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Analytics, Business Intelligence, and Data Science - What's the Progression?

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Data analysis can include looking back at historical data, understanding what an organization currently has, and even looking forward to predictions of the future. This presentation will talk about the differences between analytics, business intelligence, and data science, as well as the differences in architecture — and possibly even organization maturity — that make each successful.

Learn more about these topics we will explore including:

Defining analytics, business intelligence, and data science
Differences in architecture
When to use analytics, business intelligence, or data science
Whether there has been an evolution between analytics, business intelligence, and data science

Publicado en: Empresariales
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Analytics, Business Intelligence, and Data Science - What's the Progression?

  1. 1. The First Step in Information Management looker.com Produced by: MONTHLY SERIES In partnership with: Sept. 7, 2017 Analytics, Business Intelligence and Data Science: What's the Progression? Sponsored by:
  2. 2. Topics for Today’s Analytics Webinar  Defining Business Intelligence (BI), Analytics and Data Science  Differences in Architectures  When to Use Analytics, BI and Data Science  Evolution Between Analytics, BI and Data Science  Key Take-Aways  Q&A pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  3. 3. Why is Today’s Topic Important?  Organizations struggle with where to place and how to manage the use of data.  The addition of powerful analytics just adds another item to the stack of data usage that needs to be managed.  Organizations need to be clear about where the capabilities lie – and who is responsible for successful application of all the varieties of using data.  There are numerous alternatives, and there is no one reference model.  Too many organizations are going the self-service route and are failing at meeting their data needs.  Without a good understanding of what will work in your organization, you are at risk. pg 3© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  4. 4. www.firstsanfranciscopartners.com Defining Business Intelligence (BI), Analytics and Data Science
  5. 5. Definitions  No solid demarcation between these “styles” of using data pg 5© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  6. 6. Definitions  No solid demarcation between these “styles” of using data  Business intelligence ‒ *an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. Then does it include analytics? pg 6© 2017 First San Francisco Partners www.firstsanfranciscopartners.com *Source: Gartner IT Glossary
  7. 7. Definitions  No solid demarcation between these “styles” of using data  Business Intelligence - *an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.  Analytics ‒ *Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on). pg 7© 2017 First San Francisco Partners www.firstsanfranciscopartners.com *Source: Gartner IT Glossary
  8. 8. Definitions  No solid demarcation between these “styles” of using data  Business intelligence - *an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.  Analytics – *Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on).  Data science – (grouped in with Advanced Analytics definition) *the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks. pg 8© 2017 First San Francisco Partners www.firstsanfranciscopartners.com *Source: Gartner IT Glossary
  9. 9. Definitions  Business Intelligence  Analytics  Data Science Which one(s) do I use? pg 9© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  10. 10. www.firstsanfranciscopartners.com Differences in Architectures
  11. 11. Architecture Drivers pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Backward-looking Forward-looking Operations and “better decisions” Data science, new insights, strategy Steady state of sources Dynamic Sources TRADITIONAL CONTEMPORARYBUSINESSGOALSAND INFORMATIONREQUIREMENTS TRADITIONAL DIFFERENCES AUDIENCE DATA SOURCE DIFFERENCES BUSINESSINTELLIGENCE ANALYTICS&DATASCIENCE Quality, reliability and precision Enablement not control MANAGEMENT & GOVERNANCE
  12. 12. Architecture Differences pg 12© 2017 First San Francisco Partners www.firstsanfranciscopartners.com ETL, EAI, data quality Anything that works Suitable for batch or near-time Streaming and high volume Queries have limitations Tuned for huge volumes TRADITIONAL CONTEMPORARY INFRASTRUCTURE DATA VOLUMES BUSINESSGOALSAND INFORMATIONREQUIREMENTS BUSINESSINTELLIGENCE ANALYTICS&DATASCIENCE DATA USAGE
  13. 13. It’s a Continuum  Effective use means exploiting data assets  Various standard architectures are presented to allow for understanding; the reality is no single style of architecture can address all situations  Note that algorithms and query complexity are not called out, because you can run complex algorithms against anything pg 13© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Reports Dimensional Query Predictive Modeling Scenario- based Forecasting Goal Seeking Models Normalized Data Structures EDW and Marts Load Hadoop Schema on Read Hadoop Structure and flexibility Sourcing and data types
