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BI2017 Analytics Innovation, Disruption, and Transformation

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Presentation on analytics trends from SAP Insider BI2017 event, Feb 28th, Orlando, Florida.

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BI2017 Analytics Innovation, Disruption, and Transformation

  1. 1. @timoelliott Timo Elliott, SAP Analytics Innovation, Disruption And Transformation
  2. 2. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 1 Agenda Top Trends Supporting “Modern BI” Big Data Architectures Predictive & Machine Learning Organizing for Data Wrap-up
  3. 3. Top Trends
  4. 4. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 3 Top Strategic Technology Trends, 2017 INTELLIGENT DIGITAL MESH Applied AI & Advanced Machine Learning Intelligent Apps Intelligent Things Virtual & Augmented Reality Digital Twins Blockchains and Distributed Ledgers Adaptive Security Architecture Digital Technology Platforms Mesh App and Service Architecture Conversational Systems Source: Gartner Identifies the Top 10 Strategic Technology Trends for 2017 (Gartner, 2016)
  5. 5. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 4 Technology Priorities for 2017 and Beyond Rank Technology Trend 1 BI/Analytics 2 Cloud 3 Digitalization / Digital Marketing 4 Infrastructure & Data Center 5 Mobile 6 Cyber and information security 7 Industry-Specific Applications 8 ERP 9 Networking, Voice, and Data Comms Ten out of Twelve years 2006-2017 ANALYTICS #1 Source: Gartner
  6. 6. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 5 WE ARE USED TO PROCESSES GENERATING DATA FOR ANALYTICS BUSINESS PROCESS BUSINESS INTELLIGENCE A Big Change
  7. 7. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 6 BUT DIGITAL TRANSFORMATION IS ABOUT ANALYTICS CREATING NEW PROCESSES BUSINESS PROCESS BUSINESS INTELLIGENCE A Big Change
  8. 8. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 7 By 2020, information will be used to reinvent, digitalize, or eliminate % of business processes and products from a decade earlier. From The Back Office To The Business Models of Future ” “ Source: Gartner
  9. 9. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 8© 2017 SAP SE or an SAP affiliate company. All rights reserved. Analytics Enables Live Business
  10. 10. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 9 BI Success … “BI initiatives described as ‘successful’ dropped from 41% to 35% in 2015” Techtarget, 2015 Source: New reports highlight state of BI reporting tools (TechTaget, 2015)
  11. 11. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 10 Are you a BI-nosaur?
  12. 12. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 11 Complaints… 31% wait days or weeks for an average BI request 32% say Enterprise BI too complex, complicated, cumbersome to use Enterprise systems don’t have all the data needed -- >45% from outside Source: Forrester
  13. 13. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 12 The Penetration of BI Remains Low “Close to 40% of organizations report fewer than 10% of employees using BI” Source: New reports highlight state of BI reporting tools, Techtarget, 2015
  14. 14. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 13 Are You The Taxi Company?
  15. 15. Supporting “Modern BI”
  16. 16. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 15 Data-Driven Approach Push: • From IT • Data-Driven • Data to Insight • Technology-Centric A.S.P.I.R.E.
  17. 17. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 16 Value-Driven Approach Pull: • From LOB • Outcome-Driven • Insight to Data • Use-Case-Centric
  18. 18. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 17 Combination Approach Push: • From IT • Data-Driven • Data to Insight • Technology-Centric Pull: • From LOB • Outcome-Driven • Insight to Data • Use-Case-Centric
  19. 19. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 18 “Modern BI” DATA Self-service data preparation Structured/Unstructured Internal/External Batch/Streaming Integration, blending Cleansing, augmentation Agile modeling BI DB Columnar In-memory Self-service data analysis Data discovery Visual exploration Dashboards/storytelling Agile Iteration Now considered “optional!” Data warehouse Semantic layers OLAP Cubes
  20. 20. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 19 Invest in Self-Service Data Discovery Tools “Through 2020 spending on self-service visual discovery and data preparation market will grow 2.5x faster than traditional IT-controlled tools for similar functionality” – IDC, 2015
  21. 21. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 20 Invest in Self-Service Data Preparation SAP Agile Data Preparation I.e., “Data Blending” — combine, merge, cleanse data
  22. 22. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 21 Invest in Predictive Analytics Model deployed using In-Database-Apply Customer Database Hancock, John M 38 D Y 4.2 N Y Doe, Jane F 45 M Y 9.4 N N Red, Simply F 18 S N 2.1 N Y SQL Dataset w/ Scoring Business Users can get on-the-fly scoring without even knowing they are using predictive algorithms BI Artifact (or even just a dataset) SAP BI (3.x/4.x) Embedded into any application SQL (Or any other application)
  23. 23. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 22 SAP BusinessObjects Cloud
  24. 24. Big Data Architectures
  25. 25. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 24 You Need Both of These…
  26. 26. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 25 A Common Question “We like SAP ERP (and HANA), we like Hadoop, and your BI tools are a standard. But we don’t understand how it’s all going to fit together. Help!”
