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DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Business Goals

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Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.

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DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Business Goals

  1. 1. Copyright Global Data Strategy, Ltd. 2019 Building a Data Strategy - Practical Steps for Aligning with Business Goals Donna Burbank, Managing Director Global Data Strategy, Ltd. February 28th, 2019 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  2. 2. Global Data Strategy, Ltd. 2019 Donna Burbank 2 Donna is a recognised industry expert in information management with over 20 years of experience in data strategy, information management, data modeling, metadata management, and enterprise architecture. Her background is multi-faceted across consulting, product development, product management, brand strategy, marketing, and business leadership. She is currently the Managing Director at Global Data Strategy, Ltd., an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. In past roles, she has served in key brand strategy and product management roles at CA Technologies and Embarcadero Technologies for several of the leading data management products in the market. As an active contributor to the data management community, she is a long time DAMA International member, Past President and Advisor to the DAMA Rocky Mountain chapter, and was recently awarded the Excellence in Data Management Award from DAMA International in 2016. Donna is also an analyst at the Boulder BI Train Trust (BBBT) where she provides advice and gains insight on the latest BI and Analytics software in the market. She was on several review committees for the Object Management Group’s for key information management and process modeling notations. She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa and speaks regularly at industry conferences. She has co- authored two books: Data Modeling for the Business and Data Modeling Made Simple with ERwin Data Modeler and is a regular contributor to industry publications. She can be reached at donna.burbank@globaldatastrategy.com Donna is based in Boulder, Colorado, USA. Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  3. 3. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA) • April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner) • May 23 Master Data Management - Aligning Data, Process, and Governance • June 27 Enterprise Architecture vs. Data Architecture • July 25 Metadata Management: from Technical Architecture & Business Techniques • August 22 Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 Self Service BI & Analytics: Architecting for Collaboration • October 24 Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 3 This Year’s Lineup
  4. 4. Global Data Strategy, Ltd. 2019 Today’s Topic Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started. 4
  5. 5. Global Data Strategy, Ltd. 2019 What is a Data Strategy? 5 Strategy: 1. the art of devising or employing plans or stratagems toward a goal 2. an adaptation or complex of adaptations (as of behavior, metabolism, or structure) that serves or appears to serve an important function in achieving evolutionary success 3. the science and art of military command exercised to meet the enemy in combat under advantageous conditions Strategy vs. Management - Source Merriam Webster Management: 1. judicious use of means to accomplish an end 2. the act or art of managing : the conducting or supervising of something (such as a business) - Source Merriam Webster
  6. 6. A Successful Data Strategy links Business Goals with Technology Solutions Level 1 “Top-Down” alignment with business priorities Level 5 “Bottom-Up” management & inventory of data sources Level 2 Managing the people, process, policies & culture around data Level 4 Coordinating & integrating disparate data sources Level 3 Leveraging data for strategic advantage Copyright 2019 Global Data Strategy, Ltd Aligning Business and Data Strategy Global Data Strategy, Ltd. 2019 www.globaldatastrategy.com
  7. 7. Global Data Strategy, Ltd. 2019 Business & Data Strategy – the Interdependency 7 Business Strategy Data Strategy Informs & Guides Informs & Guides Business Strategy
  8. 8. Global Data Strategy, Ltd. 2019 Current State AssessmentBusiness Goals & Strategy Implementation RoadmapProposed Future State Where to Begin? Data Strategy Assessment & Roadmap Understanding Current Maturity & Environment Identifying Business Goals & Objectives Aligned to Data Prioritizing Efforts & Identifying “Quick Wins” Propose Future State Capabilities, Processes & Organizational Structure Communication & Evangelism Business Motivation Model Data Management Maturity Assessment 3-12-24 Month Roadmap KPIs and Metrics Organizational Structure & Framework Architecture & Technology Recommendations Technology Landscape Overview Roles, Skills & ResponsibilitiesProcesses & Procedures Summary & Recommendations www.globaldatastrategy.comGlobal Data Strategy, Ltd. 2019 Business Drivers Mapped to Strategic Data Initiatives
