Getting Started with Big Data for Business Managers
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Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before. Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to: Approach Big Data and associated business challenges -- Identify what types of new insights can be revealed by Big Data -- Staff for this undertaking and implement the technology necessary to be successful -- Take the first steps toward getting started with Big Data on Hadoop
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Getting Started with Big Data for Business Managers
- 1. Fastest Time to New Insights
- 2. How to Get Started on Hadoop for Business Managers" © 2014 Datameer, Inc. All rights reserved.
- 3. Audio" ▪ Audio will be streamed over the web for today’s webcast ▪ Make sure your computer speakers are turned up and the volume is adjusted ▪ If you are having trouble connecting, please send the host a chat message through the chat window
- 4. Tony Baer @Ovum Principal Analyst Tony Baer leads Ovum’s Big Data research area. Over his 25 years in the industry, he has studied issues of data integration, software and data architecture, middleware, and application development. Having tracked the emergence of BI and data warehousing back in the 1990s, Baer sees similar parallels emerging in the world of Big Data today. His coverage focuses on how Big Data must become a first-class citizen in the data center, IT organization, and the business. @TonyBaer About Our Speaker"
- 5. Azita Martin @datameer CMO Azita Martin is Chief Marketing Officer at Datameer with extensive marketing leadership experience at high-growth start-ups and category-creating public companies like Salesforce and Siebel. Azita has global responsibility for scaling all aspects of Datameer’s product and corporate marketing, including defining go-to-market strategy, driving thought leadership, and increasing brand awareness and customer acquisition. Prior to Datameer, Azita built and led marketing teams for both fast-growing start-ups and major public companies, including Get Satisfaction, Moxie Software, LiveOps, Salesforce, Siebel and SGI. #datameer @datameer About Our Speaker"
- 6. www.ovum.com How to get started on Hadoop for Business Managers" Tony Baer, Principal Analyst tony.baer@ovum.com © Copyright Ovum 2014. All rights reserved.
- 7. Agenda" § Why Big Data & Hadoop? § Making the business case § When to use Hadoop § How to make it happen? § What’s the End Game © Copyright Ovum 2014. All rights reserved. 7
- 8. Making the business case" § Address documented business challenges § Choose high-impact problem & solution that delivers actionable results § Determine whether the solution absolutely requires Big Data This is not a data science exercise! © Copyright Ovum 2014. All rights reserved. 8
- 9. When to use Hadoop" New data sources Schema on read New analytic approaches beyond SQL Inexpensive compute cycles • Structured data • Text • Social networks • Mobile data • Machine data • Rich media • Let the data drive you to the problem or insight • Path analytics • Cluster analytics • Graph analytics • Streaming analytics • Move compute loads from DW to Hadoop © Copyright Ovum 2014. All rights reserved. 9
- 10. Use case – general themes" New perspectives to addressing existing problems New data generates new revenue streams © Copyright Ovum 2014. All rights reserved. 10
- 11. Agenda" § Why Big Data & Hadoop? § How to make it happen? § Making Big Data & Hadoop first class citizens § Getting the right people § Walk before you run § What’s the end game? © Copyright Ovum 2014. All rights reserved. 11
- 12. Big data must become a 1st class citizen in the enterprise" No separate Big Data silos! “Big Data cannot exist on its own island” • IT: Map to existing staff & skills • Data Center: Map to existing policies & rules • The Business: Map to existing business challenges © Copyright Ovum 2014. All rights reserved. 12
- 13. Get the right people" Technical Analytics team Business Platform Specialists/ Cluster architect Java/Python Developers Data Architect DBA Domain/subject matter experts Statistical experts Mgmt. champion Evangelist Business owner/sponsor (Supplemented by applications or tools) Data steward/ Data curator Some will play dual roles © Copyright Ovum 2014. All rights reserved. 13
- 14. Do you really need a data scientist?" § Creative, investigative mind § Statistical programming skills § Domain/industry awareness § A “nose” for data sets § Some database skills/awareness § Ability to communicate & evangelize Applications & tools may embed data science! Look for a team, not a rock star! © Copyright Ovum 2014. All rights reserved. 14
