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Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
Best Practices for Clean Data
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Best Practices for Clean Data

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Everyone knows good data is what makes sales and marketing run like a charm and CRM adoption skyrocket. But how do you gather good data to fuel your business? And after gathering this data, how do you …

Everyone knows good data is what makes sales and marketing run like a charm and CRM adoption skyrocket. But how do you gather good data to fuel your business? And after gathering this data, how do you keep it clean? This session offers customer best practices on capturing quality lead data, validating and updating this data, and maintaining clean CRM data overall. Learn how Jigsaw can clean up your dirty data, fill in incomplete existing records, and give you a massive new source of leads, contacts, and accounts.

Presented by: David Hughan, salesforce.com, Greg Malpass, Traction and Kevin Kramer, Riverbed

Published in: Business, Technology
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  • 1. Best Practices for Clean DataAdministratorsDavid Hughan, salesforce.comGreg Malpass, TractionKevin Kramer, Riverbed
  • 2. Safe HarborSafe harbor statement under the Private Securities Litigation Reform Act of 1995:This presentation may contain forward-looking statements that involve risks, uncertainties, and assumptions. If any suchuncertainties materialize or if any of the assumptions proves incorrect, the results of salesforce.com, inc. could differmaterially from the results expressed or implied by the forward-looking statements we make. All statements other thanstatements of historical fact could be deemed forward-looking, including any projections of product or service availability,subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plans ofmanagement for future operations, statements of belief, any statements concerning new, planned, or upgraded servicesor technology developments and customer contracts or use of our services.The risks and uncertainties referred to above include – but are not limited to – risks associated with developing anddelivering new functionality for our service, new products and services, our new business model, our past operatinglosses, possible fluctuations in our operating results and rate of growth, interruptions or delays in our Web hosting, breachof our security measures, the outcome of intellectual property and other litigation, risks associated with possible mergersand acquisitions, the immature market in which we operate, our relatively limited operating history, our ability to expand,retain, and motivate our employees and manage our growth, new releases of our service and successful customerdeployment, our limited history reselling non-salesforce.com products, and utilization and selling to larger enterprisecustomers. Further information on potential factors that could affect the financial results of salesforce.com, inc. is includedin our annual report on Form 10-Q for the most recent fiscal quarter ended April 30, 2011. This documents and otherscontaining important disclosures are available on the SEC Filings section of the Investor Information section of our Website.Any unreleased services or features referenced in this or other presentations, press releases or public statements are notcurrently available and may not be delivered on time or at all. Customers who purchase our services should make thepurchase decisions based upon features that are currently available. Salesforce.com, inc. assumes no obligation anddoes not intend to update these forward-looking statements.
  • 3. David HughanSenior Director, Services
  • 4. Tell us about yourself How many of you are a Salesforce admin? How many are in sales? Is this your first Dreamforce? Do you know what Data.com is? What are your biggest data pain points? A. Not enough contacts B. Incomplete/Inaccurate Contacts C. Duplicates D. Other
  • 5. Agenda What is Data.com? What data issues do we see in clients data? Why you need to focus and plan Best practices for data cleansing and data quality How Riverbed is tackling the challenge of keeping their data clean and relevant Q&A
  • 6. Jigsaw Becomes Data.com at Dreamforce
  • 7. Today, Succeeding in Business Requires Good Data Top Salesforce Customer Request “ “ “ “ Source: MarketTools May 2011 Salesforce Customer Survey
  • 8. Business Data Currently Lives All Over the Place
  • 9. As a Result, Your Business Can’t Focus With Bad Data or No Data, Its Tough to Make Headway Who to Call? What to Target? How to Allocate Resources?