  9. 9. Global Data Strategy, Ltd. 2019 9 Level 1: Aligning Business Strategy with Data Strategy
  10. 10. Global Data Strategy, Ltd. 2019 Business Motivation Model 10 Corporate Mission Corporate Vision Goals & Objectives To provide a full service online retail experience for art supplies and craft products. To be the respected source of art products worldwide, creating an online community of art enthusiasts. Artful Art Supplies ArtfulArt C External Drivers Digital Self-Service Increasing Regulation Pressures Online Community & Social Media Customer Demand for Instant Provision Internal Drivers Cost Reduction Targeted Marketing 360 View of Customer Brand Reputation Community Building Revenue Growth C Accountability • Create a Data Governance Framework • Define clear roles & responsibilities for both business & IT staff • Publish a corporate information policy • Document data standards • Train all staff in data accountability C Quality • Define measures & KPIs for key data items • Report & monitor on data quality improvements • Develop repeatable processes for data quality improvement • Implement data quality checks as BAU business activities C Culture • Ensure that all roles understand their contribution to data quality • Promote business benefits of better data quality • Engage in innovative ways to leverage data for strategic advantage • Create data-centric communities of interest • Corporate-level Mission & Vision • May already be created or may need to create as part of project. • Project-level, Data-Centric Drivers • External Drivers are what you’re facing in the industry • Internal Drivers reflect internal corporate initiatives. • Project-level, Data-Centric Goals & Objectives • Clear direction for the project • Use marketing-style headings where possible
  11. 11. Global Data Strategy, Ltd. 2019 The Role of the Data Professional in the Data-Driven Business • In the current environment of data-driven business, Data Professionals have an opportunity to have a “seat at the table” • Finding new opportunities to leverage data for business benefit • Creating efficiencies & business process optimization • Integrating data from disparate sources for new business insights • Supporting organizational change 11
  12. 12. Global Data Strategy, Ltd. 2019 Look for Business Value “Levers” • Identify areas that will derive the highest business value by addressing. • Is this supporting the new marketing campaign for a high visibility product launch? • Or are you “re-arranging the deck chairs on the Titanic” – i.e. focusing valuable time and effort no low-value activities • As with any areas of the business that have value, it is helpful to build a model or architectural design around the key areas of business value. Identify “Quick Wins” LoadEffort Fulcrum Identify areas where data can be the fulcrum.
  13. 13. Global Data Strategy, Ltd. 2019 Mapping Business Drivers to Data Objectives 13 Challenges Aligning Business & IT • Policies not actionable at technology level • Clarity needed around core business definitions • Better taxonomies & classifications needed Information Silos • Key definitions stored in single use tools • Reporting & Metrics differ across groups • Database structures not easily accessible • Documentation often project-focused & not widely shared. Limited Information Context • Lack sufficient context to manage & consume data effectively • Lack of data lineage to view key relationships • Time spent searching for data & definitions, not new business uses for data. • No single ‘roadmap’ of information assets Process Inefficiencies • Policy interpretations are done as snapshots • Duplication of effort across reporting teams (e.g. different queries for same use case) Single View of Customer • Single View of Customer across systems • Map data elements to Customer Journey • Automate PII classification for Privacy & Security • “How Can Data Better Support Our Customers? Establish Integration & Lineage • Automate data lineage between key data sources • Source to target mapping for reporting • Change impact analysis • Policy Audit • “Understand Critical Connections Between Data” Data-Centric Objectives Publish Common Data Catalogue • Provide common Business Glossary • Publish physical data structures • Share common queries for reuse • “Reuse & Efficiency via Information Sharing.” Support Collaboration & Discovery • Build a collaboration platform for sharing of ideas • Provide a common map of data to highlight new ways to leverage & integrate information • Assign data stewards for key data areas • “Innovation through Collaboration” Customer Satisfaction & Brand Integrity Business Effectiveness Collaboration & Insights Operational Efficiency Risk Reduction Business Drivers
