Data-Ed: Data-centric Strategy & Roadmap
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Data-Ed: Data-centric Strategy & Roadmap

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Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset ...

Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.

Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/

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Data-Ed: Data-centric Strategy & Roadmap Data-Ed: Data-centric Strategy & Roadmap Presentation Transcript

  • Data-centric Strategy & Roadmap Date: February 11, 2014 Time: 2:00 PM ET 11:00 AM PT Presenters: Peter Aiken, Lewis Broome 1 Copyright 2014 by Data Blueprint
  • Commonly Asked Questions 1)  Will I get copies of the slides after the event? 2)  Is this being recorded so I can view it afterwards? 2 Copyright 2014 by Data Blueprint
  • Get Social with Us! Live Twitter Feed @datablueprint @paiken #dataed Like Us www.facebook.com/datablueprint Join the Group Data Management & Business Intelligence 3 Copyright 2014 by Data Blueprint View slide
  • Building a Data-centric Strategy & Roadmap What needs to be done… avoiding a haphazard approach Presented by Peter Aiken, Ph.D. and Lewis Broome View slide
  • 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 5 Copyright 2014 by Data Blueprint
  • Building a Data-centric Strategy & Roadmap What needs to be done … avoiding a haphazard approach Presented by Peter Aiken, Ph.D. and Lewis Broome Copyright 2014 by Data Blueprint
  • Outline •  Data Strategy Overview •  Determining the Business Needs –  Foundational Business Understanding –  Identify Specific Business Needs –  An Example •  Measurement & Success Criteria –  An Overview –  An Example •  Developing a Solution to Address Needs –  Closing Foundational Gaps –  Solving for Specific Needs •  Developing a Roadmap and Plan •  Q&A 7 Copyright 2014 by Data Blueprint
  • Simon Sinek: How great leaders inspire action WHY HOW “…it’s not what you do, it’s why you do it” WHAT http://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action.html 8 Copyright 2014 by Data Blueprint
  • Summary: Enterprise Data Strategy Choices Q4 Using data to create strategic opportunities Innovation Q3 Both (Cash Cow) Only 1 in 10 organizations has a board approved data strategy! Q1 Q2 Keeping the doors open (little or no proactive data management) Increasing organizational efficiencies/effectiveness Improve Operations 9 Copyright 2014 by Data Blueprint
  • ‘Why’ a Data Strategy? Data becoming inextricably linked to, and part of, the actual products & services being sold Customers see enhanced value in having relevant, accurate & meaningful information combined with the products and services they purchase Information is power in a competitive market place Situational awareness (e.g. a 360º view) of your customers, suppliers, competition & operating environment creates a competitive advantage that enables you to plan and react Volume and velocity of data impacting operating models Organizations are being put at greater operating and reputational risk because legacy business processes and systems are straining under the requirements to process and understand everincreasing volumes and speed of data Read more at my blog: http://www.datablueprint.com/winning-todays-information-economy-data-centric-business-strategy/ 10 Copyright 2014 by Data Blueprint
  • Putting the Data Strategy Together Get on the same page with business partners Measure Business Value Develop a holistic solution and approach Get a true understanding of your organization’s competitive advantage and current business goals Working with business leaders, managers and operators, define specific opportunities to meet the organizational goals Collaborating with your business partners, define the metrics that measure levels of success Develop a comprehensive solution using people, process, data and technology Outline an achievable implementation plan in a roadmap with timelines, milestones and level of effort estimates Note: For many organizations this requires a transformation in how they think and operate – this is the greatest challenge in becoming a ‘data-driven’ organization 11 Copyright 2014 by Data Blueprint
