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Take Your Enterprise Analytics to the Next Level With Native BI Platforms for Data Lakes

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Many large modern enterprises are data-aware – they deploy processes to transform raw data into information using a variety of data integration, data management, and business intelligence (BI) tools. But being data-aware, or even data-driven, does not necessarily mean being insights-driven.

Are your BI applications providing valuable insights? Are these insights prescriptive and actionable? Are these actions driving tangible business outcomes? In this webcast you will learn what it takes to move your BI environments to the next level by harnessing the power of a data lake to drive new insights and business agility.

Join our webinar where our featured speakers Forrester Vice President and Principal Analyst, Boris Evelson, Alex Gutow from Cloudera, and Steve Wooledge from Arcadia Data will discuss:

Benefits and challenges of becoming an insights-driven business
Benefits of bringing BI to data (vs. bringing data to BI)
Evolution and best practices for modernizing BI through data lakes
Getting the full value of your data with agile BI
Real world customer successes

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Take Your Enterprise Analytics to the Next Level With Native BI Platforms for Data Lakes

  1. 1. Arcadia Data. Proprietary and Confidential Take Your Enterprise Analytics to the Next Level with Native BI Platforms for Data Lakes April 19, 2018
  2. 2. Arcadia Data. Proprietary and Confidential Meet Your Presenters 2 Special Guest Speaker: Boris Evelson VP, Principal Analyst, Forrester Boris serves the Application Development & Delivery role. He is a leading expert in business intelligence (BI) — a set of processes, methodologies, and technologies used to transform raw data into meaningful, useful, and action-oriented enterprise information. Steve Wooledge VP Marketing, Arcadia Data Steve Wooledge is responsible for overall go-to-market strategy and marketing for Arcadia Data. He is an 17-year veteran of enterprise analytics software and customer solutions. Alex Gutow Director, Product Marketing, Cloudera Alex Gutow is the product marketing director at Cloudera, where she focuses on the analytic database platform solution and technologies.
  3. 3. © 2018 FORRESTER. REPRODUCTION PROHIBITED.
  4. 4. © 2018 FORRESTER. REPRODUCTION PROHIBITED. Take Your Enterprise Analytics to the Next Level with Native BI Platforms for Data Lakes Boris Evelson, VP, Principal Analyst April 19th, 2018
  5. 5. © 2018 Forrester Research, Inc. Reproduction Prohibited 5 Data Driven Insights Driven Enterprises must transform from data- driven to insights-driven
  6. 6. © 2018 Forrester Research, Inc. Reproduction Prohibited 6 Systems of insight (SOI) power insights- driven business Source: Forrester’s “Digital Insights Are The New Currency Of Business” report Systems of engagement touch people Systems of record host processes Systems of insight power digital businessSystems of automation connect the physical world
  7. 7. © 2018 Forrester Research, Inc. Reproduction Prohibited 7 “A new kind of company — we call them insights- driven businesses — has formed. They are growing at an average of more than 30% annually and are on track to earn $1.8 trillion by 2021”
  8. 8. © 2018 Forrester Research, Inc. Reproduction Prohibited 8 Income- Statement-Based Top- And Bottom- Line Tangible BI Benefits
  9. 9. © 2018 Forrester Research, Inc. Reproduction Prohibited 9 Balance-Sheet-Based Tangible BI Benefits
  10. 10. © 2018 Forrester Research, Inc. Reproduction Prohibited 10 “We are drowning in data and starving for insight.” — Global Bank The quote >10 years old and we still hear about it from most of our clients
  11. 11. © 2018 Forrester Research, Inc. Reproduction Prohibited 11 46% of organizations still don’t realize quantitative BI/analytics ROI
  12. 12. © 2018 Forrester Research, Inc. Reproduction Prohibited 12 49% of organizations still take one or more years to realize payback on their BI/analytics investments
  13. 13. © 2018 Forrester Research, Inc. Reproduction Prohibited 13 TECHNOLOGY › Single BI platform › Streamlined data architecture › Centralized support › Single version of the truth BUSINESS › I just want to get my job done › Single version of the truth is not my top priority › Good enough but timely data/info is good enough for me Business and technology pros are not in complete alignment
