TDWI Boston Keynote: The New BI/Analytics Synergy
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To stay relevant in a fast-changing business and data environment, business and analytics leaders need to recognize that their teams are no longer the center of the data universe. They need to reach out and partner with other data analytics players in the organization and create a shared vision for the future. The new business analytics leader fosters a rich analytical ecosystem of people, processes and technologies that fuels a data-driven organization. You Will Learn: - How the data world has changed and why - The cyclical nature of power in the data world - Characteristics of the new analytical ecosystem - The role of BI leaders and teams in the new world order
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TDWI Boston Keynote: The New BI/Analytics Synergy
- 1. © Eckerson Group LLC www.eckerson.com The New BI/Analytics Synergy: How to Align Business and IT Around Data Wayne Eckerson July 30, 2015
- 2. © Eckerson Group LLC www.eckerson.com • The data world has changed • BI is one of many players • BI still has a critical role to play Objectives
- 3. © Eckerson Group LLC www.eckerson.com The Center of the Universe Claudius Ptolemy 90-168 AD Ptolemaic System Geocentrism Copernican System Nicolaus Copernicus 1473-1543 AD Heliocentrism
- 4. © Eckerson Group LLC www.eckerson.com BIcentrism Business Intelligence Reports and Dashboards Data warehouse Relational (Integrated) End-User Tools Architecture Data Reporting Monitoring Analysis Exploration Mining Bill Inmon Ralph Kimball BI/DW Team
- 5. © Eckerson Group LLC www.eckerson.com ContinuousIntelligence DashboardsandAlerts Complexevent processing& streaminganalytics Eventand machinedata Business Intelligence Reports and Dashboards Data warehouse Relational (Integrated) End-User Tools Architecture Data AnalyticalEcosystem Analytics Intelligence Spreadmarts, data silos, analytical sandboxes Relational (Non-integrated) Excel, OLAP, SQL, visual discovery tools, machine learning ContentIntelligence Keyworksearch,Xquery,Hive, Java,Python,Pig,Perl Hadoop,XMLengines,Search engines Non-relational, multistructured Power Users Top-down Bottom-up Reporting Monitoring Analysis Exploration Mining
- 6. Power User Casual User IT-driven Certified data Predefined KPIs Business-driven Ad hoc queries New insights Source Data Staging Area, Hub and Archive Business Intelligence (Reporting & analysis - DW) Analytics Intelligence (Exploration & Discovery) Ad hoc Query, Visualization, and Predictive Tools Corporate Dashboards & Reports (Web and mobile) Continuous Intelligence (Alerting) Analytics/Search Streaming ETL QueryKey: Rules Conceptual Architecture – The New Analytical Ecosystem Query Processing Systems Intelligence Tools AnalyticFunctions OperationalReporting Business Users Data Capture and Pre- processing Systems Operational Systems ContentIntelligence SemanticLayerSemantic Layer DataDirectory Relational Data (ERP, CRM, Legacy) Non- relational Data (Files, Images, Text, etc.) On Premise, Cloud, or Hybrid © Eckerson Group, 2015
- 7. © Eckerson Group LLC www.eckerson.com The New Analytical Organization BI/DW Team Functional Analytic ManagersInfrastructure Administration Business Unit Heads Business Analysts Advanced Analytics Team Reporting Monitoring Analysis Exploration Mining Business Analytics Center of Excellence Business Unit CIOs Chief Data Officers Security/Audit MDM Team
- 8. © Eckerson Group LLC www.eckerson.com Technology Revolution 1993 –DW for a Pharmacy Chain 10 Pentium CPUs 2 GB RAM 1 TB disk $1.6 million 2009 – Typical Desktop 4 core CPU 6 GB RAM 750 GB disk $600 2015 – DW on a phone? 64-bit, 128 GB $60/month Performance Price CPU, Memory, Disk storage Waves of Computing 1970s – Mainframes, Minis 1980s – Client/server, Desktops 1990s – Web/Internet 2000s – E-commerce/Social media 2010s – Mobile, Cloud, Big data
- 9. © Eckerson Group LLC www.eckerson.com Analytical Revolution Old World New World Enterprise Data Warehouse IT-driven Reactive Slow Static Hard to use Tabular Reporting Desktop On premise Transactional data Business vs IT Big Data Ecosystem Self-service Proactive Fast Interactive Easy to use Visual Analytics Mobile Cloud Multi-structured data Business and IT
- 10. © Eckerson Group LLC www.eckerson.com 1990-20001980-1990 Ideal? Power Struggle 2000-2010 2010-2020 Top Down BI IT-driven Focus on standards For casual users To monitor processes Answer known questions Using certified data From the DW GOVERNED Bottom-up BI BU-driven Focus on speed For power users To find new insights Answer new questions Using non-certified data From any data source UNGOVERNED
