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2014 Edison CEO Summit - Day 1 Waves Concurrent Session
Phil Simon presented on the topic of big data at a conference. He began by introducing himself and defining big data as large volumes of diverse data that is mostly external to companies and unstructured. He explained the 3Vs of big data - volume, velocity and variety. Simon discussed how companies are using big data to determine why customers buy certain products and how to improve campaigns. He provided examples of how insurance, healthcare and financial companies utilize big data. Simon concluded by offering tips for companies getting started with big data and emphasizing that embracing data discovery and thinking incrementally are important approaches.
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2014 Edison CEO Summit - Day 1 Waves Concurrent Session
- 1.
- 2. Concurrent Session: 1:15– 2:15pm What’s Your Big Data Credo? Phil Simon, author of The Visual Organization: Data Visualization, Big Data and the Quest for Better Decisions; and Too Big Too Ignore: The Business Case for Big Data
- 3. Edison Ventures Phil Simon AtlanticCity, NJ June 18, 2004
- 4. @philsimon 4 • Award-winningauthor of six books, most recently The Visual Organization • Speaker, consultant, and technology authority
- 5. • Introduction • WhoI am • What is Big Data and why does it matter? @philsimon, 5
- 6. • What arecompanies currently doing with Big Data and what can we learn from them? • Tips on getting started • Discussion • Big Data: Moving from buzzword to practice @philsimon, 6
- 7.
- 8. • The 3v’s • Mostly external to the enterprise • Mostly unstructured data Edison Ventures - @philsimon, 8
- 9. • Structured • Semi-Structured •Unstructured • Metadata Edison Ventures - @philsimon, 9
- 10. • Increasingly generatedby machines, not people • Inherently “unmanageable” in the traditional sense • Doesn’t play nice with SQL Edison Ventures - @philsimon, 10
- 11. • The eraof Big Data does not mean that Small Data has become irrelevant • Rather, Big Data extends the power of Small Data Edison Ventures - @philsimon, 11
- 12. • Determine whythings happen: • Why do customers buy what they buy? • Why are some campaigns and promotions are effective than others? Edison Ventures - @philsimon, 12
- 13. • New datasources create new opportunities and potential value • We can ask better questions of our data Edison Ventures - @philsimon, 13
- 14. • Different statisticalpackages (R) • Hadoop • NoSQL/NewSQL • Sentiment Analysis • Text Analytics/Text Mining Edison Ventures - @philsimon, 14
- 15. • Natural Language Processing •A/B Testing • Predictive Analytics • Data Visualization Edison Ventures - @philsimon, 15 Source: The Visual Thesaurus
- 16. Edison Ventures -@philsimon, 16
- 17. @philsi mon 17 • Roughly48M customers • Nearly $28B market cap • Responsible for 34% of all US weeknight Internet traffic Data as of June 9, 2014 Edison Ventures - @philsimon, 17
- 18. • Data shouldbe accessible, easy to discover, and easy to process for everyone. Source: Netflix - tinyurl.com/tvo-netflix Edison Ventures - @philsimon, 18
- 19. @philsi mon 19 • Thelonger you take to find the data, the less valuable it becomes. Edison Ventures - @philsimon, 19
- 20. @philsi mon 20 • Whethera dataset is large or small, being able to visualize it makes it easier to explain. Edison Ventures - @philsimon, 20
- 21. @philsi mon 21 • Whatthey watch • When they watch • The device on which they’re watching • When they pause and/or resume watching Edison Ventures - @philsimon, 21
- 22. @philsimon, 22Edison Ventures- @philsimon, 22
- 23. • Insurance • Healthcare •Financial Services Edison Ventures philsimon, 23
- 24. • Most carinsurance companies base rates on simple demographic variables • Progressive Pay as you Drive program collects real-time driver data Edison Ventures philsimon, 24
- 25. • Collect asmuch data as you can • Be transparent with your customers about what you’re collecting—and what you’re going to do with it. Edison Ventures philsimon, 25
- 26. • Used patientmedical record data to identify nearly 200 people with diabetes who hadn’t been diagnosed yet. Edison Ventures philsimon, 26
- 27. • Go beyondstructured data. There’s tremendous potential valuable to be gleaned from unstructured data. • Act on insights without scaring customers. Edison Ventures philsimon, 27
- 28. • RavenPack turnstext from traditional and social media into structured feeds for quantitative applications Edison Ventures @philsimon, 28
- 29. • Information likethis can potentially help determine when sentiment towards a particular stock or set of stocks has changed • Coupled with traditional investment measures, better decisions may very well result Edison Ventures @philsimon, 29
- 30. @philsimon, 30Edison Ventures- @philsimon, 30
- 31. • Threatens thestatus quo and takes people out of their comfort zones • Often meets with resistance—and lots of it • Essential to the future of business • Often requires new skills. Edison Ventures - @philsimon, 31
- 32. Edison Ventures -@philsimon, 32 • Embrace data discovery • Think incrementally and add new types of data • Don’t try to boil the ocean • Think description first, prediction later
- 33. Edison Ventures -@philsimon, 33 • Manage expectations, under- promise, and over-deliver • Build internal momentum • Aim for little victories—and the communicate them • Make the skeptics and dataphiles come to you
- 34. Edison Ventures -@philsimon, 34 • Reject “set it and forget it.” • Look outside of the organization • Lead, follow, or get out of the way • Describe first…predict later
- 35. Edison Ventures -@philsimon, 35 • It’s a marathon… • not a sprint
- 36.