  • 10. What is Data.com? How can we help? What do we see? Accurate Business Contact & Account Data 30 million crowd-sourced contacts 1 million new contacts a month 200 million accounts 400% growth in customers Socially Gathered and Managed 2 Million Member Community 40% of Corporate Customers Contribute Data Accessible Any Way You Want It Live inside Salesforce CRM Online at Jigsaw.com Offline for Lists or Cleaning
  • 11. What are our solutions? Data.com Lists Data.com in Data.com Clean SalesforceFor Sales & Marketing Online for Sales & Marketing For IT & Operations• Targeted Lists • Unlimited Account Viewing • List Cleaning• Prospecting Tools • Unlimited Contact Viewing • Master File Creation• Full Database Offline • Real-time Data Cleanse • Fully integrated into Salesforce
  • 12. What do we see and how can we help? 90% 75% 28% 5% Incomplete Need Updates Dead Duplicate What Data.com is seeing in client engagements for analysis or hygiene
  • 13. What we see in Marketing Organizations 70% of Contact Data Outdated after 12 Months1 65% of titles incorrect 42% of addresses incorrect 43% of phone numbers incorrect 2010 37% of email addresses incorrect 2011 1Coe, John M. “B2B Data Decay – The Untold Story.” Sales & Marketing Institute. 2002 http://www.b2bmarketing.com.
  • 14. Having Clean Data Requires a Plan 1 2 3 4 5 Standardize Cleanse De-dupe Enrich/Integrate/ Validate Automate Names Find & Identify, Load to Replace Match & Score Sandboxacme incorp.-> Acme Inc Hot  High J. Smith, John Smith – Cold  Low 80% Company Name & Address Addresses Naming Merge Validate Conventions J. Smith, John Smith -> Hierarchy Data & Modify US, U.S, U.S.A -> USA Acme-Widgets-453 John Smith Acme Inc HQ Acme UK Postal Data Re-parent Load to Standards Transformation Child Records Demographics Production Mergers, acquisitions, Account: Division, spin-offs Opportunity, Contact Archiving & Filtering
  • 15. Greg MalpassFounder and CEO Traction
  • 16. Walkaways  Process to realize data utopia  Tools /Strategies to accelerate your path in getting their  You, APEX, Force.com, API and Data Quality Warning: There is a lot of content. All samples, tools and materials referenced will be posted after the session with their applicability.
  • 17. Building a plan around Data Quality Define what is quality to your users Assess the current state Isolate what is fueling the issue Resolve Fix the problems, nail the symptoms. Repeat
  • 18. What fields are mission critical? Account Assignment/ Asset Management Contact Management URL matching and Search Territories/Marketing Reports/Planning Finance System Integration Layouts
  • 19. What fields are adoption critical?
  • 20. How is the business measured?  Look to dashboards/reports to give you insight What reports are being run and by whom? What data is being reviewed for decision making?
  • 21. What is quality to your users? – Other examples to help determine what matters • Lead Assignments • Named account/Territory Management • Public facing forms • Marketing/Customer profiling/segments • Support integrations/passover
  • 22. What is quality to your users? – Other examples to help determine what matters • Lead Assignments • Named account/Territory Management • Public facing forms • Marketing/Customer profiling/segments • Support integrations/passover – Don’t boil the Ocean • Don’t get obsessive • Find the balance (quality/quantity)
  • 23. Building a plan around Data Quality Define what is quality to your users Assess the current state Isolate what is fueling the issue Resolve Fix the problems, nail the symptoms. Repeat
  • 24. Where are you from where you need to be?  Look to dashboards/reports to give you insight How complete are your records? - Address - Email - Corporate info How consistent are the values? - Easy to ID - Easy to fix
  • 25. Where are you from where you need to be?  Look to dashboards/reports to give you insight How fresh is your data? - Lead info - Corp profiles - Pipeline Is it accurate? - 3rd party integration/compare - internal
