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The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
The Big Deal About Big Data
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The Big Deal About Big Data

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Paul Zikopoulos, Vice President, IBM IM Technical Sales and Big Data reveals how to manage the vast array of available social, cloud and mobile data points to drive informed business-level …

Paul Zikopoulos, Vice President, IBM IM Technical Sales and Big Data reveals how to manage the vast array of available social, cloud and mobile data points to drive informed business-level decisions.

Delivered at the #SmarterBiz Summit in Vancouver, BC, June 11, 2014.

For more information contact:
Paulz_ibm[at]msn.com
twitter.com/@BigData_PaulZ

Published in: Technology, Business
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  • 1. 1 #SmarterBiz The Deal The Brains of a Smarter Planet About Paul Zikopoulos, BA, MBA Vice President, IBM IM Technical Sales and Big Data email: paulz_ibm@msn.com Twitter: @BigData_paulz
  • 2. 2 #SmarterBiz Legal Disclaimer  © IBM Corporation 2014. All Rights Reserved.  The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM’s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software.  References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results.  If the text contains performance statistics or references to benchmarks, insert the following language; otherwise delete: Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.  If the text includes any customer examples, please confirm we have prior written approval from such customer and insert the following language; otherwise delete: All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer.  Please review text for proper trademark attribution of IBM products. At first use, each product name must be the full name and include appropriate trademark symbols (e.g., IBM Lotus® Sametime® Unyte™). Subsequent references can drop “IBM” but should include the proper branding (e.g., Lotus Sametime Gateway, or WebSphere Application Server). Please refer to http://www.ibm.com/legal/copytrade.shtml for guidance on which trademarks require the ® or ™ symbol. Do not use abbreviations for IBM product names in your presentation. All product names must be used as adjectives rather than nouns. Please list all of the trademarks that you use in your presentation as follows; delete any not included in your presentation. IBM, the IBM logo, Lotus, Lotus Notes, Notes, Domino, Quickr, Sametime, WebSphere, UC2, PartnerWorld and Lotusphere are trademarks of International Business Machines Corporation in the United States, other countries, or both. Unyte is a trademark of WebDialogs, Inc., in the United States, other countries, or both.  If you reference Adobe® in the text, please mark the first use and include the following; otherwise delete: Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries.  If you reference Java™ in the text, please mark the first use and include the following; otherwise delete: Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both.  If you reference Microsoft® and/or Windows® in the text, please mark the first use and include the following, as applicable; otherwise delete: Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both.  If you reference Intel® and/or any of the following Intel products in the text, please mark the first use and include those that you use as follows; otherwise delete: Intel, Intel Centrino, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries.  If you reference UNIX® in the text, please mark the first use and include the following; otherwise delete: UNIX is a registered trademark of The Open Group in the United States and other countries.  If you reference Linux® in your presentation, please mark the first use and include the following; otherwise delete: Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both. Other company, product, or service names may be trademarks or service marks of others.  If the text/graphics include screenshots, no actual IBM employee names may be used (even your own), if your screenshots include fictitious company names (e.g., Renovations, Zeta Bank, Acme) please update and insert the following; otherwise delete: All references to [insert fictitious company name] refer to a fictitious company and are used for illustration purposes only.
  • 3. Paul C. Zikopoulos, B.A., M.B.A., is the Vice President of Technical Professionals for IBM’s Information Management division and additionally leads the World Wide Competitive Database and Big Data teams. Paul is an award winning writer and speaker with more than 20 years of experience in Information Management and is seen as a global expert in Big Data and Analytic technologies. Independent groups often recognize Paul as a thought leader with nominations to SAP’s “Top 50 Big Data Twitter Influencers”, Big Data Republic’s “Most Influential”, Onalytica’s “Top 100”, and AnalyticsWeek “Thought Leader in Big Data and Analytics” lists. Technopedia listed him a “Big Data Expert to Follow” and he was consulted on the topic of Big Data by the popular TV show “60 Minutes”. Paul has written more than 350 magazine articles and 18 books, some of which include “Hadoop for Dummies”, “Harness the Power of Big Data”, “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, “New Dynamic In-Memory Analytics for the Era of Big Data: DB2 10.5”, “DB2 pureScale: Risk Free Agile Scaling”, “DB2 Certification for Dummies”, “DB2 for Dummies”, and more. In his spare time, he enjoys all sorts of sporting activities, including running with his dog Chachi, swimming, and overall fitness training (he no longer worries about avoiding punches in his MMA training as an eventual understanding that he became too slow for full contact forced him into retirement). Ultimately, Paul is trying to figure out the world according to Chloë—his daughter. You can reach him at paulz_ibm@msn.com.