- 11. © Eckerson Group LLC www.eckerson.com • Deliver certified, trustworthy data – Build and manage the “data warehouse” – Create and maintain data dictionary – Build and monitor analytic sandboxes • Support business analysts – Train to use self-service BI tools – Help them navigate the data warehouse – Convert ad hoc reports into production reports • Support business unit report developers – Matrix to BI team and train in BI tools and processes – Collaborate to build robust departmental dashboards and scorecards Role of BI Team
- 12. © Eckerson Group LLC www.eckerson.com • Create BI/Analytics Center of Excellence – Identify the players – Create shared vision & infrastructure – Facilitate hand-offs between groups • Create a federated BI organization – Matrix BI/data pros in each business unit – Manage steering committees (BI Council) – Go from order takers to consultants • Evangelize value of business analytics – Brand and market analytics – Sell up, down, and across – Run internal conferences • Spearhead data governance initiatives Role of the BI Leader Business Analytics Center of Excellence Federated BI Organization
- 13. © Eckerson Group LLC www.eckerson.com Foster convergence Business Intelligence Advanced Analytics Data Management • LOB Partnership • AA Partnership • BI Self-service • KPI Dashboards • Visual Discovery • Data science • Data engineering • Data analysis • Data directory • Exploration • DW Modernization • Hadoop, NoSQL, and Spark systems • Real-time streaming • Analytic sandboxes Drive business value Analytical ecosystem and team
- 14. © Eckerson Group LLC www.eckerson.com How Evolve?
- 15. © Eckerson Group LLC www.eckerson.com • The data world has changed • BI is one of many players • BI still has a critical role to play Summary
- 16. © Eckerson Group LLC www.eckerson.com Questions? I’m listening!
- 17. © Eckerson Group LLC www.eckerson.com Wayne Eckerson Hingham, MA wayne@eckerson.com (781) 740-9504 Wayne Eckerson • Principal Consultant, Eckerson Group • 20+ year thought leader in BI and analytics • Sought-after speaker and consultant • Former director of research at TDWI • Author of hundreds of articles and reports Performance Management BI/Analytics Data Governance (2016)
- 18. © Eckerson Group LLC www.eckerson.com Eckerson Group RESEARCH CONSULTING ASSESSMENTS Business Intelligence Big Data Management Performance Management Advanced Analytics, Internet of Things Empowering BI and Analytics Leaders Research Consultants Recent Clients Practices
Editor's Notes
- In astronomy, the Geocentric Model (also known as geocentrism, or the Ptolemaic system) is a description of the cosmos where Earth is at the orbital center of all celestial bodies. This model served as the predominant cosmological system in many ancient civilizations such as ancient Greece including the noteworthy systems of Aristotle (see Aristotelian physics) and Ptolemy. As such, they assumed that the Sun, Moon, stars, and naked eye planets circled Earth. Two commonly made observations supported the idea that Earth was the center of the Universe. The stars, the sun, and planets appear to revolve around Earth each day, making Earth the center of that system. The stars were thought to be on a celestial sphere, with the earth at its center, that rotated each day, using a line through the north and south pole as an axis. The stars closest to the equator appeared to rise and fall the greatest distance, but each star circled back to its rising point each day.[2] The second observation supporting the geocentric model was that the Earth does not seem to move from the perspective of an Earth-bound observer, and that it is solid, stable, and unmoving. Epicycle – a planet moves around the epicycle at the same time as the deferent cycle. The epicycle explains the fact that planets slow down, stop and move backwards in retrograde motion. Sometimes the planets appear to slow down or move backwards. This is an illusion caused by the Earth passing slower moving outer planets. Ptolemy - AD 90 – c. 168) was a Greco-Egyptian writer of Alexandria, known as a mathematician, astronomer, geographer, astrologer, and poet of a single epigram in the Greek Anthology.