Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Best Practices for Keeping Your Data Clean<br />Marketing Professionals<br />Dave Hughan: Jigsaw<br />Greg Malpass: Tracti...
Safe Harbor<br />Safe harbor statement under the Private Securities Litigation Reform Act of 1995: This presentation may c...
Dave Hughan<br />salesforce.com<br />
Agenda<br />Why you need to have a data quality strategy?<br />Best practices for data cleansing and data quality<br />How...
Good Data is the Lifeblood of Sales & Marketing<br />Business <br />Cards<br />Marketing Lists<br />Sales Reps<br />Web Fo...
All Companies Struggle in Some Way with Data<br />Average Dirty Data Found in Customers Before Jigsaw<br />90%<br />74%<br...
We Spend $10B+ Buying, Managing, Cleaning It<br />$0.5 Billion Market<br />Sales & Marketing Research, Intelligence<br />$...
There’s a Real Impact on Marketing Organizations<br />70% of Contact Data Outdated<br />after 12 Months1<br />65% of title...
And a Real Impact on Sales Organizations<br />One sales rep calling 12 wrong numbers a day wastes 20+ hours a month.<br />...
Having Clean Data Requires a Plan<br />1<br />2<br />5<br />4<br />3<br />Enrich/Integrate/<br />Automate<br />Standardize...
Greg Malpass<br />Traction<br />
Best Practices Overview<br />Building the plan<br />Data Quality – Who/How<br />Getting required buy-in<br />
Data Quality Planning/Strategy<br />Where is quality data mandatory<br />
Data Quality Planning/Strategy<br />Where is quality data mandatory<br />What is the business doing to workaround<br />Exc...
Data Quality Planning/Strategy<br />Where is quality data mandatory<br />What is the business doing to workaround<br />How...
Getting from Mediocre to Great<br />Not all at once<br />Eventually, you are going to need to spend money<br />Strategy<br...
Consider All Data Levers<br />Web/Static Lists <br />Customers<br />Internal dB<br />Users<br />Data Change Tools<br />Sub...
Consider All Data Levers<br />AutoClean: Standardize/Normalize your bad data<br />Goal: Standardize your existing data<br ...
State standardization
Country ISO</li></ul>Clean<br />
Consider All Data Levers<br />Leverage the Web: Free data, resources, partners etc<br />Goal: Fill in the blanks with what...
Consider All Data Levers<br />External Data Quality Service Providers<br />Goal: Link into the cloud<br />Tools: Jigsaw, H...
Don’t open the gates up right away
Don’t expect perfection</li></li></ul><li>Consider All Data Levers<br />Tools: Get what you need, become an expert<br />Go...
Forwarn users
Add exclusion fields for users</li></li></ul><li>Consider All Data Levers<br />Users: the obvious choice.  Easily angered<...
Conga – PPT generator
EchoSign - Esignature</li></ul>-Validation Rules – force quality<br />-Workflow – fill in the blanks realtime<br />Process...
Consider All Data Levers<br />Customers: Closest to the truth, most effort to engage<br />Goal:     Expose Salesforce data...
Executive Buy-in<br />Show progress<br />Make Salesforce your system of record<br />Work around the warehouse<br />Make ex...
Renee Gellatly<br />NetApp<br />
About NetApp<br />The Company<br />NetApp creates innovative storage and data management solutions that deliver outstandin...
Incomplete Records
Upcoming SlideShare
Loading in …5
×

Best Practices for Keeping Your Data Clean

20,144 views

Published on

Accurate data is fundamental to the success of both sales and marketing, yet creating a process for maintaining clean data is no small task. In this session, we'll cover best practices for creating and maintaining clean data, as well as for converting dirty data into your next hot lead. Join us to hear from operations and IT leaders at salesforce.com, Jigsaw, and a few of our fantastic customers.

Published in: Business, Technology
  • Important points to consider before data cleansing -

    •    An understanding of what kind of data you are maintaining
    •    Having a systematic approach to removing duplicate data
    •    A means of enhancing and updating data
    •    A program for verifying data
    •    Continually updating and augmenting the data in your database

    Thanks,
    Dhirender
    http://www.dataladder.com
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Best Practices for Keeping Your Data Clean

