain linkage across functions Data Integration: Order semantically defined across functions Business Process Engineering: Redesign processes to leverage single view of an order Organizational Roles: Business ownership of event data Amount of time to find and access the complete history of an order Difference between the amount of time being reactive vs. proactive in a crisis 84

43 Data Mgmt. Capability Needs (2) Data Value Imperatives DM Imperatives Data Mgmt. Needs Tactics Efficiency Maximize straight-through-processing (STP) from order capture thru dispatch Master Data Mgmt.: Master data quality greatly reduces processing errors Data Governance: Data standards & metadata enables automated workflows Enterprise Data Architecture: Globally organized data only way to control data for STP Data Quality: Enforce data correct the first time at point of data entry Business Process Engineering: Design exception-based workflows Organizational Roles: Business ownership of exception workflows; Governance roles # of order processed per account rep # of auto-dispatched loads 85 Vision of the Future A Vision that enables efficiency, transparency, control, stability and integration across the enterprise. while also allowing the flexibility of each division to meet their own, specific requirements 86

44 Detailed Vision Efficiency Transparency Control Stability Integration Across the enterprise 87 Capability Imperative: People & Organization Leadership Establish clear and explicit leadership role for Data Mgmt. Given Authority, Responsibility and Accountability to meet demands Given budget to match demands Roles & Responsibilities Define new and enhance existing roles and responsibilities Establish support organization for Data Mgmt. Leadership Enhance existing roles across business, IT and Data teams to meet new demands Skills & Experience Acquire new and further develop existing skills Data training provided across business, IT & data teams Hire and/or rent talent KEY CAPABILITY IMPERATIVE CAPABILITY TARGET 88

45 Capability Imperative: Data Mgmt. Practices Data Quality Establish repeatable data quality processes that deal with the root cause issues Id most important data Define and standardize repeatable DQ process Train cross functional teams on process Set improvement targets and monitored progress Data Architecture Organize views of the data assets to convey meaning for multiple business and IT purposes Business level view provides awareness, participation & responsibility with business roles Conceptual and logical views enable business, data & IT teams to effectively communicate Data Security can only be effective with a controlled inventory of data assets An operating model for creating and maintaining data architecture KEY CAPABILITY IMPERATIVE CAPABILITY TARGET 89 Tactics Preparing for the Roadmap Increase Operational Efficiencies Create efficiencies in the order lifecycle will Lower cost per order by 15% Increase resource capacity by 20% As-Is As-Is Efficiency Challenges Complex & un-integrated processes Poor data quality requires constant manual intervention Lack of transparency and controls creates work-around s To-Be To-Be Efficiency Tactics Eliminate non-value added manual work-around s Develop straight-through-processing where possible Automate exception-based workflows Create transparency across the order lifecycle Develop repeatable data quality processes 90

46 Tactics Preparing for the Roadmap Improve the Customer Experience Improving customer experience will Maintain >98% on-time delivery services Increase revenue per customer by 7% As-Is As-Is Customer Experience Challenges Manual monitoring of orders needing attention Reactive to customer status inquiries Reactive to unexpected order booking issues To-Be To-Be Customer Experience Tactics Automate exception identification & resolution Predictively find on-time delivery issues Provide customers cross-division service options Provide customers real-time views of orders Develop master data mgmt. solution for customer data 91 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 92

47 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 93 Roadmap Framework Y1 Y2 Y3 Y4 Data Strategy Leadership Planning & Business Strategy Alignment Program Management Tie Projects to Outcome-Based Targets Business Case and Project Scope Project Management and Execution Measure Outcomes Create Leading Coalition Communicate the Vision Leverage Short-Term Wins Institutionalize Data-driven Behaviors Fit for Purpose 94