- 15. Getting there with the army you have" § Hadoop platform training is critical § Big Data analytics training is critical § Greater variety of data § Different analytics methods (beyond traditional SQL) § Cloud § Reduces technology skills requirements, depending on type of cloud service § Different architecture than on premises cluster deployments § Requires retraining if cloud used for jumpstart to on-premises © Copyright Ovum 2014. All rights reserved. 15
- 16. Walk before you run" Low Road High Road Higher Road Big Data on Hadoop use cases Clickstream/log analytics DW optimization Customer optimization Risk Management Anti-fraud Operational Efficiency Create new business services Business transformation © Copyright Ovum 2014. All rights reserved. 16
- 17. Agenda" § Why Big Data & Hadoop? § How to make it happen? § What’s the end game? © Copyright Ovum 2014. All rights reserved. 17
- 18. Set realistic goals" • “Hard” ROI numbers from • DW optimization • Operational efficiency • “Soft numbers” from • Benefits that are directly attributable to using Big Data analytics • New business opportunities from Big Data • New capabilities for sensing & responding Challenges are similar to any analytics project © Copyright Ovum 2014. All rights reserved. 18
- 19. Embrace & Extend" IT organization Data Center Enterprise Embrace Extend Existing SQL, Java, other language skills Mgmt for bigger, more variable data sets & use new analytic methods beyond SQL Existing data stewardship, resource mgmt, security, perf mgmt practices Practices to support different workload characteristics & active archiving Existing competitive problems Problem solving by using new data types & analytic methods to boost understanding How to make Hadoop & Big Data 1st class citizens © Copyright Ovum 2014. All rights reserved. 19
- 20. Summary: The elements for getting started with Hadoop" 1. Problem 2. People 3. Training 4. Technol. 5. Document results • Start simple • Choose high-impact problem & solution that delivers actionable results • Determine whether the solution absolutely requires Big Data • Extend DW teams with greater roles for developers, statistical analysts • Don’t expect to find a “data scientist.” • Management champion is critical • Get data center practitioners up to speed with Hadoop platform • Develop understanding of how to work with new data types + analytics methods beyond traditional SQL • Start small. • If cloud used for jumpstart, architectural migration will be required for moving on premises • Unless used for operational efficiency or DW optimization, benefits will rely on “soft numbers” • Identify business benefits that are directly attributable to using Big Data analytics © Copyright Ovum 2014. All rights reserved. 20
- 21. www.ovum.com Thank you" Tony Baer Ovum (646) 546-5330 @TonyBaer tony.baer@ovum.com © Copyright Ovum 2014. All rights reserved.
- 22. Big Data Use Cases"
- 23. The State of Big Data Market"
- 24. Working with 200+ Customers"
- 25. Combining More Data for New Insights" Social Media Mobile Ads Web Logs CRM Product Logs Transaction Call Center Are keywords related to customer segments? Which campaign combinations accelerate conversion? Which product features drive adoption? Which features do users struggle with? What behavior signals churn? Where do hack attempts originate? How do we determine which cell towers to upgrade? Can we predict production failure?
- 26. Decrease Customer Acquisition Cost 30% Lower Customer Acquisition Costs © 2014 Datameer, Inc. All rights reserved.
- 27. Internet of Things" Connected Home Energy Consumption Data Reduced False Alarms User Behavior Improved Customer Experience
- 28. New Data-Driven Service © 2014 Datameer, Inc. All rights reserved. Server Logs Product Catalog CRM Predictive Maintenance New Revenue Stream
- 30. For the webinar: http://bit.ly/1DcEdeu"
Notes de l'éditeur
- But as we said up front, appealing to the enterprise changes the script for Hadoop – When Hadoop was invented by Yahoo, Facebook, and others, it was for solving highly specialized problems. And as a new technology that they open sourced to the Apache Foundation, the practitioners were a small, select, highly elite group. Hadoop clusters were their own specialized environments set apart from the operational systems, and run by their own dedicated teams. Hadoop emerged as an island of its own. That model will not be sustainable in the enterprise! That’s why we at Ovum firmly believe that for Big Data – and Hadoop – to gain traction with the enterprise, it must get off the island. It must become a first class citizen in the enterprise.