  27. 27. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 26 “Classic” Enterprise Hadoop Use Cases Semi-structured data loading / processing • First web data, now IoT/documents/images, etc. Offload traditional relational DW • Typically no reduction in existing DW, but new data increasingly tiered Queryable alternative to tape backups • E.g., when upgrade to different ERP system, keep copy of all old data
  28. 28. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 27 A “Modern Data Architecture” Example
  29. 29. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 28 Other Interesting Hadoop Use Cases Fast scale up/down • Game apps company: big fan of Teradata, and found it cheaper to run than Hadoop, but when individual games became a hit, they needed to be able to scale up (and down) fast Avoid “brittle” ETL, push schema creation to the business • Large investment bank had dozens of different CRM setups, thousands of ETL jobs that kept breaking – kept traditional DW, but added data lake -- “it’s all in there – have fun!” Excel on steroids/exploration • Big, one-off decisions • We don’t know what we don’t know Customer-facing “analytics” • Gas bill, etc.
  30. 30. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 29 Sandboxing/Data Extensions
  31. 31. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 30 Not Just a Data Store – A Platform Far more than a batch-driven data store • Many still have an out of date view – it’s now based on Yarn/Spark, etc. • ”Data at Rest and Data in Motion” • But still not for “transactions” any time soon Still maturing, still a lot of work, but has proved enterprise value • In particular, overcame biggest security & auditing concerns – Kerberos integration, encryption, tokenization, Apache Ranger, … • Low capital costs to try things out (but don’t underestimate time/training/expertise needed) Considered the heart of “digital transformation” in some large organizations… • ...At least by the team implementing Hadoop! (but there’s typically a large ”traditional IT” modernization effort going on at the same time)
  32. 32. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 31 Result of All This: Data Complexity For The Foreseeable Future Data Warehouse Hybrid Transaction/ Analytical Processing Hadoop, MongoDB, Spark, etc. Personal Data / BI Where does data arrive? When does it need to move? Where does modeling happen? What can users do themselves? What governance is required? Big Data Architectures got complicated What we would like — consistent, seamless solution Data Feeds
  33. 33. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 32 SAP HANA Vora What’s Inside and What Does It Do? Democratize Data Access Make Precision Decisions Simplify Big Data Ownership SAP HANA Vora is an in-memory query engine that leverages and extends the Apache Spark execution framework to provide enriched interactive analytics on Hadoop. Drill Downs on HDFS Mashup API Enhancements Compiled Queries HANA-Spark Adapter Unified Landscape Open Programming Any Hadoop Clusters
  34. 34. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 33 SAP Predictive Analytics 3.0 & Hadoop Native Spark Modeling Standalone or included in SAP HANA Predictive Factory Integration with cloud & other apps
  35. 35. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 34 SAP HANA DW – Future-proof data management platform
  36. 36. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 35 Future Vision: More “Black Box” Approach
  37. 37. Predictive & Machine Learning
  38. 38. 37© 2017 SAP SE or an SAP affiliate company. All rights reserved. Random… I helped launch Business Objects BusinessMiner in 1996 – 20 yrs ago! “Data Mining for the Masses”
  39. 39. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 38 Retail Predictive Analytics Example SAP BusinessObjects Mobile showing store managers near real-time sales compared to prior day/week
  40. 40. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 39 What Food to Make, When? Knowledge Check Past Sales Check Forecast Check Must Stock Run and Check Range Tool Set 60%/70% Fixed First Production Hot Food Continuous Replenishment All Other Food Monitor for 2nd and 3rd Variable Productions
  41. 41. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 40 What Food to Make, When? (cont.) Trading Patterns Core Range Weather Special Events
  42. 42. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 41 Internal Data External Data Slow and Steady Data Transactional, Changeable Data POS Data Deliveries Store Attributes Store Org Structure Store Placement Store Staff Store Visibility, Signage Competitor Store Attributes Census, ONS Data POI Data GIS Competitor and Cannibalization Footfall Weather Events Real Estate Choosing a New Store Location