  14. 14. © 2018 Forrester Research, Inc. Reproduction Prohibited 14 While the number of companies storing >100Tb of data almost doubled in 2017… 30% 28% 8% 0% 5% 10% 15% 20% 25% 30% 35% 2015 2016 2017 <10Tb 30% 31% 22% 2015 2016 2017 10Tb-99Tb 31% 31% 59% 2015 2016 2017 >100Tb Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2017
  15. 15. © 2018 Forrester Research, Inc. Reproduction Prohibited 15 Source: anecdotal evidence Used 50% Unused 50% Used 20% Unused 80% Used 33% Unused 67% Used 10% Unused 90% Structured data Unstructured data Perception Reality …We only get insights from a subset of ALL data available
  16. 16. © 2018 Forrester Research, Inc. Reproduction Prohibited 16 Majority of analytical apps are still being built using spreadsheets › 66% report >50% of BI content in spreadsheets › 15% report >80% Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2016
  17. 17. © 2018 Forrester Research, Inc. Reproduction Prohibited 17
  18. 18. © 2018 Forrester Research, Inc. Reproduction Prohibited 18 We have entered the Age of the Customer
  19. 19. © 2018 Forrester Research, Inc. Reproduction Prohibited 19 Awareness Dangerous Formidable Execution Clueless Paralyzed CI Channel integration MR Market responsiveness KD Knowledge dissemination DP Digital psychology CM Change management BI Business intelligence IE Infrastructure elasticity PA Process architecture SI Software innovation SC Sourcing & supply chain Business agility is a key success factor in the age of the customer Source: Forrester’s “The 10 Dimensions Of Business Agility” report
  20. 20. © 2018 Forrester Research, Inc. Reproduction Prohibited 20 Awareness Dangerous Formidable Execution Clueless Paralyzed Lower performers CI MR KD DP CM BI IE PA SI SC Awareness Dangerous Formidable Execution Clueless Paralyzed Higher performers CIMRKD DP CM BI IE PASI SC Source: Forrester’s “The 10 Dimensions Of Business Agility” report Agile enterprises are more likely to be industry leaders
  21. 21. © 2018 Forrester Research, Inc. Reproduction Prohibited 21 Four components of Agile BI
  22. 22. 22© 2018 Forrester Research, Inc. Reproduction Prohibited
  23. 23. © 2018 Forrester Research, Inc. Reproduction Prohibited 23 Data warehouse Data hub Data lake Modern BI data architecture to get insights from ALL data Staging area, data mining, searching, exploration, profiling, cataloging Agile insights apps Mission critical, low latency insights apps • Less expensive HW SW • All enterprise data • More latency • Less governance • Lower data quality • Used by data scientists • More expensive HW SW • Use case specific data • Less latency • More governance • Higher data quality • Used by end users and data analysts Use cases
  24. 24. © 2018 Forrester Research, Inc. Reproduction Prohibited 24 In-memory analytics. Data on demand RDBMS. Single version of the truth. 20%-50% of data Schema-on-write SQL on Big Data. 50% of data Schema-on-read SQL on Big Data. 80% of data Data lake. HDFS. NoSQL. 100% of data Data mining, search, explore, profile, catalog Non mission critical, agile analytical apps Mission critical, stable analytical apps • Less expensive HW SW • All enterprise data • More latency • Less governance • Lower data quality • Used by data scientists • More expensive HW SW • Use case specific data • Less latency • More governance • Higher data quality • Used by end users and data analysts Modern BI data architecture to get insights from ALL data
  25. 25. © 2018 Forrester Research, Inc. Reproduction Prohibited 25 › Data movement in/out of clusters › Increased WAN/LAN traffic › JDBC choke point › SQL › Metadata is lost in/out of cluster › Only queries are distributed and linearly scalable Data Node Data Node Edge Node JDBC BI Server JDBC Earlier generation BI architecture – bring the data to BI Browser Semantic layer Cubes/index Data Node Data Lake Cluster
  26. 26. © 2018 Forrester Research, Inc. Reproduction Prohibited 26 Data Node Data Node Edge Node BI Server Semantic layer Cubes/Index Next generation BI architecture –V1 – bring BI to the data Browser Data Node Data Lake Cluster › No data movement in/out of clusters › No extra WAN/LAN traffic › No JDBC choke point › SQL and files (JSON, etc.) › Rich metadata › Only queries are distributed and linearly scalable