- Updated Nov. 1 (Non-NDA)
- This use case study is a major credit card company (AMEX) that spend millions of dollars in both digital and non digital advertisement. This credit card company used Datameer to correlate purchase history, profile information and behavior of their customers on social media sites. For example, they collected customer profile data on their platinum customers. Then they’d correlate this data with transaction history and things the customers “liked” on Facebook. From those findings the company targeted their advertisement on TV channels their high value customers like to watch (food network) and offered promotions at an organic foods store, where their customers frequently shop at. As a result of using Datameer, they were able to decrease their customer acquisition costs by 30% . For a major credit card company this represents millions of dollars in annual savings. Additional detail: This financial services company gathers data from Facebook and generate profiles of what people likes. They gather and correlate this data to check for patterns. With these patterns, they can set their marketing strategy to target people of certain profiles with certain advertisements. As a result of using Datameer, they were able to decrease their customer acquisition costs by 30% and the time it took to create these reports down to 1 day from 16 weeks. The average customer acquisition cost is about $60 per customer for credit cards. For first 3 months of 2013, new cards increased 3% to 42.5M for this company. So that is 1.275M new customers. If we reduced customer acquisition costs by 30%, that is $18 per customer, or $23M. http://about.americanexpress.com/news/pr/2012/earnings-preannouncement.aspx http://www.nbcnews.com/id/50496724/ns/business-small_business/t/how-much-did-new-customer-cost-you/#.UYPBwSuAe24 http://www.rtefs.com/images/Forrestor_20Financial_20Services_20Firms_20Open_20Up_20About_20Customer_20Acquisition_20Costs.pdf http://seekingalpha.com/article/1351731-american-express-gains-on-consumer-spending-and-international-growth
- Vivint — Serves 800,000+ homes, Vivint’s touchscreen panel (their hub) creates a streamlined network that connects all of the home’s smart systems (security, HVAC, lighting, small appliances, video, etc). — Uses Datameer to: — integrate and analyze not just row data but also streaming data, which is a key component to their smart home analytics solution. — to parse, join and sessionize various complex data streams to determine occupancy and vacancy patterns in an effort to reduce the number of false alarms and improve the overall efficiency of their devices. — sessionize data based on events and looking at log windows before and after a selected event. —Ensures a better customer experience by improving the understanding of the customers behavioral patters. — Time savings due to rapid implementation of the analytical solution for sensor data — Cost savings due to minimal investment in skilled Hadoop resources — Can compare to other to others homes and learn behavior — Based on outlier detection - e.g. unusual movements within house and detect thieves, improve products from this insights)
- NetApp This enterprise hardware company was generating and collecting data that was doubling every 15 months. In addition to the rapidly growing data volumes, there were hundreds of different semi-structured and unstructured log formats. Before Datameer, analysts were forced to write ad hoc Perl code to parse a subset of the log files and store data locally. By using Datameer, the company was able to derive valuable insights that helped virtually every group -- Support, Development, Marketing, Services. They also use their data-driven intelligence to offer “Premium Support” as a new revenue service offering. For example, Support was able to send out a replacement part before the component actually failed. Sales was able to look at usage patters to improve forecasting and renewal negotiations. Additional detail: http://documentation.datameer.com/documentation/display/SALES/Device+Analytics
- QUESTIONS?
- Well, that ends our webcast today. Thank you to Tony and Azita for talking with us today as we wrap up 2014. A reminder that we have other webcast recordings and resources on our website at http://www.datameer.com/learn/index.html We encourage you to visit our website to learn more, request a trial download at datameer.com or follow us on twitter @datameer. This concludes the audio portion of our webcast today. Thank you and Happy Holidays. See you in 2015.