48 Leadership & Planning Y1 Y2 Y3 Y4 Data Strategy Leadership Planning & Business Strategy Alignment Program Management On-going and iterative activities Responsible for other two streams Data Strategy Execution accountability and leadership (CDO) Adjust strategic imperatives/tactics based on changing business needs Manage relationships with business leaders and data strategy program stakeholders Don t Over-engineer the Process 95 Leadership & Planning Y1 Y2 Y3 Y4 Establish Leadership Organization and Processes On-going Sponsor Engagement Clearly Defined Imperatives, Tactics & KPI s On-going Planning & Adjustments Budget Cycles Establish PMO Practices & Processes Manage Project Portfolio 96

49 Project Development & Execution Y1 Y2 Y3 Y4 Tie Projects to Outcome-Based Targets Business Case and Project Scope Project Management and Execution Measure Outcomes Where the rubber hits the road Incremental Business Value Strengthen Capabilities Iterative and Additive Beyond Technology Hand-in-Hand with Cultural Readiness 97 Roadmap Operating Model Leadership & Planning Execution Leadership Planning Imperative, Tactic & KPI Targets Budgets & Resources Recommended Projects Approved Recommended Projects Project Development & Execution Define Milestones Define Projects Program Mgmt. Project Status & Outcome Measures Project Oversight & Support Execute Projects 98

50 Project Development & Execution Project Development Initialize High-level Milestone Targets (value & capability) Define the Initial Set of Projects (6 to 18 months out) Process for Defining Projects (business case & scope) Project Execution Define the Project Lifecycle by Project Type Focus on Execution Measuring Outcomes 99 Project Development Initialize Long-term Milestones Tie to Strategic Imperatives & Tactics Initialize Projects to Execute Establish On-going Project Definition Process 100

51 Project Development: Initial Roadmap Y1 Y2 Y3 Y4 Imperatives, Tactics & KPI s (Value targets) Strengthen Data Mgmt. Capabilities (Cap. targets) Establish Project Definition Process Short-Term Wins Leverage Momentum & Strengthened Capabilities 101 Initializing Milestones: Logistics Example Y1 Y2 Y3 Y4 Streamline Order Capture Proactive Exception Mgmt. Lower Operational Costs per Order Increase Revenue per Customer Enterprise View of Customers Cross-Divisional Selling Proactive Exception Mgmt. 360º View of Orders Optimized Routing & Equipment Utilization Improve Service Quality Master Data Mgmt. First-Time Correct Policy Data Quality Business Entities Conceptual (Enterprise) Logical (by subject) Data Architecture KEY Value Targets Capability Targets Equipment Tagging GIS and Telemetry Data Data Analytics 102

52 Initialize Projects Y1 Y2 Y3 Y4 Establish Project Definition Process Start Here! Short-Term Wins Repeatable Process for Defining Projects (Initial & On-going) Leverage Momentum & Strengthened Capabilities Project Definition Process Inputs Milestone Targets (Value and Capability) Cultural Readiness Goals Existing Capabilities (People, Process, Data, Technology and Readiness for Change) Outcomes from Previous Projects 103 Project Definition Process Capability Targets Value Targets Use to define initial road map projects Use iteratively for on-going project definition Leverage PMO and Program sponsorship Collaborate closely with Cultural Readiness teams Readiness Goals Existing Capabilities Project Outcomes Identify Candidate Projects Develop Business Case Measure Analyze Define & Sequence Projects Scope Resources Expected Value Recommend Projects Priority Justification Agreement Achievability 104

53 Achievability Business Impact Over-promise & under-deliver Cash Cow In the Tank Small Wins Level of Control (Influence) 105 The Approach of Crawl, Walk, Run Crawl: Identify business opportunity and determine a scope that fosters early learning yet delivers measureable value Walk: Develop foundational & technical data management practices ensuring they are repeatable. Enlarge the scope of projects that expand capabilities Run: Continuous improvement and expanded application of maturing data management practices 106