  14. 14. Global Data Strategy, Ltd. 2019 14 Level 2: People, Process, and Culture
  15. 15. Global Data Strategy, Ltd. 2019 Speak with a Wide Variety of Stakeholders 15 • It’s important to speak with a wide range of roles across the organization. • Business & IT • Cross-functional teams (Marketing, Finance, Analytics, etc, etc.) • Understand key opportunities & challenges. • Recruit allies & volunteers (and identify those you still need to convince. ☺ )
  16. 16. Global Data Strategy, Ltd. 2019 Data Governance – A Basic Framework Organization & People Process & Workflows Data Management & Measures Culture & Communication Vision & Strategy Tools & Technology Business Goals & Objectives Data Issues & Challenges Managing the Complexity Interactions between Technology, Organizations, and People
  17. 17. Global Data Strategy, Ltd. 2019 Building the Data Governance Framework 17 Vision & Strategy Organization & People Processes & Workflows Data Management & Measures Culture & Communications Tools & Technology Is there a clear understanding of the strategic goals of your organization & the need for enterprise data governance? Who are the key data stakeholders within and outside your organization? Do business process design and operations management take data needs into account? Has key data been identified, defined and analyzed? Has the importance of data been communicated across the organization? Is there a data communications plan? Is there a coherent data architecture in place to define and guide how data is captured, processed, stored and used? How does your organization rely on data – now and in the future? Who are the primary data producers, consumers & modifiers? Are there any specific data management / improvement processes in place? Have data models been built – conceptual / logical / physical? Is the value of good data management understood and championed by senior managers? What primary IT systems and platforms are used to store and process key data? What impact are data problems currently having on your organization? Are individuals formally accountable for data ownership? Are there issue and workflow management processes to address data problems? Has the relationship between business processes and data been mapped? Do all employees and third parties receive data awareness and improvement education and training? Do design gateways exist to ensure data needs are taken into account in new & modified platforms? Do you have a data governance policy? Are employees trained in good data management practices? Has there been any analysis of the efficiency and effectiveness of how data is managed within operational business processes? Are data shortcomings known, measured & recorded? Are there communication channels for communicating best practice in data management? What specialist data management tools are currently in use? What are the overall expected benefits of better data governance? Are there any channels through which data shortcomings can be highlighted and investigated? How does the business and IT interact to manage data improvement? Are there are formal standards & rules specifying how data should be managed and improved? Are there internal success stories that could be used to promote better data management across the organization? What metadata is captured and stored?
  18. 18. Global Data Strategy, Ltd. 2019 Mapping Organizational Capability • Organizational Capability, Organizational Structure, and Roles are key to any Data Strategy 18 Aligning to Organizational Capabilities e.g. From Plan to Production to Sales & Distribution Designing Org Structures for Data-Centric Efforts e.g. Aligning Data Governance to Individual Culture
  19. 19. Global Data Strategy, Ltd. 2019 5 Basic Models of Data Governance & Stewardship Model Description Process Centric Process owner(s) become(s) the data owner for all data created, amended & deleted by the business process for which he / she is responsible (e.g. Claims process, Billing process, etc.) Systems Centric System owner(s) become(s) the data owner for all data created, amended & deleted by the IT system for which he / she is responsible (e.g. CRM, Billing System, etc.) Data Domain Centric Business appointed full or part-time roles accountable for improvement of key data domains, created, stored or used across an organization (e.g. Patient, Student, Product, Customer, etc.) Organization Centric Business appointed FT or PT roles accountable for improvement of key data domains on the basis of departmental boundaries (e.g. Finance, Marketing, Clinical, etc.) or geographical locations. Blended In large and complex organizations, an overall Data Governance program may consist of combinations of some or all of the above models 19 • There are diverse ways to implement data stewardship, unique to each organization.
