Succeeding with Analytics: Mastering People, Process, and Technology
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The document discusses the evolution of data management from data warehousing to big data analytics, emphasizing the importance of analytics in understanding past performance, optimizing current strategies, and predicting future trends. It identifies key success factors for analytics, including the right people, tools, and organizational structure, while advocating for the operationalization of analytics and data monetization as strategic assets. Additionally, the document highlights opportunities in consumer empowerment and the transformation of static documents into interactive analytical formats.
Succeeding with Analytics: Mastering People, Process, and Technology
- 1. Succeeding with Analytics Mastering People, Process, and Technology Wayne W. Eckerson Principal Consultant, Eckerson Group Dr. Rado Kotorov Chief Innovation Officer, Information Builders
- 2. © Eckerson Group LLC What’s in a Name? 2 Data Warehousing (Early 1990s) Business Intelligence (Early 2000s) Performance Management (Mid 2000s) Big Data/ Analytics (2010s)
- 3. © Eckerson Group LLC What’s in a Name? 3 DATA INSIGHTS ACTION Data Warehousing (Early 1990s) Business Intelligence (Early 2000s) Performance Management (Mid 2000s) Big Data/ Analytics (2010s)
- 4. © Eckerson Group LLC What’s in a Name? 4 DATA INSIGHTS ACTION DATA ACQUISITION ETL, data modeling, data quality, data warehousing IT ENVIRONMENT Reports, analysis, dashboards, predictive analytics DATA DELIVERY BUSINESS ENVIRONMENT Data Warehousing (Early 1990s) Business Intelligence (Early 2000s) Performance Management (Mid 2000s) Big Data/ Analytics (2010s)
- 5. © Eckerson Group LLC • Understand the past – “Which campaigns provided the best return?” • Optimize the present – “Which packaging provides the best lift in sales?” • Predict the future – “Which customers are likely to respond to this offer?” Why Analytics?
- 6. © Eckerson Group LLC • Right people • Right tools • Right architecture • Right organization Analytics Success Factors
- 7. © Eckerson Group LLC Right People CONSUMERS • Data Consumer • Data Explorer • Data Analyst • Data Scientist • Statistician PRODUCERS • Report Analyst • Data Engineer • Business Developer • App Developer
- 8. © Eckerson Group LLC TOOLS Data Consumer Data Explorer Report Analyst Data Analyst Data Scientist Data Enginee r KPI dashboards Ad hoc reports Report authoring Ad hoc discovery Predictive modeling Data development Right Tools • KPI dashboards = Interactive KPI reporting; locked down data • Ad hoc reports = Modifiable reports that expose data model to data explorers or search BI • Report authoring = Create reports from scratch from semantic layer • Visual discovery = Connect to data sources, mash the data, and visualize results • Predictive modeling = Data science tools • Data development = SQL, ETL and other tools Lines of Support Top Down Bottom up Casual Users Power Users ExposeonDemand
- 9. © Eckerson Group LLC Source Data (Relational and NoSQL, internal, external, devices) On Premise, Cloud, or Hybrid Data Hub (Integrated Tables) Move Stream Flatten Link Business Views (Subject-specific, physical or virtual models) Landing Areas (Raw Tables) Analytic Applications (Reports, dashboard, embedded analytics, custom applications) Data Consumer (50%) Pointandclick Search Data Explorer (40%) Drag/Drop/Edit Adhocreports Design Conform Create Refine Data Analyst (8%) Connect,discover, mash,visualize Data Scientist (2%) Connect,prepare, model,score RDBMS, Hadoop, Spark, Cloud PaaS Systems Analysts 100/0 Data Engineers 80/20 Business Developers 30/70 Data Pipelining, DW Automation, Streaming DB Views, DV tools APIs, SDKs, IDEs KEY: Data/Business skills ARCHITECTUREDEVELOPERSBUSINESSUSERSTOOLS Analytic Platforms, App Development App Developers 20/80 Data Mining Toolkit Statistician Top-Down: Silver ServiceBottom-up: Self Service KEY:%= %ofBusinessUsers Enterprise GovernanceGrassroots Governance Operations Data Catalog Data Prep Data Prep
- 10. © Eckerson Group LLC Right Organization Director/VP BI tools specialists Data architects ETL developers BA Team Business Analytics Council Executive Committee – Business unit sponsors Facilitated by BA Head Working Committee – Business Unit Analyst Managers Facilitated by BA Head and Relationship Managers Analytics Forum – Business unit analysts and data scientists Facilitated by BI Relationship Managers Analytics Dir/VP Statisticians/Data Scientists Business Units Analyst Managers Business analysts Report analysts Source System – Owners - DBAs BA Relationship Managers BA Liaisons 1 per unit 1 per system
- 11. 40+ Years of Sustained InnovationMaking Money with Analytics Dr. Rado Kotorov Chief Innovation Officer
- 12. Not Your Physical Assets; Data Is the Most Strategic Asset Infonomics is the new management science about how to manage data assets
- 14. Bitcoin is data!