- 37. Edison Ventures -@philsimon, 37 • How can our organization use Big Data? • How can we learn more about our customers? Increase retention? Upsell? • How do I monitor trends and my competition? • How can I use new data sources to inform product and positioning?
- 38. www.philsimon.com @philsimon Edison Ventures -@philsimon, 38
- 39.
- 40. Networking Break Please beseated back in WAVES at 2:30pm for the next concurrent session
- 41. Concurrent Session: 2:30– 3:30pm Create a Repeatable Winning Sales Engine Elay Cohen, Co-founder & CEO SalesHood, former SVP Sales Productivity, salesforce.com
- 42. Elay Cohen Founder &CEO How To Scale Deal and Ramp Velocity?
- 43. Elay Cohen Founder, CEO,and Author Former Senior Vice President, Sales Productivity salesforce.com Creator of the Partner Relationship Management Category
- 44. Sales Productivity IsA $60B+ Problem Sales training & meetings are ineffective Sales and Marketing GTM are not aligned Reps aren’t ramping fast enough Difficult to scale velocity, certainty & repeatability
- 45. We Solved TheseSales Productivity Problems From $500M to $3B Deal Velocity Ramp to Revenue Revenue & rep productivity trend line
- 46. Think Like ACEO and Act Like an Entrepreneur Own outcomes Pipeline Revenue Customer success Skills & development Marketing programs First line sales managers make it happen
- 47. What It TakesTo Win? Sales Values & Process Ongoing Learning & Sharing Recruiting Machine Sales Management Cadence Go To Market
- 49. TEACH LEARN B US I N E S S T E C H N O L O G Y E M O T I O N A L P R E F E R E N C E R E A D I N E S S PROVE SIGN CLOSE L E G A L P U R C H A S I N G You Need Culture, Process, & Domain to Create
- 50. 1. What arethey buying? 2. Why are they buying? 3. When are they buying? 4. Who is buying? 5. Who is paying? Answer These Questions In Every Deal
- 51. Compelling Events DriveUrgency Compelling Event Date Internal Pressure External Pressure Personal Impact Consequences of Inaction
- 52. Be Transparent AboutMetrics & Results Follow Activity Emails Calls Connects Pipeline 1. Created 2. Ratios 3. Activity Revenue 1. Bookings 2. New logos 3. Cross-sell
- 53. Winning Sales ManagersCreate Team Cadence Monthly Sales Team Calendar Week 1: Kick Off & Forecast Increase predictability Week 2: Sales Topic & Pipeline Improve sales skills Week 3: Sales Topic & Pitch Share best practices Week 4: Close & Celebrate Energize the team
- 54. Learning is Relevant,Ongoing, & Immediately Usable World-class Onboarding Pre-work, boot camps & coaching Best Practice Sharing Peer to peer deal strategies, pitches & proposals Ongoing Learning Sales meetings, 1:1s, mentors 1 2 3
- 55. Balance Best PracticeExecution with Inspection Focus rep on customer activity that matters Share best practices across the team
- 56. Build a RecruitingMachine Recruiting Scorecard 1. Domain knowledge 2. Success selling technology 3. Quota accomplishments 4. Customer references 5. Peer references 6. Presentation skills 7. People skills 8. Social selling 9. Business acumen 10. Sales pitch The Pitch Try before you buy Team Everyone interviews
- 57. Execute a WinningGo To Market Strategy Marketing & sales alignment Product launches Partners & Resources Customer success Right leadership team Internationalization Maturity Localization Inspection Balance cultures
- 58.
- 59. Networking Break Please beseated back in AQUA at 3:45pm
Editor's Notes
- #3 What we subscribe to
- #5 3 bullets on who I am.
- #6 Who I am What is Big Data and why does it matter?
- #7 Who I am What is Big Data and why does it matter?
- #8 Who I am What is Big Data and why does it matter?