  20. 20. Global Data Strategy, Ltd. 2019 20 Level 3: Leveraging Data for Strategic Advantage
  21. 21. Global Data Strategy, Ltd. 2019 Visualizing Current vs. Target Maturity • It’s important to take a realistic look at your organization’s current state maturity • Where you are • Where you want to be 21 Determine Relative Strengths Significant Gap in Data Governance Metadata Management meets Target Maturity We’re “overdoing it” for Data Architecture
  22. 22. Global Data Strategy, Ltd. 2019 Find a Balance in Implementing Data Architecture • Find the Right Balance • Data Architecture projects can have the reputation for being overly “academic”, long, expensive, etc. • No architecture at all can cause chaos. • When done correctly, Data Architecture helps improve efficiency and better align with business priorities 22 Focus on Business Value Business Value Too Academic, nothing gets done Too “Wild West”, nothing gets done - chaos
  23. 23. Global Data Strategy, Ltd. 2019 Developers Managers Enterprise Data Management Part of a Data Strategy is Defining Fit for Purpose Solutions Operational Data Reporting & Analytics Master & Reference Data Metadata CRM Customer X orders Product Y at 2pm on Oct 24, 2017 Sales CRM ERP Customer Care IoT Customer X calls Support at 1pm on Nov 1, 2017 Inventory consists of x number of Product X components on Oct 24, 2017 Supply Chain Customer turns on foot warmer at 11pm on Oct 30, 2017 Product Team Customer CRM & other systems DW What were total sales for Product X in 2016 by region? Lake Operational Reporting Enterprise Historical Reporting Analytics & Discovery What variables most influence customer repeat purchases? Limited Personal Use Limited ad hoc analysis for small data sets. Not recommended for enterprise data management. UCM UPM “Golden Record” for Customer, Product, etc. Mary Smith lives on 101 Main ST, Detroit, MI and has been a customer since 2011 Product 720 has a product code of SS720 & a suggested retail price of $11,000 USD. Business & Technical Context & Descriptions ELT How many support calls are currently open? Analytics Team Managers Reference Data Hierarchies The Sales management reporting hierarchy is structured as follows. Valid Return Codes are “X, Y, & Z” State Codes include MA, MD MI … Applications DW Etc DW Etc DW Etc Business Glossary How is Total Sales calculated? What is a Qualified Lead? Business Users Data Models How do we uniquely identify a customer? Can a customer have more than 1 email? Data Dictionary What is this DW table used for? The standard length for customer ID is CHAR(12) Developers Data Lineage How was this field calculated? What will break downstream if I make a change? Developers Developers Business Users Access
  24. 24. Global Data Strategy, Ltd. 2019 Master Data Management & Data Integration • Master Data Management (MDM) is the practice of identifying, cleansing, storing & governance core data assets of the organization (e.g. customer, product, etc.) • There are many architectural approaches to MDM, and data integration overall. Two are the following: 24 Centralized -- Commonly Relational Virtualized/Registry – Commonly Graph MDM Virtualization Layer • Core data stored in a common schema in a centralized “hub”. • Used as a common reference for operational systems, DW, etc. • Data remains in source systems. • Referenced through a common virtualization layer. BOTH require the same core foundation of data quality, parsing & matching, semantic meaning, data governance, etc. in order to be successful… and that’s usually the hardest part.