  • 4. IBM IBV/MIT Sloan Management Review Study 2011 Copyright Massachusetts Institute of Technology 2011 Studies show that organizations competing on analytics outperform their peers 4
  • 5. substantially outperform Studies show that organizations competing on analytics outperform their peers 1.6x Revenue Growth 2.0x EBITDA Growth2.5x Stock Price Appreciation IBM IBV/MIT Sloan Management Review Study 2011 Copyright Massachusetts Institute of Technology 2011 IQbusiness initiative BUSINESS IMPERATIVE
  • 6. Key Business Imperatives for Insight Create new business models Optimize operations and reduce fraud Attract, grow, retain customers Transform financial processes Manage risk Improve IT economics Big Data & Analytics Big Data & Analytics
  • 7. 7 There is a perfect storm where a vast constellation of applications meets a massive, ubiquitous, and unlimited network of endpoints Social Mobile Cloud
  • 8. 8
  • 9. 9 Automatic Spatially and Temporally Enriched Data
  • 10. “Pinning” the Way to Smarter Commerce • Advertisements • Promotions • Campaigns • Planning • Preferred Styles • Designs • Products • Interests • Pins / Re-pins • Likes / Dislikes • Tweets • Favorites Photo Albums and Pinboards Style Kitchen Gallery Dream Home Wedding • Photo Semantic Analysis • User Segmentation Computer Consumer Models Products Brands Logos Styles Designs Retailers, Marketers and PlannersWe're now moving from text-centric expression to visual-centric expressions
  • 11. While data collection has become 24x7. Decision making IS NOT. Heart Beats: 1 value/hour (7,799 lost) Breathing: 1 value/hour (2,099 lost) Blood Oxygen Levels: 1 value/hour (3,599 lost) ECG: 1000 readings/sec (86,400,000 lost)
  • 12. Volume Variety Velocity Veracity Data at Scale Terabytes to petabytes of data Data in Many Forms Structured, unstructured, text, multimedia Data in Motion Analysis of streaming data to enable decisions within fractions of a second. Data Uncertainty Managing the reliability and predictability of inherently imprecise data types. Velocity IS the game changer: It’s NOT just how fast data is produced or changed, BUT the speed at which it must be received, understood, and processed.
  • 13. There’s a shift in the CIO’s office, from mostly spending money to save money, to spending money to make money. By 2017, CMOs will spend more on IT than CIOs.IDC “GM IT Goal: Boost IT’s measurable payoff by 10x, handle 2x the projects, and get them done 3x faster. The DW incorporates both cutting edge Hadoop technology as well as more traditional MPP technologies historically used for data warehousing. For Hadoop, GM has deployed IBM’s BigInsights platform [the IBM non-forked Hadoop distribution – 1.1PB cluster – 55 of 200 data marts moved].”
  • 14. Realize It. Invest in a Big Data & Analytics platform. All Data Harness All Data & All Paradigms Information Governance Zone Information Ingestion & Operational Information Zone Real-time Analytics Zone Exploration, Landing & Archive Zone Enterprise Warehouse, Data Mart & Analytic Appliance Zone
  • 15. All Data New/ Enhanced Applications IBM Big Data & Analytics Platform Systems, Security, Storage IBM Big Data & Analytics Infrastructure Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Information Governance Zone Real-time Analytics Zone Exploration, Landing & Archive Zone Information Ingestion & Operational Information Zone Enterprise Warehouse, Data Mart & Analytic Appliance Zone Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Realize It. Invest in a Big Data & Analytics platform.
  • 16. All Data New/ Enhanced Applications IBM Big Data & Analytics Platform Systems, Security, Storage IBM Big Data & Analytics Infrastructure Information Governance Zone Real-time Analytics Zone Exploration, Landing & Archive Zone Information Ingestion & Operational Information Zone Enterprise Warehouse, Data Mart & Analytic Appliance Zone Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Realize It. Invest in a Big Data & Analytics platform.
  • 17. All Data New/ Enhanced Applications IBM Big Data & Analytics Platform Systems, Security, Storage IBM Big Data & Analytics Infrastructure Information Governance Zone Real-time Analytics Zone Exploration, Landing & Archive Zone Information Ingestion & Operational Information Zone Enterprise Warehouse, Data Mart & Analytic Appliance Zone Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Realize It. Invest in a Big Data & Analytics platform.
  • 18. IBM Big Data & Analytics Platform Systems, Security, Storage IBM Big Data & Analytics Infrastructure All Data Reporting, Analysis, Content Analytics Cognitive Exploration & Discovery Decision Management Predictive Analytics & Modeling Information Governance Zone New/ Enhanced Applications Real-time Analytics Zone Exploration, Landing & Archive Zone Information Ingestion & Operational Information Zone Enterprise Warehouse, Data Mart & Analytic Appliance Zone Realize It. Invest in a Big Data & Analytics platform.
  • 19. © 2014 IBM Corporation
  • 20. © 2013 IBM Corporation21 #ENDOFCOFEEBREAKANLAYTICS
  • 21. © 2013 IBM Corporation22
  • 22. #ENDOFCOFEEBREAKANLAYTICS