- #9 Let’s briefly define our terms here. What is Big Data? Doug Laney, Gartner variety, velocity, volume Generated by consumers, not employees 80% or more of new data generated is unstructured – contrast with Excel, Oracle
- #10 Explain each…
- #11 The Internet of Things Talk about duplicate tweets This is fundamentally different data; SQL has its limits
- #12 Self-explanatory Talk about Amazon; it combines both exceptionally well.
- #13 1 bullet, 3 sub-bullets
- #14 It isn’t necessarily about finding the right answers; it’s about having the data to ask better questions.
- #15 Different statistical packages ® Hadoop NoSQL/NewSQL – not only SQL Sentiment Analysis Text Analytics/Text Mining - Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text.
- #16 Shows the natural relationships among words. MOVE TO EXAMPLES
- #17 Who I am What is Big Data and why does it matter?
- #18 3 bullets - 3rd – this is why Net Neutrality is such a big deal Net neutrality (also network neutrality or Internet neutrality) is the principle that Internet service providers and governments should treat all data on the Internet equally, not discriminating or charging differentially by user, content, site, platform, application, type of attached equipment, and modes of communication. The term was coined by Columbia media law professor Tim Wu
- #19 Talk about the need for transparency, data discovery, and data democracy. Netflix tracks each customer selection, pause, exit. But forget staying within the enterprise. Netflix purchases an extensive amount of third-party data and metadata from firms like Nielsen, a leading global information and measurement company, provides market research, insights & data about what people watch & what people buy.
- #20 Never before has it been so important to act quickly, but how many organizations possess the right tools to act immediately? And a culture that embraces data to this extent? Because of the extent to which it relies upon data to make business decisions, Netflix knows more on its customers than just about any out there. The Netflix culture encourages and values curiosity. ***2nd bullet For instance….
- #21 And it’s this part that I want to focus on. KEVIN SPACEY NEXT
- #22 MOVE TO TWITTER
- #23 Who I am What is Big Data and why does it matter?
- #24 Here are some interesting examples
- #31 Who I am What is Big Data and why does it matter?
- #32 3 bullets Not used to doing this HR Director and Delamura examples time permitting Not going anywhere
- #33 4 bullets 1.Think less about proper reporting 2 Focus on the open-minded and the dataphiles 3. don’t try to boil the ocean. Maybe on a department or division basis ; Start small and organically; the entire organization need not approve. 4. The Holy Grail will have to wait.
- #34 2 big bullets; 2 subs after 2nd Manage expectations, under-promise, and over-deliver. Big Data reduces uncertainty; it does not eliminate it Build internal momentum Communicate big victories Make the skeptics and dataphiles come to you
- #35 Constantly look at reports, algorithms, data sources Big mistake Big Data isn’t going anywhere. Let people know what data you’re tracking–and how you might use it.
- #37 Who I am What is Big Data and why does it matter?
- #41 What we subscribe to
- #42 What we subscribe to
- #44 Give examples of Apple and Salesforce Talk from personal experience World class productivity Scale best practices Nurture peer coaching Resource appropriately Crowdsource sales tools Deliver just in time learning
- #45 90% of what is shared in training events is forgotten Over 100M hours of sales meetings are ineffective
- #46 Talk about SMB and companies of all sizes
- #51 What are they buying? Teach your sales teams to get crystal clear clarity on what solutions your customers are interested in buying. What they are buying should map back to challenges and have a clear sense of need. Why are they buying? The why a customer is buying should be tied to a specific event and top company priority. Focus on the corporate needs along with the personal needs of the buyers. When are they buying? Get your teams focused on a compelling event. Tie a decision to a specific date that is meaningful to your customer like their fiscal year, or a board decision or even some kind of company kick off event. Who is buying? Identifying the buyer and all the influencers is key to mapping out the steps to close and the actions required to win over every executive. Consider all the influencers even the ones that are not so obvious. Invest the time early to build a proper organization influence and political map to avoid surprises late in the sales cycle. Who is paying? A common question that is not usually answered until too late every sales cycle sheds light on the decision making process within a prospect. Find out who is paying and understand the customer’s decision-making process. Sometimes even though an executive champion makes a deciosin, procurement or finance approval can take weeks or months.
- #52 Four Dimensions of Compelling Events: Internal Pressure: Are there any forces within the prospect organization that are driving a decision to be made? External Pressure: Is there pressure from shareholders, regulatory agencies, or other 3rd parties that are creating a deadline? Personal Impact: How does the purchase decision impact the individuals in the prospect organization? Does professional advancement depend on the outcome; or the opportunity to achieve a bonus? Consequences of Inaction: Are there any negative consequences of doing nothing? Are careers at risk?
- #60 What we subscribe to