  25. 25. Global Data Strategy, Ltd. 2019 Master Data Management Data Architecture Data Governance & Stewardship Business Process Alignment • Accountability & stewardship • Business rule validation • Conflict resolution • Business Prioritization • Business process models • Data mapping to process • CRUD and usage matrices • Optimizing business process for data improvement • System Architecture & data flow • Data models & hierarchies • Match/merge and survivorship rules • Data integration & design Successful MDM Combines Data, Process, & Accountability
  26. 26. Global Data Strategy, Ltd. 2019 26 Level 4: Coordinating & Integrating Disparate Data Sources
  27. 27. Global Data Strategy, Ltd. 2019 Both Business & Technical Drivers Require Data Integration 27 A Data Model is a Common Reference Hub for Business & Technical Rules Business Drivers Technology Drivers Enterprise Knowledge Inventory Mergers & Acquisitions Innovation & Collaboration Efficiency & Agility Etc… Data ModelData Warehousing Master Data Management (MDM) Data Lake APIs & Application Integration Etc… A Data Model can be a Common Reference
  28. 28. Global Data Strategy, Ltd. 2019 Metadata Management Tools • The following are common architectural options for metadata management within & between organizations. • There is no “one size fits all” approach. • They can be used together within the same organization. 28 Central, Enterprise-wide Metadata Catalogue / Repository Metamodel(s) Metadata Storage (Database) Population Interfaces Matching & Reuse Logic Publication & Sharing Reports Web Portal Integration & Export Tool or Purpose-Specific Repository Business Glossary ETL Tool Data Modeling Tool BI ToolEtc Data Dictionary Database Metadata Exchange & Registry Information Sharing & Standards
  29. 29. Global Data Strategy, Ltd. 2019 Data Catalogues - Harnessing “Tribal Knowledge” 29 Usage Ranking • Which: • Definitions are most complete & helpful? • Algorithms offer a helpful starting point? • Queries offer great logic to share? • Etc. Helpfulness Ranking • Which: • Queries are others using? • Tables are accessed the most? • Glossary terms are most often searched? • Etc. Collaboration & Crowdsourcing Term: Part Number Alternate Names: Component Number Definition: A part number is an 8 digit alphanumeric field that uniquely identifies a machine part used in the manufacturing process. Is this truly the same as the old Component Number? That was a 10 digit numeric field. It didn’t have letters. Yes, it is. I had the same problem for the finance app, and I wrote a quick program to convert the numbers. We just strip off the first two chars now. Click here to find it.
  30. 30. Global Data Strategy, Ltd. 2019 Crowdsourcing Governance & Metadata Definitions • Many data governance projects (& vendors) are embracing the concept of “crowdsourcing”. i.e. The Wikipedia vs. Encyclopedia approach • Open editing • Popularity & Usage Rankings • Dynamically changing 30 Encyclopedia Wikipedia • Created by a few, then published as read-only • Single source of “vetted” truth • Static • Created by a by many, edited by many • Eventual consistency with multiple inputs • Dynamic For Standardized, Enterprise Data Sets For Self-Service Data Prep & Analytics
  31. 31. Global Data Strategy, Ltd. 2019 31 Level 5: “Bottom-Up” Management & Inventory of Data Sources
  32. 32. Global Data Strategy, Ltd. 2019 Data Source Inventory • Document key data sources across the organization • …as well as who is using them (i.e. key departments & stakeholders) • Data models & other architecture tools can help document the technical structures & metadata 32 Data Sources Leadership Sales Finance Marketing Support R&D HR Legal Compliance Relational Databases MySQL X Oracle X X X X X X X X SQL Server X X Sybase X Etc. BI Tools Tableau X X X X X X Qlik X X X Etc. Open Data Data.gov – agricultural data X X X Etc.