  43. 43. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 42 “It is mind-blowing how versatile and nimble our data warehouse is on SAP HANA.” Agile self-service with SAP HANA and SAP Lumira. 9 years of data, structured & unstructured Healthcare Example
  44. 44. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 43 ”Does this 86-year old grandmother really need the same knee as the professional linebacker?” Benchmarking Surgical Procedures
  45. 45. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 44 Benchmarking Surgical Procedures “Using surgical procedure data to help achieve $9.42 million in cost reductions, eliminate or minimize the use of certain surgical products, reduce variation in surgical protocols, establish best practices across surgical departments and ensure quality post-operative results for patients.” Source: http://www.himss.org/sites/himssorg/files/mercy-periop-case-study.pdf
  46. 46. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 45 Predictive Analytics Develop expertise in treating breast cancer and type II diabetes
  47. 47. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 46 Predictive Trends At “peak of inflated expectations” Predictive is a lower priority than data discovery/self-service, data quality, governance, … But higher use of predictive is … predicted The top users of predictive are now BI Experts & Business Analysts – not data scientists Biggest challenges: greater volume & variety of data, operationalizing predictive, usability, skills/understanding
  48. 48. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 47 Situational Awareness What do I need to do right now? Prediction What can I expect to happen? Suggestion What do you recommend? Notification What do I need to know? Perception What’s happening now? Artificial Intelligence-Powered Processes Automation What should I always do? Prevention What can I avoid?
  49. 49. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 48 The Challenge of AI & Humans Working Together “Anything you can do, AI can do better…”
  50. 50. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 49 « Une grande responsabilité est la suite inséparable d’un grand pouvoir » -Voltaire Beware: Ethics Ahead! (“with great power comes great responsibility”)
  51. 51. Organizing For Data
  52. 52. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 51 BICCs Are Dead? Long live ACEs: “Analytic Communities of Excellence”!
  53. 53. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 52 Embrace Shadow IT Don’t fight back — be a co-conspirator … 40% of users are using an equal amount or more of homegrown applications Source: http://sapassets.edgesuite.net/sapcom/docs/2015/09/541ccd61-437c-0010-82c7-eda71af511fa.pdf
  54. 54. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 53 Updating the Traditional BICC to Include Community A Business Intelligence Competency Center (BICC) is a cross-functional organizational team that has defined tasks, responsibilities, roles, and skills for supporting and promoting the effective use of Business Intelligence* across an organization * I.e., Analytics, Big Data, Data Science, etc.
  55. 55. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 54 It’s About Culture Change First and Foremost From Power to Empower From Collection to Connection From Control to Trust New BICCs are about providing good governance and encouraging best practice rather than providing reports and analytics
  56. 56. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 55 It’s All About The Relationship! It’s nearly impossible to spend too much time understanding the real business needs. It’s not something that can only be done from head office.
  57. 57. Wrap-up
  58. 58. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 57 Where to Find More Information My personal blog: timoelliott.com SAP BICC Playlist on YouTube: Link SAP BI Self Assessment : www.sap.com/bistrategy SAP BI Strategy Playlist on YouTube: Link BI News: www.sap.com/BINews SAP Community Network: https://blogs.sap.com/?s=bi+strategy
  59. 59. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 58 7 Key Points to Take Home Business Intelligence and Analytics is more strategic than ever Analytics now creates processes instead of just being generated by them New trends in analytics means new approaches are required Companies should invest in more self-service analytics for business users Companies should invest in more flexible information architectures Start preparing now for the artificial intelligence future The number one priority is always the same: optimize your organization
  60. 60. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 59 Thank You! Timo Elliott VP, Global innovation Evangelist Timo.Elliott@sap.com @timoelliott
  61. 61. © 2017 SAP SE or an SAP affiliate company. All rights reserved. 60 © 2017 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward- looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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