  • 23. What to Remember About Cloudant…  Operational JSON “document” database  Spreads data across data centers & devices for scale and HA; allowing for data to sync between datacenters and devices  Fully managed distributed NoSQL Database as a Service (DBaaS) - 24/7 – no other competitor offers this  Ideal for mobile apps that require: – Rapid deployment, Time to Value – Massive, elastic scalability – High availability – Geo-location services – Full-text search – Support for occasionally connected users Delivered as a cloud service, Cloudant eliminates complexity & enables developers of fast-growing web and mobile apps to focus on developing their applications, without the need to manage database infrastructure and growth
  • 24. 25 Where there is data, there is potential for breaches and unauthorized access.
  • 25. 26 Typical regulatory compliance uses the least possible work to comply approach. Create regulatory dividends. Repurpose the same data used for regulatory compliance for other uses.
  • 26. © 2014 IBM Corporation27
  • 27. Complete Integration of Data Privacy and Security De-indentify sensitive data on demand Deploy centralized controls for real- time monitoring Encrypt data with negligible performance impact Remove sensitive data from documents Automate detection of sensitive data  Mask with pre- build functions or customize  Mask consistently across systems  Policy-based controls to detect unauthorized activity  Vulnerability assessment & change auditing  Encrypt files and structured data  Unify policy and key management for central administration  Increase efficiency via automation and reduce cost of manual redaction  Control the data viewed by each user  Classify sensitive data types  Discovery hidden data relationships Optim Discovery & Guardium Optim Data Masking & Data Privacy for Hadoop Guardium Activity Monitoring Guardium Data Encryption Guardium Data Redaction Discover & classify sensitive data Mask structured & unstructured data Monitor database & hadoop activity to assess vulnerabilities Encrypt structured and unstructured data Redact data in documents & forms
  • 28. 29 Demand that complexity is placed behind the glass and move decisions from the elite few to the empowered many.
  • 29. Opt in | out promotions Public Calendar (Gift giving season) Personal & business callsEmail Known for being indecisive, offer acceptance history Professional Architect & Small Business Owner Single mobile account for both personal and business use Only somewhat tech-savvy Social apps Web browsing Apps Married with no children When the Unaffordable becomes Affordable… the Impossible Becomes the Possible 31  Increasing abundance of automated consumer-facing service opportunities gives us the data to know more about an entity than ever before – BUT ironically, we know less (think local banking branch) Storage on Device Service Suspended in Past? Lost device? Payment? Usage Classification High LD? Evenings? Roam? Threshold warnings and preferences Lifecycle.. -moving -new TV -+++
  • 30. Age + Income + GeographyPreferred Product CategoriesPreferred Channel Participation in Loyalty Program Use of In-House Credit Card Use of Service Programs Return / Exchange BehaviorBreadth of Categories Shopped Length of Time as Customer Recency + Frequency + Value Response to Media Time until Repurchase in Key Categories Annual Spend Level Annual Transactions Econometric: Real-estate & Unemployment Service Profile: Current Handset = RealPhone Next Upgrade = March 2013 Data Plan = Unlimited Domestic Features = Basic Customer Insights: Customer Seg = SME Customer Value = High Influencer Score = Moderate Churn Risk = Mod/High Loyalty Member = No Usage Data Summary (3 mos): 80% of calls out-of-network Made 3 calls to a competitor call center 5 streaming video events per day Heavily uses smartphone app Data roamed in Japan 6 times Billing Profile: Average Bill = $200 per mo Pays by autopay Customer Profile: Gender = Male Marital = Married Children = No Income = Upper/Mid Tier Language = English Preference: Movies & video Sports International Travel Social Media (Facebook) The Death of the Average: Client D.N.A
  • 31. Likelihood to Purchase: Churn Risk: Product Education: 65 Audience and ID: Bill Middleton, 1234567 Products of Interest: NanoPhone 65 60 33 Digital Body Language on Your Premise… Moves You From Transactions to Interactions…
  • 32. Information Integration & Governance Systems Security On premise, Cloud, As a service Storage IBM Watson Foundations IBM Big Data & Analytics Infrastructure New/Enhanced Applications All Data What action should I take? Decision management Cognitive Fabric Landing, Exploration and Archive data zone EDW and data mart zone Operational data zone Real-time Data Processing & Analytics What is happening? Discovery and exploration Why did it happen? Reporting and analysis What could happen? Predictive analytics and modeling Deep Analytics data zone Cognitive is the Analytics Engine of the Future
  • 33. © 2014 IBM Corporation35 10 Tech Companies Proving Innovation Isn't Dead
  • 34. How to Move Strategically and Transform Your Business 36 Invest in a big data & analytics platform Be proactive about privacy, security and governance Imagine It. Realize It. Trust It. Build a culture that infuses analytics everywhere To trust the insights you have to trust the facts Privacy and security to protect the data Enable risk-aware decisions Build towards a platform for all data and analytics Analyze data in motion Cultivate new partnerships & roles Start with your people Infuse analytics into key business processes Deploy the full range of analytics
  • 35. IBM delivers a governable and consumable Big Data and Analytics platform that’s steeped in analytics for data in-motion and data at-rest called Watson Foundations
  • 36. 38 THINK @BigData_paulz

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