  • Outline •  Data Strategy Overview •  Determining the Business Needs –  Foundational Business Understanding –  Identify Specific Business Needs –  An Example •  Measurement & Success Criteria –  An Overview –  An Example •  Developing a Solution to Address Needs –  Closing Foundational Gaps –  Solving for Specific Business Needs •  Developing a Roadmap and Plan •  Q&A 12 Copyright 2014 by Data Blueprint
  • Understanding Your Company’s Competitive Advantage •  Do you really know why your company has an advantage over the competition? –  You may be surprised! –  Its not about being the best, its about being different (counter intuitive) –  Its about deciding between a set of trade-offs –  Data strategy must align •  Frameworks for understanding competitive advantage –  –  –  –  –  Porter’s Five Forces Porter’s Competitive Strategic Matrix SWOT Analysis PEST Analysis Four Corners Analysis 13 Copyright 2014 by Data Blueprint
  • Porter’s Competitive Strategic Matrix Product Differentiation: How specifically focused are your products? Cost: Are you competing on cost? How cost-sensitive is your market? Market Scope: Are you focused on a narrow market (i.e. niche) or a broad market of customers? Lower Cost Differentiation Broad Broad Overall Low-Cost Leadership Differentiation Range of Strategy Strategy Buyers Blue Ocean Brands Narrow Buyer Segment Focused Low-Cost Strategy Focused Differentiation Strategy Note: (Typically) Can’t be all things to all consumers – where are you? 14 Copyright 2014 by Data Blueprint
  • Porter’s Competitive Strategic Matrix - Examples Lower Cost Differentiation Broad Range of Buyers Narrow Buyer Segment 15 Copyright 2014 by Data Blueprint
  • Porter’s Five Forces Once you find your place in the four quadrants…What is your competitive advantage? 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? http://www.strategy-keys.com/michael-porter-five-forces-model.html 16 Copyright 2014 by Data Blueprint
  • An Example – The Automotive Industry Once you find your place in the four quadrants…. •  What is your competitive advantage against those around you? Lower Cost Differentiation Broad Broad Overall Low-Cost Leadership Differentiation Range of Strategy Strategy Buyers Blue Ocean Brands Narrow Buyer Segment Focused Low-Cost Strategy Focused Differentiation Strategy 17 Copyright 2014 by Data Blueprint
  • Applying the Five Forces 5 Forces Porsche Hyundai Threat of New Entrants Very Weak Weak Bargaining Power of Buyers Moderate Very Strong Bargaining Power of Suppliers Weak Very Weak Threat of Substitutes Moderate Strong Competitive Rivalry Moderate Strong Porsche •  Customer relationship data is critical. Develop individualized customer interactions •  High quality & efficient data processing to support R&D to further differentiate products Hyundai •  Price-sensitive customers. Use strength over suppliers to maintain low COGS. •  Reduce non-value added to keep operational costs low by eliminating inefficiencies created by poor data quality 18 Copyright 2014 by Data Blueprint
  • Data Value Generation Take-Away Source: http://www.cioupdate.com/insights/article.php/3936706/The-4-Principles-of-a-Successful-Data-Strategy.htm 19 Copyright 2014 by Data Blueprint
  • Summary: Same Page with Your Business Partners A Data Strategy must be Business Focused •  Understand the business fundamentals of your organization •  Develop a common language and shared perspective with your business partners – enabling collaboration •  Identify specific business opportunities or areas of improvement •  Focus the data strategy solution on improving those specific business needs Next Step: •  Measuring business value of making improvements: •  Metrics, Object of Measurement and Methods 20 Copyright 2014 by Data Blueprint
  • One of two choices •  Good business strategy –  Understand what it really is: •  Organizational strategy •  IT strategy •  Data strategy •  Got to figure out/improve the business strategy –  Analysis –  What changes would be seen as useful/important? –  Plan to accomplishing something useful … 21 Copyright 2014 by Data Blueprint
  • Outline •  Data Strategy Overview •  Determining the Business Needs –  Foundational Business Understanding –  Identify Specific Business Needs –  An Example •  Measurement & Success Criteria –  An Overview –  An Example •  Developing a Solution to Address Needs –  Closing Foundational Gaps –  Solving for Specific Business Needs •  Developing a Roadmap and Plan •  Q&A 22 Copyright 2014 by Data Blueprint