  1. 1. Best Practices for Keeping Your Data Clean<br />Marketing Professionals<br />Dave Hughan: Jigsaw<br />Greg Malpass: Traction Sales & Marketing Inc<br />Renee Gellatly: NetApp<br />
  2. 2. Safe Harbor<br />Safe 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 such uncertainties materialize or if any of the assumptions proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other than statements of historical fact could be deemed forward-looking, including any projections of subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plans of management for future operations, statements of belief, any statements concerning new, planned, or upgraded services or technology developments and customer contracts or use of our services.<br />The risks and uncertainties referred to above include – but are not limited to – risks associated with developing and delivering new functionality for our service, our new business model, our past operating losses, possible fluctuations in our operating results and rate of growth, interruptions or delays in our Web hosting, breach of our security measures, the outcome of intellectual property and other litigation, risks associated with possible mergers and 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 customer deployment, our limited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further information on potential factors that could affect the financial results of salesforce.com, inc. is included in our annual report on Form 10-K for the most recent fiscal year ended January 31, 2010. This documents and others are available on the SEC Filings section of the Investor Information section of our Web site. <br />Any unreleased services or features referenced in this or other press releases or public statements are not currently available and may not be delivered on time or at all. Customers who purchase our services should make the purchase decisions based upon features that are currently available. Salesforce.com, inc. assumes no obligation and does not intend to update these forward-looking statements.<br />
  3. 3. Dave Hughan<br />salesforce.com<br />
  4. 4. Agenda<br />Why you need to have a data quality strategy?<br />Best practices for data cleansing and data quality<br />How NetApp is keeping their data clean and relevant <br />and the results<br />Q&A<br />
  5. 5. Good Data is the Lifeblood of Sales & Marketing<br />Business <br />Cards<br />Marketing Lists<br />Sales Reps<br />Web Forms<br />Critical for Prospecting<br />Required for Customer Analysis<br />
  6. 6. All Companies Struggle in Some Way with Data<br />Average Dirty Data Found in Customers Before Jigsaw<br />90%<br />74%<br />21%<br />7%<br />Incomplete<br />Need Updates<br />Dead<br />Duplicate<br />
  7. 7. We Spend $10B+ Buying, Managing, Cleaning It<br />$0.5 Billion Market<br />Sales & Marketing Research, Intelligence<br />$4 Billion Market<br />CRM Data & <br />Marketing Lists<br />$6 Billion Market<br />Data Integration,<br />Management / Hygiene<br />$10+ Billion Annual Market<br />
  8. 8. There’s a Real Impact on Marketing Organizations<br />70% of Contact Data Outdated<br />after 12 Months1<br />65% of titles incorrect<br />42% of addresses incorrect<br />43% of phone numbers incorrect<br />37% of email addresses incorrect<br />2010<br />2009<br />1Coe, John M. “B2B Data Decay – The Untold Story.” Sales & Marketing Institute. 2002 http://www.b2bmarketing.com.<br />
  9. 9. And a Real Impact on Sales Organizations<br />One sales rep calling 12 wrong numbers a day wastes 20+ hours a month.<br />Average Dirty Data Found in New Jigsaw Customers:<br />90%<br />74%<br />21%<br />7%<br />1Coe, John M. “B2B Data Decay – The Untold Story.” Sales & Marketing Institute. 2002 http://www.b2bmarketing.com.<br />Incomplete<br />Need Updates<br />Dead<br />Duplicate<br />
  10. 10. Having Clean Data Requires a Plan<br />1<br />2<br />5<br />4<br />3<br />Enrich/Integrate/<br />Automate<br />Standardize<br />Cleanse<br />Validate<br />De-dupe<br />Names<br /> Load to <br />Sandbox<br />Find & <br />Replace<br /> Identify, <br />Match & Score<br />acme incorp.-> Acme Inc<br />Company <br />Name & Address<br />Hot  HighCold  Low<br />J. Smith, John Smith – 80%<br />Naming Conventions<br />Addresses<br /> Merge<br />Validate <br />&Modify<br />Hierarchy Data<br />J. Smith, John Smith -> John Smith<br />US, U.S, U.S.A -> USA <br />Acme-Widgets-453<br />Acme Inc HQ<br />Acme UK<br />Data Transformation<br />Postal <br />Standards<br /> Re-parent <br />Child Records<br />Load to <br />Production<br />Demographics<br />Mergers, acquisitions, spin-offs<br />Account: Division, Opportunity, Contact<br />Archiving & Filtering<br />
  11. 11. Greg Malpass<br />Traction<br />
  12. 12. Best Practices Overview<br />Building the plan<br />Data Quality – Who/How<br />Getting required buy-in<br />
  13. 13. Data Quality Planning/Strategy<br />Where is quality data mandatory<br />
  14. 14. Data Quality Planning/Strategy<br />Where is quality data mandatory<br />What is the business doing to workaround<br />Excel Reporting<br />Other tools/dB<br />Outlook <br />Notepads<br />