- 15. From Data Management to Data Monetization You maximize the business value of your information investments through complete information management and pervasive business intelligence and analytics.
- 16. IB Platform is Built for Data Monetization The methodology for data monetization is aligned with our technology Decision Management Applications Value High Low Raw Data Integrated and Enriched Data Analyzed Information Repurpose Information for Other Revenue Generation Use Cases Land of Opportunities Analytical Tools IT Tools InfoApps™ (Analytic Applications) Customer Facing InfoApps™ Data Assets Data Monetization Managing The Data Value Chain
- 17. Where Do We See The Opportunities?
- 18. Opportunity 1: Operationalize Analytics When analytics is implemented, operationalization is never being considered Only 22% of employees get BI 90% of BI projects fail to produce ROI The secret to success is in empowering the 78%
- 19. Why are the 78% so important? "In the study of organizations, the employee must be the focus of attention, for the success of the structure will be judged by his performance within it". Herbert Simon, Nobel Prize winner for studies in decision-making in 1978 19
- 20. Money Are Made in Operations 20 Insights create opportunities, but only operationalization turns insights into value
- 21. Empower the Mechanics to Save Money 21 An InfoApp from Information Builders available in 14 different languages for 60,000+ users in 14,000 dealerships. Helps employees make on the job repair-or-replace decisions and save Ford $60 million per year.
- 22. Opportunity 2: Big Data Leads to More Customers 22 People discuss only big data analytics, but what about getting more customers?
- 23. Empowering 260,000 Small Advertisers 23 Yellow Pages provides advertisers information to measure the return on their advertising dollars and track the success of their campaigns: Approximately 52 billon rows (nine TB) of raw data per day Response rate 2 to 10 seconds
- 24. Empowering 350,000 Merchants with Spend Analytics 24
- 25. Opportunity 3: Empower the Consumer With Analytics 25 Why not consumerise and commoditize BI and analytics ?
- 26. Billions of Documents Are Distributed to Consumers 26 Every document is an opportunity to consumerise BI and analytics 20 Billion 10 Per month 137 Pages 71% No HTML 10% Provide Linking 14% Navigatbe PDF
- 27. Static PDF Leaves the Consumer Powerless 27 All static PDFs are a missed opportunity to consumerise BI and analytics 98% static
- 28. PDF (No analytics) vs ADF (For In-document analytics) 28 IB Analytical Document Format allows to put the analytics in every document Each PDF file encapsulates: (1) a complete fixed-layout, (2) fonts & images (3) text, data, and charts (4) Requires a reader to render file Each ADF file Encapsulates: (1) a complete fixed-layout, (2) fonts & images (3) text, data, and charts (4) Requires a reader to render file (4) Interactive Analytic Engine (5) Responsive design to fit any device PDF (Portable Document Format) ADF (Analytical Document Format)
- 29. Distributing 2 Million 401K Statements Monthly 29 Huge savings, high adoption, increased customer satisfaction Principal Financial Group: A $2.80 to print, stuff, mail, support 17% switched to eStatements within three months
- 30. Thank you 30
Editor's Notes
- #9: An ideal organization provides built-in help and support to all users because the best training happens in real-time in the trenches when users are trying to do something concrete that they need to do their jobs. Savvy BI directors create a comprehensive chain of embedded support where business users can turn to a colleague in their department for guidance to use a BI tool or extract meaning from data. For example, a casual user (manager) who knows how to navigate and modify interactive reports and dashboards can support a casual viewer (executive) who only views static documents (PDFs, and Excel spreadsheets). A report analyst (i.e., glorified report developer in a business unit) supports casual users by creating new reports and dashboards they need plus they support business analysts who need to know a bit about the data warehouse and the company’s standard interactive BI tool and existing reports. The report analyst converts business analyst output into production reports. A data engineer reviews and creates data sets for data sciences to use, making them more efficient. A data engineer or analyst finds, evaluates, and blends data sets for use by data scientists, who support more complex analyses prototyped by business analysts.
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