  • 26. Don’t get lost in the exceptions – Important consideration – Pareto Principle • 80/20 rule • Grade level of difficulty to fix
  • 27. Building a plan around Data Quality Define what is quality to your users Assess the current state Isolate what is fueling the issue Resolve Fix the problems, nail the symptoms. Repeat
  • 28. Symptoms/Problems that drive poor data internally No Incentive Inappropriate Ask Unaware EvilIncomplete X X XInaccurate X XInconsistent X XAged X XDuplicate X X X
  • 29. Overload ahead  Posting to chatter  Most have free full prod trials  Quick sheet also will be posted on the tools
  • 30. Building a plan around Data Quality Define what is quality to your users Assess the current state Isolate what is fueling the issue Resolve Fix the problems, nail the symptoms. Repeat
  • 31. You fix it Duplicates/Import Process – Standardize your method – Distribute your templates – Save your mappings – Share across your team
  • 32. You fix it Normalize outliers – Focus on critical fields – Find auto fixes for std issues – Generate exception reports
  • 33. You fix it Fill in the blanks – Workflow can handle basics – Manual append – (external ID) – Append with third party
  • 34. You fix it API Fill in the blanks Informatica – Integration Considerations ETL Boomi Castiron • When to use the API Sesame – Realtime – Event driven – Limited transformation • When to use tools – Batch – Complex – Changing
  • 35. Users fix it Carrot before the stick – Make Salesforce “Do” • Create Oppty • Send Mail merge letters • Add products • Send quote • Update products • Send contract
  • 36. Users fix it Carrot before the stick – Make Salesforce “Smart” • Visualizations of the data • Launchpad into the web • Launchpad into reports • Then validation/required etc…. SFDC URL Report ID Filter 1 Filter 2 https://tractionsm.my.salesforce.com/00O300000061vQG?pv0={!Account.Annual_Revenue}&pv1={!Account.Industry}
  • 37. Customers fix it Straight to the source – Customer / Partner Portal – CSAT/NPS – Surveys – Profiling/Registration – Contracts as a datasource – Progressive profiling
  • 38. Get Creative
  • 39. Using APEX/API/ETL to your advantage  APEX – Use triggers/APEX to complete inline record correction
  • 40. What are you looking for?What to do when you find it?Where to put it?
  • 41. Leveraging the Jigsaw/SFDC API  Access / append your data with more context – Secondary industry – More advanced metadata – Via custom objects/business process
  • 42. Jigsaw XML and JSON support
  • 43. Kevin KramerSenior Director of SalesExecution and Strategy
  • 44. About Kramer, Riverbed, Etc.  Kevin Kramer – Sr, Director Sales Execution & Strategy
  • 45. What is Sales Execution & Strategy?
  • 46. What is Sales Execution & Strategy? Sustain RVBD Growth Through Relentless Execution • Targeting • Strategies • Tools • Programs
  • 47. Why Bother? What Value Does Clean Data Have?  Finding the Right Organizations to Target – Data-driven targeting  Finding The Right Entry Points to an Organization – Target buyer profiles  Providing Context to Sales Reps – The right conversation at the right time with the right information
  • 48. What Does Clean Data Do For Me? As a Sales Executive, I want to know:  What do I have? What don’t I have? How can I get more? – Corporate Family Insight – Whitespace  Who do I have? Who don’t I have? How can I get more? – Contact counts at customers – Contact types at customers  Who is expressing interest? How do they overlap with what I have? How can I prioritize them effectively? – Rating the Company not the Person
  • 49. What Does Clean Data Do For Me? As a Sales Executive, I want to know:  What do I have? What don’t I have? How can I get more? – Corporate Family Insight – Whitespace  Who do I have? Who don’t I have? How can I get more? – Contact counts at customers – Contact types at customers  Who is expressing interest? How do they overlap with what I have? How can I prioritize them effectively? – Rating the Company not the Person
  • 50. What do these things have in common?