  27. 27. © 2018 Forrester Research, Inc. Reproduction Prohibited 27 Data Node Semantic layer Cubes/Index Data Node Semantic layer Cubes/Index Edge Node Rendering Next generation BI architecture – V2 – bring BI to the data Browser Data Node Semantic layer Cubes/Index Data Lake Cluster › No data movement in/out of clusters › No extra WAN/LAN traffic › No JDBC choke point › SQL and files (JSON, etc.) › Rich metadata › More functionality “pushed down” › Linearly scalable
  28. 28. Arcadia Data. Proprietary and Confidential POLL: How do you (or plan to) give users access to analyze data in data lake? 1. Earlier generation BI architecture (e.g., Tableau, Qlik, MicroStrategy) 2. BI middleware accelerators 3. Native BI architecture 28
  29. 29. FORRESTER.COM Thank you © 2018 FORRESTER. REPRODUCTION PROHIBITED. Boris Evelson bevelson@forrester.com http://www.forrester.com/Boris-Evelson http://blogs.forrester.com/boris_evelson https://twitter.com/bevelson https://www.linkedin.com/in/bevelson https://www.facebook.com/ForresterBI
  30. 30. DATA WAREHOUSING & ANALYTICS WITH CLOUDERA
  31. 31. 31 © Cloudera, Inc. All rights reserved. LIMITATIONS OF EXISTING INFRASTRUCTURE • Not able to take on more reports, use cases, users, etc. • Constrained exploration to prevent risking critical SLAs • Proliferation of data silos to address additional workloads • Maintain data copies causes inefficiencies for storage, processing, and people • Need to contain costs for existing workloads • Difficult to justify budget and maintenance for expansion • Struggle to do more with less • Designed for curated reports, not iterative, self-service analytics • Not built for elasticity or object store integration Compute Store
  32. 32. 32 © Cloudera, Inc. All rights reserved. ADVANTAGES OF A MODERN ANALYTIC DATABASE Data Flexibility • Iterative modeling and self-service accessibility • Portability: No proprietary formats or storage lock-in Go Beyond SQL • Consolidate data silos with an open architecture • Shared data across SQL and non-SQL workloads High-Performance SQL + Cost-Effective Scalability • Elastic scale in any environment • Cloud-native integration for optimized pay-per-use costs • Proven at massive scale Hybrid Decoupled Architecture • Runs across multi-cloud & on-prem for zero lock-in • Multi-storage over S3, ADLS, HDFS, Kudu, Isilon, etc Shared Data
  33. 33. 33 © Cloudera, Inc. All rights reserved. MODERNIZED DATA WAREHOUSING ARCHITECTURE Fixed Reports DATA SOURCES MODERN ANALYTIC DATABASE Flexible Reporting Advanced Analytics Self-Service BI/Ad Hoc Dashboards/ Analytic Apps EDW
  34. 34. 34© Cloudera, Inc. All rights reserved. 5 keys to success 1) Build a data-driven culture 2) Develop the right team and skills 3) Be agile/lean in development 4) Leverage DevOps for production 5) Right-size data governance 34© Cloudera, Inc. All rights reserved.
  35. 35. 35 © Cloudera, Inc. All rights reserved. CLOUDERA ENTERPRISE The modern platform for machine learning and analytics optimized for the cloud Amazon S3 Microsoft ADLS HDFS KUDU SECURITY GOVERNANCE WORKLOAD MANAGEMENT INGEST & REPLICATION DATA CATALOG Core Services Storage Services ANALYTIC DATABASE DATA SCIENC E EXTENSIBLE SERVICES OPERATIONAL DATABASE DATA ENGINEERING
  36. 36. Arcadia Data. Proprietary and Confidential BI – “Native” to Data Lakes Steve Wooledge
  37. 37. Arcadia Data. Proprietary and Confidential 37 “Data” and “Platforms" Have Changed – Why Haven’t BI Tools? From To Data Platforms BI Tools rows and columns and multi-structured batch and interactive and real-time small and large volumes many sources internal and external tables and doc’s, search indexes, events schema on write and schema on read commodity hardware ETL and ELT and ELDT data warehouses and data lakes rows and columns batch smaller data volumes limited # sources mainly internal tables schema on write proprietary hardware ETL data warehouses SQL queries extracts cubes BI servers small/med scale Why haven’t BI tools evolved?
  38. 38. Arcadia Data. Proprietary and Confidential 38 Enterprises Today Need Two Separate BI Standards
  39. 39. Arcadia Data. Proprietary and Confidential 39 Data Warehouse BI Architecture 39 BI Server Analytic Process Optimize Physical Semantic Layer Secure Data Load Data Big Data Requirements Native Connection Semi-Structured Parallel Real-time Data Warehouse (RDBMS)
  40. 40. Arcadia Data. Proprietary and Confidential 40 Data Lake BI Architecture – The Native BI and Analytics Way 40 BI Server Analytic Process Optimize Physical Semantic Layer Secure Data Load Data Big Data Requirements Native Connection Semi-Structured Parallel Real-time Data Warehouse (RDBMS) Data Lake (*DFS, Cloud Object Storage) Arcadia Data was built from inception to run natively within data lakes