  • 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 23 Copyright 2014 by Data Blueprint
  • Great point of initial inspiration ... •  Formalizing stuff forces clarity •  Special shout out to Chapter 7 –  Measuring the value of information –  ISBN: 0470539399 –  http://www.amazon.com/ How-Measure-AnythingIntangibles-Business 24 Copyright 2014 by Data Blueprint
  • Measuring Business Value – An Example International Chemical Company Engine Testing •  $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 ($25K to $250K each) 25 Copyright 2014 by Data Blueprint
  • Objects of Measurement & Metrics •  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 26 Copyright 2014 by Data Blueprint
  • 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 Overview of Existing Process 27 Copyright 2014 by Data Blueprint
  • 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 28 Copyright 2014 by Data Blueprint
  • 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 29 Copyright 2014 by Data Blueprint
  • Outline •  Data Strategy Overview •  Determining the Business Needs –  Foundational Business Understanding –  Identify Specific Business Needs –  An Example •  Measurement & Success Criteria –  An Overview –  An Example •  Developing a Solution to Address Needs –  Closing Foundational Gaps –  Solving for Specific Business Needs •  Developing a Roadmap and Plan •  Q&A 30 Copyright 2014 by Data Blueprint
  • The Data Strategy Solution With an understanding of business needs and measures of success criteria, align a solution leveraging the following: • Rethink the SDLC: Application- vs. Data-Centric • Make it Comprehensive: –  People: Organizational Structure –  Data Management Practices: Foundational & Technical –  Data: Determine What is Important –  Process: Business Process Changes –  Technology: Engineering/Architectural Concepts • Match your organization’s abilities to deliver 31 Copyright 2014 by Data Blueprint
  • 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 32 Copyright 2014 by Data Blueprint
  • 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 •  Development of systems/applications is derived from the data/network architecture •  Goals/Objectives Network/infrastructure components are developed to support organization-wide use of data •  Strategy Advantages of this approach: –  Data/information assets are developed from an organization-wide perspective Data/Information Network/Infrastructure –  Systems support organizational data needs and compliment organizational process flows –  Maximum data/information reuse Systems/Applications 33 Copyright 2014 by Data Blueprint
  • People: Who is Involved? •  Open question: Who is responsible for creating and implementing the company’s Data Strategy? -  Organizational Leadership is required – a Chief Officer that reports up through the business lines -  Data strategy requires governance – Business, IT and Data team representation •  Stakeholders -  CEO, CFO, COO, CIO, etc.. -  Lines of Business Senior Management and Operational Managers -  Functional Areas Senior Management and Team Leads •  The Data Team – formal and implicit -  Architects -  Modelers -  Developers -  Analysts -  Stewards -  CDO 34 Copyright 2014 by Data Blueprint
  • ce n te r/I T nS ec eff tio ur ity /P ri Vi rtu vacy ali ic i za en Im cie tion pr s/C ov ing lo S pe oc ia ud lM op le/ ed St lea ia an de da rsh rd iz a BI /an ip tio IT n/c aly wo on ti c rkf s so or lid ce ati de on ve lop me IT Mo nt go bil ve Ri ea rn sk Im an pp ma ple ce lic me na ati ge on n ti me ng s/t ec nt pla hn Inf ns olo or /in ma gie ita tio tiv s nS es /ac ha hie rin g Ac v in qu gr is i es Pr tio ult oc n /p s es ro s/s jec ys tm tem gt int eg St ra ra tio teg n ic pla nn ing Da ta fo r ma 1.  Dedicated solely to data asset leveraging 2.  Unconstrained by an IT project mindset 3.  Reporting to the business IT /In Top Job CDO Reporting Top Information Technology Job Top Operations Job Chief Data Officer Top Finance Job 0.000 Copyright 2014 by Data Blueprint Top Marketing Job Data Governance Organization 0.800 0.600 0.400 0.200 2011 2010 2009 2008 2007 2006 2005 35