  33. 33. Global Data Strategy, Ltd. 2019 Data Modeling Creates an “Active Inventory” of Data Assets • Know what data you have: Create a visual inventory of database systems • Know what your data means: Communicate key business requirements between business and IT stakeholders • Support data consistency: Build consistent database structures & support data governance initiatives Sybase MySQL Oracle Data Models Teradata Sybase SQL Server DB2 Teradata SQL Server DB2 MySQLSQL Azure SQL Azure Oracle
  34. 34. Global Data Strategy, Ltd. 2019 Industry Trends: Data Platforms are Currently in Use? • A wide range of technologies are currently in use: • Relational databases most common o Both Cloud & On-Premises • Spreadsheets ubiquitous 34 “Which of the following data sources or platforms are you currently using? [Select all that apply] Relational Databases are still clearly the leader. Spreadsheets are ubiquitous More Legacy platforms (44.6%) than Big Data (42.2%) From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017
  35. 35. Global Data Strategy, Ltd. 2019 Industry Trends: Emerging Technologies 35 “Which of the following do you plan to use in the future that you are not using currently? [Select all that Apply]” Many looking to Big Data Platforms Movement to the Cloud is popular Uncertainty is common. • For those looking at new technologies, there is a wide range of responses. • Big Data Platforms a leader • Move to Cloud RDMBS • Graph Database • Real-time Streaming • Internet of Things (IoT) • Many are still uncertain, indicating the vast rate of change and wide array of choices available. From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017
  36. 36. Global Data Strategy, Ltd. 2019 36 Building a Roadmap: Putting it All Together
  37. 37. Global Data Strategy, Ltd. 2019 Defining an Actionable Roadmap • Develop a detailed roadmap that is both actionable and realistic • Show quick-wins, while building to a longer-term goal • Balance Business Priorities with Data Management Maturity • Focus on projects that benefit multiple stakeholders • Mix core architecture with “new shiny things” 37 Maximize the Benefit to the Organization Initiatives H1 '17 H2 '17 H2 '18 H2 '18 Strategy Development Governance Lineage for Privacy Rules Business Glossary Population & Publication Data Warehouse Metadata Customer Analytics Pilot – Social Media integration Open Data Publication IoT Integration Ongoing Communication & Collaboration Customer Product Location Integrated Customer View Marketing Sales Customer Support Executive Team
  38. 38. Global Data Strategy, Ltd. 2019 Evangelism & Outreach • Key to long-term success is continued evangelism & outreach • Communicate, communicate, communicate! • Training & education • Newsletters • Webinars • “Branding” & Collateral • 1:1 Briefings • “Lunch & Learns” • Conference presentations • Service Catalogues • Etc.
  39. 39. Global Data Strategy, Ltd. 2019 39 Key Steps to Creating a Data Program • The following steps should be included when creating a data program. The order is less important than ensuring that they are completed. Steps to Success Secure Senior Executive Support • Identify a Data Champion among senior leadership. Define Vision, Drivers & Motivations • Define business-driven vision for the program. Build the Business Case • Outline key benefits of data program & risks of not doing so Deliver “Quick” Wins • Short, iterative, business-driven projects deliver short-term value, building towards long-term gain. Identify Business-Critical Data • Focus on the data that has the highest impact on the business. Identify & Interview Stakeholders • Elicit feedback from key stakeholders – listen & communicate. Create Organization • Define an organizational structure that aligns with your way of working. Communicate • Build a communication plan from initial feedback phase throughout all phases of the program. Assess IT Maturity • Assess the maturity of the IT organization across all aspects of data management. Map Business Priorities to IT Capabilities • Create a realistic “heat map” aligning business goals with data management capabilities.
  40. 40. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA) • April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner) • May 23 Master Data Management - Aligning Data, Process, and Governance • June 27 Enterprise Architecture vs. Data Architecture • July 25 Metadata Management: from Technical Architecture & Business Techniques • August 22 Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 Self Service BI & Analytics: Architecting for Collaboration • October 24 Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 40 Join Us Next Month
  41. 41. Global Data Strategy, Ltd. 2019 Related Article • Related article on DATAVERSITY, Sept 2017: • Data Management vs. Data Strategy: A Framework for Business Success 41 To Read More
  42. 42. Global Data Strategy, Ltd. 2019 About Global Data Strategy, Ltd • Global Data Strategy is an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. • Our passion is data, and helping organizations enrich their business opportunities through data and information. • Our core values center around providing solutions that are: • Business-Driven: We put the needs of your business first, before we look at any technology solution. • Clear & Relevant: We provide clear explanations using real-world examples. • Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of technical expertise in the industry. 42 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy Visit www.globaldatastrategy.com for more information
  43. 43. Global Data Strategy, Ltd. 2019 Questions? 43 • Thoughts? Ideas?

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