  41. 41. Arcadia Data. Proprietary and Confidential 41 Query acceleration for scale, performance, and concurrency Smart Acceleration Leverages What Is Learned during Data Discovery Ad hoc queries Arcadia Enterprise makes recommendations – build these with a click. Data Lake Cluster • Fast query responses • Minimal modeling • Live acceleration (no downtime) All Granular Data Analytical Views Accelerated application queries
  42. 42. Arcadia Data. Proprietary and Confidential42 Visual Insights To Purchase Paths “Arcadia Enterprise is the first product we found that provides truly on-cluster Hadoop BI… Its execution model and user self-service approach deliver performance at Hadoop scale and let us develop our analytics quickly.” — Director, Global Business Services Digital Marketing Use Cases • Increase campaign effectiveness • Measure brand recognition • Understand and respond to customer preferences • Incorporate insights into future products Challenges • Fragmented silos of applications with product and brand information • Lack of granular insight into customer response to marketing campaigns • Manual process to create reports requires data extraction & movement Results • 100s of brand managers have direct access to self-service visual analytics across all data on the effect of digital campaigns on product performance • Increased visibility into campaign effectiveness and brand recognition across geographies • Marketers and product managers can leverage insights to drive campaign creation and execution as well as product roadmap
  43. 43. Arcadia Data. Proprietary and Confidential Data Drives Market DisruptionRetail Store Drill Down Interactive maps allow for easy visualization of spatial data zooming into details
  44. 44. Arcadia Data. Proprietary and Confidential44 Faster Supply Chain Optimization “Supply chain optimization with visual analytics has been transformative for us.” — Director of BI & Analytics Use Cases • Integrate financial and physical flow data • Self-service visual analytics Challenges • One-off consulting project typically costs hundreds of thousands of dollars and lasts 6-8 months. Results • Business analysts have instant access to all data – no data movement necessary • Visualizations make it easy to highlight anomalies and potential issues • Analysts, engineers, and data scientists all can create stories directly on the data
  45. 45. Arcadia Data. Proprietary and Confidential 45 BI Native to Data Lakes Provides Faster Time to Value 1. Land / secure data 2. Build semantic layer 3. Analytic / Visual Discovery 5. Production Visual Analytics and BI Native to Data Lake 4. AI-driven performance modeling 1. Land / secure data 4. Performance Modeling - Cube / Aggregates 6. Analytic / Visual Discovery Data Warehouse or Data Lake Traditional BI Server or Middleware Cubes 9. Production Time to Value in Days Time in Value in Weeks or Months 2. Transform 3NF or Star Schema 3. Build Semantic Layer 7. Performance modeling (2 places) Time to Value Delayed Weeks - One security model - No movement of data - Discover and take action - Model based on usage 8.
  46. 46. Arcadia Data. Proprietary and Confidential 46 Top Use Cases for Native BI and Analytics on Data Lakes 46 MODERN BI PLATFORM & CUSTOMER INTELLIGENCE FINANCIAL SERVICES AND INSURANCE RISK & SECURITY OPTIMIZATION IOT ANALYTICS  DW optimization  Customer 360  Marketing analytics  Fundamental Review of Trading Book (FRTB)  Trade surveillance  Anti-money laundering  Location intelligence  Cybersecurity  Security information & event management  Fraudulent behavioral analysis  Data center monitoring  Telematics  System log analysis  Manufacturing quality assurance  Predictive maintenance
  47. 47. Arcadia Data. Proprietary and Confidential Demo: See It in Action
  48. 48. Social media: @arcadiadataarcadiadata.com 48 Thank You The Forrester Wave™: Native Hadoop BI Platforms, Q3 2016 See Cloudera & Arcadia in Action Download Arcadia Instant https://www.arcadiadata.com/lp/forrester-wave- hadoop-bi-research-report/ https://www.youtube.com/watch?v=APPpg GNP5Gs arcadiadata.com/instant The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave™. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.

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