Analytics Keynote. @timoelliott. 73% of keynote speakers know that the best way to get an audience s attention is to present a useless factoid

Analytics Keynote Timo Elliott, Innovation Evangelist, SAP 73% of keynote speakers know that the best way to get an audience s attention is to present a useless factoid @timoelliott

Questions! QuestionRater.com/ukisug13

YASOHMDTIITW* Bit, Byte, Kilobyte, Megabyte, Gigabyte, Terabyte, Petabyte, Exabyte, Zettabyte, Yottabyte. Brontobyte or Hellabyte * Yet Another Slide On How Much Data There Is In The World 2013 SAP AG. All rights reserved. 8

Analytic maturity Prescriptive: How can we make it happen? Predictive: What will happen? Diagnostic: Why did it happen? Descriptive: What happened? Hindsight Insight Foresight

Unlocking the value of Dark Data Use Analytics 10% Today Missing new insights Not utilizing all the information out there IT is not agile enough and the business wants to get involved Ability to manage and consume all data is getting harder 75% Need Analytics by 2020 = Not leveraging the power of collective insight On average, companies only use 12% of their data. Nucleus Research, Gartner, Fortune Magazine Forrester Research

Big Data Transactions, Interactions, Observations Map Reduce / Hadoop / NoSQL Data Scientists

Google Spanner NoSQL is out, NewSQL is in Data is stored in schematized semi-relational tables Spanner supports general-purpose transactions, and provides a SQL-based query language 16

Imagine if Your Apps Looked Like a DW to BI Tools Customer Service Risk Management Team Finance and Operations Account Administration Executive Management SAP HANA SAP HANA Live (Virtual Data Model) Customers Inventory Channel Products Suppliers Pricing Accounting Planning Forecasting 2013 SAP AG. All rights reserved. 17

Real-Time Data Processing Platform Fewer Layers Same Core Data Simpler Landscape Traditional Databases & Solutions Specialty Analytic Databases Transact Analyze SAP RTDP Analyze & Transact in Real-time

Edward James Snowden Edward Joseph Snowden

Information Steward 4.2 Data Quality Advisor Assess Recommend Tune Redundancy Profiling Data Profiling Address Profiling Content Type Validate address data Discovery Dependency Profiling Identify attribute-level connections in data. (Normalization rules practice) Redundancy Profiling Identify degree of duplication Uniqueness Profiling Validation Rules Cleansing Rules Match Rules View Before / After Results Fine Tune With What-If Analysis Publish Rules Drill down to duplicate and non-duplicate records Identify non-unique data 2013 SAP AG. All rights reserved. Public 23

But It s Not About Technology The stone age was marked by man's clever use of crude tools; the information age, to date, has been marked by man's crude use of clever tools. 2013 SAP AG. All rights reserved. 25

80% of CEOs think they deliver a superior customer experience -- but only 8% of customers agree. Source: The New Yorker

Engage Your Fans Data-driven decision making played a huge role in creating a second term for the 44th President. In politics, the era of big data has arrived. - Time Magazine 2013 SAP AG. All rights reserved. 36

By 2017, there will be close to $11 Billion in revenue from 35-million homes using home automation platforms across the globe. Source: GIGAom, 2013

Wearable devices have grown by 2x month over month since October 2012. Source: Mary Meeker s Internet Trends, 2013 Photo: Intel Free Press

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Do Everything Faster "CFOs can press a button at any time and see what the month end is going to look like Ignacio Garcia 25x faster in data extraction, processing and loading times ~$645k in annual labor cost savings 2 2 days time saved in monthly financial closing 2013 SAP AG. All rights reserved. 65

Create a Commerce Eco-System A platform for real-time interactivity between consumers, STM and partners Receive information, discounts & Special offers Partners SAP Precision Retailing (On-Demand, Multitenant, High Performance, Scalable) BI CRM Merchants Outings Transports Interact with consumer in the field Run mobile marketing campaigns based on consumer profile and location Interact with consumer in real-time anywhere, anytime. Design & run mobile marketing campaigns based on consumer profile and location Analyze consumer behavior in the field 2013 SAP AG. All rights reserved. 68

A Personalized, Multi-Vendor Customer Experience Select Rate & Order Deliver & Learn In the store(s) nearby Valid at this time For my profile (segments) Eligible offers The rating is based on the learning engine and on the characteristics of the shopping context, the consumer preferences, and the frequency of presentation. Top x What time is it? Where am I? What is my personal profile? What is my CRM profile? What are my preferences? Where am I? What is my personal profile? What is my CRM profile? 2013 SAP AG. All rights reserved. 69

Business Networks = information Networks Ariba Network More than 1M suppliers in more than 190 countries around the world Transact with suppliers the Network handles over $460 billion per year in commerce Procurement Sales Finance Logistics Supply Chain Sustainability Compliance Reduce supply costs customers save a combined total of $82M daily Buyers Partners Suppliers 2013 SAP AG. All rights reserved. 70

Unleashing the Power of Collective Insight ENGAGE VISUALIZE PREDICT Imagine the potential Predict demand or supply across your entire Supply Chain immediately Provide exactly the right offers and service levels to every customer Understand what your customers & potential customers are saying about you, right now Instantly predict market trends and customer needs and innovate new product and services quicker

Analytics solutions from SAP Cloud Mobile Agile Visualization Advanced Analytics Big Data Enterprise Business Intelligence Collaboration

Thank you Timo Elliott, SAP Email: timo.elliott@sap.com Twitter: @timoelliott Blog: timoelliott.com

2013 SAP AG. 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 AG. The information contained herein may be changed without prior notice. Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation or warranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Group 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. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries. Please see http://www.sap.com/corporate-en/legal/copyright/index.epx#trademark for additional trademark information and notices. 2013 SAP AG. All rights reserved. Public 81

Network of Truth Analogy Encyclopedias Wikipedia Local experts Encyclopedia Britannica Encarta

Business Intelligence Timeline 2000-2005 2005-2010 2011 No BI strategy One truth Future vision No real BI strategy IT left to prioritize Multiple versions of the truth VELUX Performance model Standard reporting One truth Anchoring in finance Extend reporting to more users Redefine our own role More end user flexibility VELUX deployed our first Global BI solution around 2000 together with the first SAP implementations 84

A change in user profiles and patterns Over a period of 7 years we have seen several shifts in our BI user group in VELUX The shifts seem to happen with shorter and shorter intervals System Expert Favored Excel as front end Could live with poor performance Primarily used data from SAP 2005 The controller 2010 The analyst General analyst Wanted to use web reports as well Interested in data from several sources Demanded better performance Expecting BI self service Want s information on mobile devices Not scared of technology, uses the right tool for the job 2012 You and me 85

Advanced Profiling Redundancy Profiling Address Profiling Validate address data Dependency Profiling Identify attribute-level connections in data. (Normalization rules practice) Redundancy Profiling Identify degree of duplication Uniqueness Profiling Identify non-unique data Drill down to duplicate and non-duplicate records 2013 SAP AG. All rights reserved. Public 94

Visualization of Data Quality High-level balanced Data Quality Scorecard Latest quality score Data quality score metrics Quality trend over time / run Key Quality Dimensions (KPI for data), customizable 2013 SAP AG. All rights reserved. Public 95

Business Value Analysis Data Quality Financial Impact Calculator Connect financial ROI to data quality and information governance initiatives Understand and demonstrate how bad data effects business bottom line Identify potential savings using what-if analysis of quality level and costs See drivers and metrics of financial impact calculation per failure 2013 SAP AG. All rights reserved. Public 96