  15. 15. Data Quality Planning/Strategy<br />Where is quality data mandatory<br />What is the business doing to workaround<br />How will you define quality data<br />Guideline: Expect perfection, expect infinite effort<br />What fields<br />Measures of quality<br />Acceptable variance<br />
  16. 16. Getting from Mediocre to Great<br />Not all at once<br />Eventually, you are going to need to spend money<br />Strategy<br />Easy wins first<br />Build value in data<br />Make the ask<br />
  17. 17. Consider All Data Levers<br />Web/Static Lists <br />Customers<br />Internal dB<br />Users<br />Data Change Tools<br />Subscription Services<br />
  18. 18. Consider All Data Levers<br />AutoClean: Standardize/Normalize your bad data<br />Goal: Standardize your existing data<br />Clean Tools: Jigsaw Clean, Excel Connector/Data Loader<br />Maintain Tools: Validation/Workflow<br />Sources: Salesforce and Std Values(ISO)<br />Process:<br /><ul><li>URL matching
  19. 19. State standardization
  20. 20. Country ISO</li></ul>Clean<br />
  21. 21. Consider All Data Levers<br />Leverage the Web: Free data, resources, partners etc<br />Goal: Fill in the blanks with what you can find<br />Clean Tools: Excel Connector/Data Loader, ListGrabberPro, Jigsaw Clean<br />Maintain Tools: Same – schedule reminder<br />Sources: Jigsaw Free Company File, Linked in, Wikipedia for stdized values, YellowPages<br />Process:<br /><ul><li>Find, Compare, Match, Append</li></li></ul><li>Consider All Data Levers<br />Integration: Is not Impossible – Crawl, Walk, Run<br />Goal: Link into other sources, single version of truth across the enterprise<br />Tools: Integration – Informatica, CastIron, Boomi, Pervasive, Jitterbit<br />Systems:ERP, Backoffice, Warehouse other<br />Process: Start simple, don’t boil the ocean<br />
  22. 22. Consider All Data Levers<br />External Data Quality Service Providers<br />Goal: Link into the cloud<br />Tools: Jigsaw, Hoovers, D&B, Onesource or consider Industry Specific via Integrators ie: Astadia and McGraw Hill <br />Considerations:<br /><ul><li>Standardize first to optimize match
  23. 23. Don’t open the gates up right away
  24. 24. Don’t expect perfection</li></li></ul><li>Consider All Data Levers<br />Tools: Get what you need, become an expert<br />Goal: Once you perfect manual, apply muscle<br />Tools: CRM Fusion, Excel Connector, Data Loader<br />Considerations:<br /><ul><li>Build your own routines
  25. 25. Forwarn users
  26. 26. Add exclusion fields for users</li></li></ul><li>Consider All Data Levers<br />Users: the obvious choice. Easily angered<br />Goal: Make it worth their while<br />Tools: Anything that saves time, and improves conversion: <br /><ul><li>SFDC Quotes
  27. 27. Conga – PPT generator
  28. 28. EchoSign - Esignature</li></ul>-Validation Rules – force quality<br />-Workflow – fill in the blanks realtime<br />Process: Get a plan, sequence it out, follow the user<br />
  29. 29. Consider All Data Levers<br />Customers: Closest to the truth, most effort to engage<br />Goal: Expose Salesforce data to customers<br />Tools: E-Sign, Clicktools, Service Portal, Customer Portal<br />How: Use Salesforce data to generate contracts, ask for updates,<br />Process: 1. Surveys<br /> 2. Quotes<br /> 3. Portals<br /> 4. E-Sign<br />
  30. 30. Executive Buy-in<br />Show progress<br />Make Salesforce your system of record<br />Work around the warehouse<br />Make exec reporting, the focal point of your implementation<br />Expose the true cost of poor data<br />Real time all the time<br />
  31. 31. Renee Gellatly<br />NetApp<br />
  32. 32. About NetApp<br />The Company<br />NetApp creates innovative storage and data management solutions that deliver outstanding cost efficiency and accelerate performance breakthroughs. Discover our passion for helping companies around the world go further, faster at www.netapp.com.<br />The Problem<br />Data Quality <br /><ul><li>Database Loss
  33. 33. Incomplete Records
  34. 34. Missing Processes </li></li></ul><li>Data Quality <br />Key Challenges<br /><ul><li>25% Database Loss
  35. 35. Incomplete Account and Contact Records
  36. 36. Missing Processes
  37. 37. Standardization
  38. 38. Cleanse
  39. 39. Append</li></li></ul><li>The Solution<br />Defining Our Data Management Strategy<br /><ul><li>Build the Solution
  40. 40. Secure Executive Sponsorship
  41. 41. Test
  42. 42. Rollout</li></ul>Marketing Database<br />
  43. 43. Data Quality – A Recent Snapshot of Activity<br />
  44. 44. The Results <br />Integrating Jigsaw into NetApp Strategies and Systems<br /><ul><li>Complete Contact Profiles
  45. 45. Quarterly Database Cleanse
  46. 46. Automation - API Integration
  47. 47. Account and Contact Master</li></ul>Complete<br />
  48. 48. How Could Dreamforce Be Better? Tell Us!<br />Log in to the Dreamforce app to submit<br />surveys for the sessions you attended<br />Use the Dreamforce Mobile app to submit surveys<br />OR<br />Every session survey you submit is a chance to win an iPod nano!<br />
  49. 49. Thank you!<br />

×