54 Initializing Projects: Logistics Example (1) Y1 Y2 Y3 Y4 Improve auto-accept rates [Data Quality, Reduce Cost per Order] Reduce cycle time to id errors [Data Quality, Reduce Cost per Order] Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue] Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order] Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer] Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order] Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] 107 Initializing Projects: Logistics Example (2) Y1 Y2 Y3 Y4 Improve auto-accept rates [Data Quality, Reduce Cost per Order] Reduce cycle time to id errors [Data Quality, Reduce Cost per Order] Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue] Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order] Short-term Wins Builds momentum for Data Strategy Re-engineer Customer Master Data [Data Quality, Architecture, CRAWL Reduces Reduce non-value Cost per added Order, work Increase Revenue per Customer] Creates repeatable data quality processes Coordinated Closely Re-engineer with Cultural Driver Master Readiness Data [Data Team Quality, Architecture, Reduce Cost per Order, Improve Service Quality] Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order] Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] 108

55 Initializing Projects: Logistics Example (3) Y1 Y2 Y3 Y4 Improve auto-accept rates [Data Quality, Reduce Cost per Order] Reduce cycle time to id errors [Data Quality, Improve STP] Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue] Increase # of Orders per Acct Rep [Data Quality, Architecture, Reduce Cost per Order] Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer] Extended Re-engineer Short-term Wins Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] WALK Order capture data shared to enhance cross divisional selling Automate order exception id & resolution Increase straight thru processing [Data Quality, Coordinated Closely Architecture, with Cultural Reduce Readiness Cost per Teams Order] Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] 109 Initializing Projects: Logistics Example (4) Y1 Y2 Y3 Y4 Improve auto-accept rates [Data Quality, Reduce Cost per Order] Reduce cycle time to id errors [Data Quality, Improve STP] Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue] Increase # of Drivers per Dispatcher [Data Quality, Architecture, Reduce Cost per Order] Re-engineer Customer Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer] Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] Foundational Data Management Increase straight Projects thru processing [Data Quality, Architecture, Reduce Cost per Order] Ties directly to multiple Value Imperatives JOG Addresses multiple data management foundational capabilities quality, architecture, master Automated data, Exception-based analytics,.. Workflows [Data Typically would fall under Quality, CDO Architecture, or Data Management Reduce Cost per Org. Order, Improve Service Quality] 110

56 Initializing Projects: Logistics Example (5) Y1 Y2 Y3 Y4 Improve auto-accept rates [Data Quality, Reduce Cost per Order] Reduce cycle time to id errors [Data Quality, Improve STP] Reduce # of order turndowns [Data Quality, Architecture, Increase Revenue] RUN Enterprise, transformational initiatives Ties Increase directly # to of multiple Drivers per Strategic Dispatcher Imperatives [Data Quality, Architecture, Reduce Cost per Order] Leverage foundational data mgmt. capabilities quality, architecture, master data, Re-engineer analytics, Customer.. Master Data [Data Quality, Architecture, Reduce Cost per Order, Increase Revenue per Customer] Typically would fall under CIO or Business Executive Re-engineer Driver Master Data [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] Increase straight thru processing [Data Quality, Architecture, Reduce Cost per Order] Automated Exception-based Workflows [Data Quality, Architecture, Reduce Cost per Order, Improve Service Quality] 111 Linking Projects to Milestones Y1 Y2 Y3 Y4 Streamline Order Capture Lower Operational Costs per Order First-Time Correct Policy Master Data Mgmt. Data Quality KEY Value Targets Capability Targets Business Entities Conceptual (Enterprise) Logical (by subject) Data Architecture Improve auto-accept rates Reduce cycle time to id errors Re-engineer Customer Master Data Increase straight thru processing 112

57 Summary: Project Development & Execution Projects must balance capability and business value creation Mix of projects: short-term wins, foundational data management projects, large enterprise initiatives Projects must directly-tie and measurably-support strategic imperatives and tactics Take a crawl, walk, run approach to project execution 113 Cultural Readiness Y1 Y2 Y3 Y4 Create Leading Coalition Establish Goals and Communication Plan Execute Communicate Plan Institutionalize Data-driven Behaviors Level of effort estimated 5% - 10% of total program in the first year Cultural change needs often neglected and under-estimated Leadership, skills and activities needed are typically missing Tie to strategic imperatives and projects; cannot be executed in a vacuum Data-driven organizations must recognize the need for transformation in attitudes, behaviors, processes, skills and organizational structures 114