  14. 14. FSFP Reference Architecture – Abstract pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Data Insight Architecture 1 Data Movement/ Logistics Context Monitoring Controls Management Layer Metadata, Lineage, Work Flow, Models, Reference Data, Rules, Canonical Data Data Access Layer Reports, Visualization Visualization, Prediction, “Closed Loop,” Edge Analytics Traditional Area ERP CRM Finance Traditional Data Collection EDW Data Marts Contemporary Area Edge Processing Ingestion Smart Machines Social Bots Business Strategy Data Scientists Traditional Stakeholders pg 14© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  15. 15. Principles  Determining which part of the data use spectrum to use is a function of principles  Most organizations just declare an architecture; e.g., “We need a lot of data so it has to be a data lake.”  Principles to apply: − Architectures to deliver BI and analytics need to reflect business needs − Supporting organizations around BI and analytics need to reflect true self service − Final architecture solution must be based on support of both modes or vintage and contemporary environments pg 15© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  16. 16. Other Decision Factors  For BI, answer these questions as YES − Are the results intended to be repeatable? − Will the result be made operational? − Are you using the result to make decisions or monitor progress?  Analytics and Data Science is more variable − What is the level of experimentation? − Is AI or machine learning involved? − Are there algorithmic models involved?  Other questions to consider − Does any of the data leave the organization? − What are the regulatory constraints? pg 16© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  17. 17. An Engineering Process to Define the Correct Architecture  Break all the previous notions. Remember there is no mandate for any particular architecture, like Data Warehouse, Data Mart, Operational Data Store and a Data Lake.  Any combination is possible, as long as it meets business needs. 17 Understand Business Strategy and Goals Determine Needs for Operations and Mgmt. Determine Data Needs for Planning and Analysis Determine Org. Support Develop Best-Fit Architecture © 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  18. 18. www.firstsanfranciscopartners.com Organizational Considerations
  19. 19. What’s the Progression? pg 19© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Data analytic tools and approaches go in and out of favor. CENTRALIZED DE- CENTRALIZED Data Science • Focused group of advanced data processing Business Intelligence Competency Center • Centralized capability to enable efficiencies Analytics Workbench • Facilitation of self- service analytics via centralized toolset Self-Service Business Intelligence • Shifting greater flexibility to the user Business-driven Analytics • Purchase and implementation via cloud, independent of IT Citizen Data Scientist • More automated, visual data processing enabling broader adoption TOOLS APPROACHES
  20. 20. Organizational Drivers pg 20© 2017 First San Francisco Partners www.firstsanfranciscopartners.com IT Driven Business Used Business Driven IT Supported STRATEGY Regulatory External / Reputation Internal Experimentation Chaos Drives Innovation RISK TOLERANCE Specialized Hard to find More General Cross-functional SKILLS Centralized Decentralized
  21. 21. Organizational Principles/Decision Factors  Business volatility/variability − How frequently does your business change?  Skills − How adaptable are your people?  Alignment − How well do you collaborate across functions?  Regulatory requirements − How tightly does your data need to be controlled? pg 21© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  22. 22. www.firstsanfranciscopartners.com Evolution Between BI, Analytics and Data Science
  23. 23. Hierarchy of Data Use Solutions pg 23© 2017 First San Francisco Partners www.firstsanfranciscopartners.com AI Operational Analytics/CEP Analytic Experimentation What If Investigate Operate Monitor DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE HINDSIGHTINSIGHTFORESIGHT
  24. 24. Summary: When to Use One or the Other pg 24© 2017 First San Francisco Partners www.firstsanfranciscopartners.com USAGE BASIS WHAT HAPPENED? WHY DID IT HAPPEN? WHAT WILL HAPPEN? MAKE IT HAPPEN BY ITSELF WHAT DO I WANT TO HAPPEN? WHAT SHOULD WE DO NEXT? PERCEIVED MATURITY REPORTING ANALYZING PREDICTIVE OPERATION -ALIZE ADAPTIVE FORESIGHT SOLUTION CATEGORY CAPABILITY REPORTING BUSINESS INTELLIGENCE INITIAL ANALYTICS ADVANCED ANALYTICS / DATA SCIENCE SURVIVAL/ OPERATE OPERATE/MANAGE MANAGE/ PLAN ANTICIPATE/AUTOMATE
  25. 25. www.firstsanfranciscopartners.com What Comes Next
  26. 26. Data Science Enables the Future of Analytics pg 26© 2017 First San Francisco Partners www.firstsanfranciscopartners.com User 1.0 To each their own.. •Persona •Tools/Language •Containers Data 1.0 Fragmented Datasets •Isolated controls •Orphaned Models •Access patterns Technology 1.0 To each their own… •Analytical Tools/Algorithms •Visualization Models •Platforms – exploration to deployment User 2.0 Self-service power persona Data 2.0 Integrated, secure, logical data warehouse Technology 2.0 In-place Analytically complete Platform virtualization Analytics 1.0 Aggregate Dashboards/BI Analytics 2.0 Connected/Mashed Datasets Analytics 3.0 Analytics-in-place at Scale Analytics 4.0 Cognitive/Multimodal Insights; Deep Learning Hypothesis testing Rapid Experimentation In-situ/CEP Insights Artificial Intelligence The rise of Deep Learning Source: May 2017 DIA webinar (Data Scientist interview)
  27. 27. Key Take-Aways  There are many definitions for BI and Analytics. ‒ Your environment to deliver data will never fall into one single definition.  The architectures for delivery will vary widely over time within a single organization. – Focus on fit for purpose.  Use a formal process to determine where and how the data supply chain is sourced, executed, managed and supported. ‒ Do not adopt external reference architecture without an alignment exercise.  BI, Analytics and Data Science will continue to evolve. – Don’t be afraid to “fail fast” within a comfortable cost structure. pg 27© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  28. 28. Questions? pg 28© 2017 First San Francisco Partners www.firstsanfranciscopartners.com MONTHLY SERIES
  29. 29. Thank you for joining – thanks, also, to Looker.com for sponsoring the webinar. Please join us Thursday, Oct. 5 for the “Data Lake Architecture” webinar. Kelle O’Neal @kellezoneal kelle@firstsanfranciscopartners.com

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