  • Data: Determine What is Important •  Think about it in terms of data ‘meta-types’: –  Transactional Data –  Workflow/Event Data –  Master & Reference Data –  Reporting & Analytical Data –  Metadata •  Not all of your data is important! •  Concept of ROT •  Understanding your business and their needs makes this easier to determine 36 Copyright 2014 by Data Blueprint
  • Data Management Practices •  Foundational Data Management Practices create the organizational infrastructure that enforces the alignment of company strategies with data assets •  Technology Data Management Practices enable an organization to leverage the data on the scale needed to support informationbased strategies Important Note: Not all DM Practices needed all the time. Tailor to meet the needs of the business. 37 Copyright 2014 by Data Blueprint
  • Foundational Practices •  3-legged stool –  Strategy –  Architecture –  Governance •  For example: –  Warehouses fail –  Missing governance –  Quality 38 Copyright 2014 by Data Blueprint
  • Health Care Provider Data Warehouse The average DW costs $30M and take 18 months to build! •  •  •  •  1.8 million members 1.4 million providers 800,000 providers no key 1 User "I can take a roomful of MBAs and accomplish this analysis faster!" 39 Copyright 2014 by Data Blueprint
  • Foundational Practice: Data Strategy •  Your data strategy must align to your organizational business strategy and operating model •  As the market place becomes more data-driven, a data-focused business strategy is an imperative •  For example, you must have data strategy before you have a Big Data strategy 40 Copyright 2014 by Data Blueprint
  • Foundational Practice: Data Architecture •  Common vocabulary expressing integrated requirements ensuring that data assets are stored, arranged, managed, and used in systems in support of organizational strategy [Aiken 2010] •  Most organizations have data assets that are not supportive of strategies •  Big question: •  How can organizations more effectively use their information architectures to support strategy implementation? 41 Copyright 2014 by Data Blueprint
  • Foundational Practice: Data Governance •  Data governance is the exercise of authority and control over the management of your mission critical data assets. •  Governance can seem like an added bureaucratic layer with little value-add. The little ‘g’ approach develop governance where it matters the most. •  Focus on organizational roles and responsibilities as well as organizational change management initiatives. 42 Copyright 2014 by Data Blueprint
  • Technical Practices •  Think like an engineer –  Holistic –  Integrated –  Driven by Requirements •  For example: –  Unwinding Mainframes –  Analytical Platforms 43 Copyright 2014 by Data Blueprint
  • Technical Practices: Data Quality •  Quality is driven by fit for purpose considerations •  Improved directional accuracy is the goal •  Focus on your most important data assets and ensure our solutions address the root cause of any quality issues – so that your data is correct when it is first created •  Experience has shown that organizations can never get in front of their data quality issues if they only use the ‘find-and-fix’ approach 44 Copyright 2014 by Data Blueprint
  • Technical Practices: Data Integration •  Data integration requires a common language and semantic understanding •  Needs to support multiple perspectives on the same data •  Creates the broad, 360 degree view – where insight comes from •  An area where governance can enable and sustain •  A challenge in organizational thinking 45 Copyright 2014 by Data Blueprint
  • Technical Practices: Data Platforms •  Incorporate engineering/ architectural concepts into holistic systems thinking •  Decouple functionality. No one data platform can answer all questions (commonly misunderstood & expensive) •  Engineered components can only be as strong as their weakest component 46 Copyright 2014 by Data Blueprint
  • Getting Data into the Cloud Transform Less Cleaner More shareable ... data 47 Copyright 2014 by Data Blueprint
  • Technical Practices: Business Intelligence •  Highly dependent on quality, metadata, governance, integration and platforms •  Exploratory in nature. Small ‘failures’ and on-going learning are part of the process •  Often exists in spread-marts and shadow IT solutions – difficult to share and have a common understanding 48 Copyright 2014 by Data Blueprint