58 Cultural Readiness Roadmap Y1 Y2 Y3 Y4 Create Leading Coalition Id Data Strategy Ambassadors Establish Cultural Readiness Goals & Communication Plan Execute Comm. Plan Vision Recognizing Wins Institutionalize Behaviors 115 Cultural Readiness In More Detail (1) "The thing I have learned at IBM is that culture is everything." - Louis V. Gerstner, Jr., Former CEO of IBM Leading Coalition that can make change happen Find the right people Create trust Common vision Establish Goals & Communication Plan Simplified Goals; Appeal to the Head and the Heart Communicate, Communicate, Communicate! 116

59 Cultural Readiness In More Detail (2) Execute Communication Plan Multiple Forums Repetition Leadership by Example Institutionalize Data-driven Behaviors Change comes last, not first Results Dependent May involve turnover Common Mistakes (1) 1. Buy-in but not Committing Responsibility, Accountability but NO Authority 2. Ready, Fire, Aim Starts without sufficiently defining the business needs 3. Trying to Solve World Hunger or Boil the Ocean Too big too fast = Recipe for disaster 4. The Goldilocks Syndrome Approach is at one extreme or another; too high-level or too in the weeds 5. Committee Overload Avoid too many chefs in the kitchen Source: Data Governance Worst Practices by Angela Guess; 3/10/

60 10 Common Mistakes (2) 6. Failure to Implement Communicate the vision 7. Not Dealing with Change Management Its mostly a people and culture issue 8. Assuming that Technology Alone is the Answer Shiny object syndrome 9. Not Building Sustainable and Ongoing Processes DG is not a project! 10. Ignoring Data Shadow Systems Missing the best part Source: Data Governance Worst Practices by Angela Guess; 3/10/ Conclusion In Summary. 120

61 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 121 Analyzing the Business Why a Company Exists Mission & Brand What a Company Produces & Sells Market Positioning Competitive Advantage How a Company Does It Strategic Data Imperatives Business Value Targets Capability Targets Tactics Data Strategy Vision Operating Model Business Goals & Objectives Business Needs 122

62 Data Strategy Solution Framework (DSSF) The solution architecture and change management plans result from this framework People & Organization Enables Business Processes Delivers Business Goals and Objectives Provides Context Creates Informs Enables Enables Enables Enables Data Assets Data Mgmt. Practices Technology Assets Measures 123 Strategic Data Imperative Framework Business Needs Current State 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 124

63 Roadmap Framework Y1 Y2 Y3 Y4 Program (Portfolio) Management Business Strategy Alignment Sponsorship Relations Management Tie Projects to Outcome-Based Targets Business Case and Project Scope Project Management and Execution Measure Outcomes Create Leading Coalition Establish Goals and Communication Plan Execute Communicate Plan Institutionalize Data-driven Behaviors Fit for Purpose 125 Sessions: Data Strategy 2.0: Focus on the Roadmap and Implementation 3 hour workshop with Lewis Broome Addressing Data Challenges using the Data Management Maturity Model Melanie A. Mecca, CMMI Institute Peter Aiken, Data Blueprint 120+ thought leaders 800 attending Senior IT Managers, Architects, Analysts, Architects & Business Executives 5 full days of in-depth education and networking opportunities and more!!! Register here:

64 Upcoming Events Enterprise Data World, Washington D.C. March 29 April 3, 2:00 PM ET/11:00 AM PT Data Governance Strategies April 14, 2:00 PM ET/11:00 AM PT 127 Questions? + = It s your turn! Use the chat feature or Twitter (#dataed) to submit your questions now. 128

65 10124 W. Broad Street, Suite C Glen Allen, Virginia