  • 51. Category: Existing Category: SuspectClosed / Open : $100K / $25K Closed / Open: $0 / $0 Category: Existing Closed / Open: $25K / $0
  • 52. Leveraging Corporate Structure InsightCategory: Existing Category: SuspectClosed / Open : $100K / $25K Closed / Open: $0 / $0 Category: Existing Closed / Open: $25K / $0
  • 53. Leveraging Corporate Structure Insight Category: Customer Closed / Open : $125K / $25KCategory: Existing Category: SuspectClosed / Open : $100K / $25K Closed / Open: $0 / $0 Category: Existing Closed / Open: $25K / $0
  • 54. Leveraging Corporate Structure Insight Closed / Open : $125K / $25K Largest Customer: Nestle NespressoCategory: Existing Category: SuspectClosed / Open : $100K / $25K Closed / Open: $0 / $0 Category: Existing Closed / Open: $25K / $0
  • 55. Leveraging Corporate Structure Insight Closed / Open : $125K / $25K Largest Customer: Nestle NespressoCategory: Existing Category: SuspectClosed / Open : $100K / $25K Closed / Open: $0 / $0Largest Sibling: Nestle Nespresso Largest Sibling: Nestle Nespresso Category: Existing Closed / Open: $25K / $0 Largest Sibling: Nestle Nespresso
  • 56. Leveraging Corporate Structure Insight
  • 57. Leveraging Corporate Structure Insight Category: Suspect Annual Rev: $1.8B Rev. Largest Sibling: Nestle Nespresso WhiteSpace
  • 58. What Does Clean Data Do For Me? As a Sales Executive, I want to know:  What do I have? What don’t I have? How can I get more? – Corporate Family Insight – Whitespace  Who do I have? Who don’t I have? How can I get more? – Contact counts at customers – Contact types at customers  Who is expressing interest? How do they overlap with what I have? How can I prioritize them effectively? – Rating the Company not the Person
  • 59. Contacts at RVBD Customers – By The Numbers
  • 60. What Does Clean Data Do For Me? As a Sales Executive, I want to know:  What do I have? What don’t I have? How can I get more? – Corporate Family Insight – Whitespace  Who do I have? Who don’t I have? How can I get more? – Contact counts at customers – Contact types at customers  Who is expressing interest? How do they overlap with what I have? How can I prioritize them effectively? – Rating the Company not the Person
  • 61. IN-Bound A Lead is an Expression of Interest in Riverbed by a Company Embodied by a Person
  • 62. IN-Bound A Lead is an Expression of Interest by a Company
  • 63. HowPicture – Give a LeadWorks:Big Probable Account “Context” by Linking to Account 1. USE EMAIL ADDRESS/DOMAIN….
  • 64. HowPicture – Give a LeadWorks:Big Probable Account “Context” by Linking to Account 1. USE EMAIL ADDRESS/DOMAIN…. 2. TO LOOK FOR A MATCHING ACCOUNT
  • 65. How Probable Account Works: to AccountGive a Lead “Context” by Linking 1. USE EMAIL ADDRESS/DOMAIN…. 2. TO LOOK FOR A MATCHING ACCOUNT3. TO PROVIDE THE BIG PICTURE ON THE LEAD
  • 66. How Probable Account Works: to AccountGive a Lead “Context” by Linking 1. USE EMAIL ADDRESS/DOMAIN…. 2. TO LOOK FOR A MATCHING ACCOUNT3. TO PROVIDE THE BIG PICTURE ON THE LEAD
  • 67. Lead – Account Matching Analysis
  • 68. Lead – Account Matching – M&A Leverage
  • 69. What Does Clean Data Do For Me? As a Sales Executive, I want to know:  What do I have? What don’t I have? How can I get more? – Corporate Family Insight – Whitespace  Who do I have? Who don’t I have? How can I get more? – Contact counts at customers – Contact types at customers  Who is expressing interest? How do they overlap with what I have? How can I prioritize them effectively? – Rating the Company not the Person
  • 70. Demonstration
  • 71. Questions & Answers David Hughan Senior Director of Services Greg Malpass Founder and CEO Kevin Kramer Senior Director of Sales Execution and Strategy
  • 72. Key Takeaways  Clean data for the sake of clean data is not the point  Your data strategy is driven by your business strategy  Have a data process, but be creative in your approach
  • 73. Data.com is Alreadyin SalesforceGet Started Today http://www.salesforce.com/data
  • 74. How Could Dreamforce Be Even Better? Tell Us! Every session survey you submit is a chance to win an iPad 2! Watch your inbox at the end of each day for an email from our survey partner, Alliance Tech. Click on the personalized link to be directed to the survey page for the sessions you attended.

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