  • Process: Business Process Impacts •  The Data Strategy Solution will impact existing business processes and may create new business processes. •  Business processes are how the data get Created, Read, Updated and Deleted (CRUD) •  A CRUD matrix shows business processes and their data activity type •  Leverage business process analysis, design and development techniques •  Capture baseline measures against existing business processes to effectively measure improvements 49 Copyright 2014 by Data Blueprint
  • Technology: Making the Right Choices •  For example: Software selection •  When it is discovered that the new software doesn't match existing organizational practices … 1.  Change software 2.  Change your business practices 3.  Some combination of both 4.  Ignore the problem •  Data strategy would have revealed the problem in advance of the selection 50 Copyright 2014 by Data Blueprint
  • Match your Abilities to Deliver Understanding your level of Data Management Practice is critical in developing achievable solutions Data management processes and infrastructure Organizational Strategies Implementation Guidance Data Program Coordination Goals Organizational Data Integration Combining multiple assets to produce extra value Organizational-entity subject area data integration Integrated Models Achieve sharing of data within a business area Data Stewardship Standard Data Application Models & Designs Provide reliable data access Direction Data Support Operations Feedback Leverage data in organizational activities Data Development Business Data Data Asset Use Business Value 51 Copyright 2014 by Data Blueprint
  • 52 Copyright 2014 by Data Blueprint
  • 53 Copyright 2014 by Data Blueprint
  • Summary: The Data Strategy Solution •  Thinking differently about the solution •  Its Comprehensive: People, Data Management, Data, Process & Technology •  Address foundational gaps to sustain solutions •  Match your organization’s abilities to deliver Next Step: •  Outline an achievable implementation plan 54 Copyright 2014 by Data Blueprint
  • Outline •  Data Strategy Overview •  Determining the Business Needs –  Foundational Business Understanding –  Identify Specific Business Needs –  An Example •  Measurement & Success Criteria –  An Overview –  An Example •  Developing a Solution to Address Needs –  Closing Foundational Gaps –  Solving for Specific Business Needs •  Developing a Roadmap and Plan •  Q&A 55 Copyright 2014 by Data Blueprint
  • Implementation Plan & Roadmap •  Outline a long-term vision and implementation milestones •  Achievable, realistic plans •  Build momentum with specific, short-term win projects –  Approach: Crawl, Walk, Run •  More to come at EDW… 56 Copyright 2014 by Data Blueprint
  • 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 57 Copyright 2014 by Data Blueprint
  • The Benefits of Crawl, Walk, Run •  ‘Pilot-like’ projects create a unique opportunity for organizational learning while providing measureable value •  Builds support for new approaches to data management – i.e. supports change management activities •  More achievable approach to managing data as an asset •  Allows for foundational components to be developed while concurrently executing more tactical solutions 58 Copyright 2014 by Data Blueprint
  • Sessions: • Implementing a Data-Centric Strategy & Roadmap – Focus on What Really Matters –  3 hour workshop with Peter & Lewis • Choosing the Right Data Warehouse Modeling Strategy based on your business needs: Kimball, Inmon, Data Vault –  Lighting Talk with Data Blueprint Team •  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: www.edw2014.dataversity.net Copyright 2014 by Data Blueprint
  • Questions? + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions now. 60 Copyright 2014 by Data Blueprint
  • Upcoming Events Emerging Trends in Data Jobs March 13, 2014 @ 2:00 PM ET/11:00 AM PT Data Quality Engineering April 11, 2014 @ 2:00 PM ET/11:00 AM PT Sign up here: •  www.datablueprint.com/webinar-schedule •  or www.dataversity.net 61 Copyright 2014 by Data Blueprint
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