[1][2] He lived in the city of Alexandria in the Roman province of Egypt, wrote in Greek, and held Roman citizenship The scientific revolution began in Europe towards the end of the Renaissance period and continued through the late 18th century, influencing the intellectual social movement known as the Enlightenment. While its dates are disputed, the publication in 1543 of Nicolaus Copernicus's De revolutionibus orbium coelestium (On the Revolutions of the Heavenly Spheres) is often cited as marking the beginning of the scientific revolution, and its completion is attributed to the "grand synthesis" of Newton's 1687 Principia. By the end of the 18th century, the scientific revolution had given way to the "Age of Reflection". It was not until the 16th century that a fully predictive mathematical model of a heliocentric system was presented, by the Renaissance mathematician, astronomer, and Catholic cleric Nicolaus Copernicus, leading to the Copernican Revolution. In the following century, Johannes Kepler elaborated upon and expanded this model to include elliptical orbits, and Galileo Galilei presented supporting observations made using a telescope. Also, Tycho Brahe, Isaac Newton Nicolaus Copernicus (/koʊˈpɜrnɪkəs, kə-/;[1] Polish: 1473 – 24 May 1543) was a Renaissance mathematician and astronomer who formulated a model of the universe that placed the Sun rather than the Earth at its center.[a] The publication of this model in his book De revolutionibus orbium coelestium (On the Revolutions of the Celestial Spheres) just before his death in 1543 is considered a major event in the history of science, triggering the Copernican Revolution and making an important contribution to the Scientific Revolution.
- If you had to report, monitor, analyze etc. you built a DW according to Kimball or Inmon using relational data
- Questions: Who is going to run this? Is it going to work together like depicted or fly apart into multiple bits of BI? There is no longer one intelligence, there are four. We’ve talked about business intelligence (top down) and analytics intelligence (bottom up), but there is also continuous intelligence and content intelligence. All four dimensions seek to turn dat into insight and action, or more tactically, support reporting, monitoring, analysis, exploration, and mining applications. These four dimensions will make up BI by 2020. Maybe even today. At the intersection of the four intelligences are areas ripe for innovation and opportunity. For example, between analytics intelligence and continuous intelligence is decision automation—operationalizing analytical models. The question in most organizations is who is going to manage these four intelligences? Ideally, it all falls under one group or rolls up to one manager. In reality, these areas are often managed by different groups. If so, it’s imperative that the groups work closely together to align their initiatives.
- The conceptual architecture for the new analytical ecosystem depicts four intelligences—business intelligence, continuous intelligence, analytic intelligence, and content intelligence—each with distinct architectures, tools, and development environments, as described in the previous slide. The framework is divided into top-down and bottom-up BI environments, each of which is geared to casual and power users respectively. Each box can be implemented using any technology either as a stand-alone environment or collectively with two or more boxes in a shared environment. Hadoop is increasingly used as the staging area for landing new data, especially multi-structured data, as well as a hub for disseminating data to downstream systems or virtual views to support ad hoc analytics. It’s also used as a low cost archive for storing data that no longer fits cost-effectively in the data warehouse. The data warehouse is represented by the “business intelligence” box. Although it straddles top-down and bottom-up environments, it is not the center of the new analytical ecosystem. The data warehouse is now just one of many component parts. Continuous intelligence systems can run inside the data warehouse if the business requires only 15 minute updates of transactional data. But if the need is to monitor and analyze thousands of events per second, then a specialized streaming system is required.
- Serves as data chief d
- CHANGE DATA DRIVEN CULTURE
- Eckerson Group is a research and consulting firm that provides strategy, design and assessment services for small and large organizations in information-driven disciplines, including BI, DW, data governance, analytics, performance management, and Big Data. www.eckerson.com.
- Eckerson Group is a research and consulting firm that provides strategy, design and assessment services for small and large organizations in information-driven disciplines, including BI, DW, data governance, analytics, performance management, and Big Data. www.eckerson.com.