How to Overcome Your Data Quality Superstitions

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Are you fearful that trying to fix your company’s data quality issues will result in 7 years of bad luck? Too often, data quality superstitions lead to paralysis by analysis. You don't need a rabbit's foot to make progress with your data management strategy, you just have to separate fact from fable.

Donato Diorio and Michael Farrington, two experts in CRM and marketing automation technology, dispel several common data superstitions providing tangible and actionable advice to ensure “good luck” for all who rely on your data.

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How to Overcome Your Data Quality Superstitions

  1. 1. How to Overcome Your Data Quality Superstitions Donato Diorio Founder & CEO Broadlook Technologies www.broadlook.com #DataBadLuck Michael Farrington Chief Product Officer RingLead www.ringlead.com
  2. 2. Key trends in CRM Social Cloud Metrics/ Dashboards Big data & sales intelligence #DataBadLuck Mobile Analytics Collaborative selling Empowered Users
  3. 3. The Foundation: Good CRM Data Without enhancing your existing data you limit your “data potential” Clean Limited potential Protect Without performing a comprehensive data cleanse, the foundation is weak #DataBadLuck Without a protection Without… strategy, your data will continually decay Decaying data Poor foundation Enhance Enhanc e
  4. 4. MYTH It’s ok to delete data #DataBadLuck
  5. 5. Never delete CRM data - Score it Instead! dhughes@broadlook.com nbauman@broadlook.com #DataBadLuck
  6. 6. Scoring Data: Focus on Good Data dhughes@broadlook.com ddiorio@broadlook.com nbauman@broadlook.com #DataBadLuck
  7. 7. What can we learn/derive from the existing data? • • • • • • • • • Domain is broadlook.com Email pattern in first-initial(.)last-name April reports to Donato On broadlook.com, there are 15 additional contacts Notes on Donato are 1 month old Notes on April are 8 months old Natalie is no longer at the company “The Doctor” is a fictional character Natalie is now a VP at another company - and an additional prospect! #DataBadLuck
  8. 8. Result: A More Complete Picture ddiorio@broadlook.com abroom@broadlook.com dhughes@broadlook.com ipetrenko@broadlook.com akazansky@broadlook.com kschuetz@broadlook.com New Prospect: nbauman@xyzcorp.com #DataBadLuck
  9. 9. MYTH Using the stick works: make all fields required! #DataBadLuck
  10. 10. Using the stick works • Determine carrot and/or stick on field basis, not object • Educate users on the importance of everything you ask of them (focus on selfish reasons) • Don’t ask what they don’t know #DataBadLuck
  11. 11. MYTH Training works to enforce data standards #DataBadLuck
  12. 12. #DataBadLuck
  13. 13. Enforcing Data Standards is Optimal #DataBadLuck
  14. 14. MYTH My Data is Fairly Complete #DataBadLuck
  15. 15. My Data is Fairly Complete • Superstition or fact? Find out! #DataBadLuck
  16. 16. My Data is Fairly Complete #DataBadLuck
  17. 17. MYTH Buy as much data as you can, all at once (because it’s cheaper) #DataBadLuck
  18. 18. Data decay happens • Change in title, promotion • Change of department • Change in working location • Change of area code • Change of phone number • Change of email format • Add mobile phone number • Merger or acquisition #DataBadLuck
  19. 19. #DataBadLuck
  20. 20. MYTH Bad Data is IT’s Problem #DataBadLuck
  21. 21. Bad Data is IT’s Problem • He who reporteth upon it... • Treat it like a project • Choose Data Quality applications that don’t require a PhD in Computer Physiology #DataBadLuck
  22. 22. MYTH The company’s name is more important than the website address #DataBadLuck
  23. 23. Company Based Changes Decade Multi year Year Contact based Event & Activity Based Quarter Month Day Static Data types Acquisition method URL Hour Dynamic Corp Name Database merging + algorithm Update strategy #DataBadLuck City State Address Zip Phone Competitors Editorial & Aggregation Revenue Employees Products Services Financials Editorial + SEC spidering Static, compiled and online databases Names Titles Emails address Phone Biographies Social Network Links Real time content spidering News Email content Blogs Net links social networks newsgroups Tweet Check-In’s Proximity Website visits Email reads Semantic monitoring services Real time API’s Real time
  24. 24. MYTH I know how to search for duplicates #DataBadLuck
  25. 25. I know how to search for duplicates • It gets messy • Users may not have access • Even if you do, is that a good use of your (user’s) time? #DataBadLuck
  26. 26. MYTH My vendor’s data is better than mine (they are the specialists right?) #DataBadLuck
  27. 27. Data industry processes •Buy data from multiple sources •Refresh top companies with editors •6 month cycle (top 10K companies) •6-12 month (next 40K companies) •24 month cycle on the next 2 million •Nothing past the top 2 million •Add social data (good for top 10%) •Add news feeds (good for top 5%) •Mob source #DataBadLuck
  28. 28. Buying data...why, how and gotchas How recent is the list as whole? How quickly was the list produced? Different from record freshness. Contact data degrades 3% per month (5% in a stressed economy). A list of 1000 records can be built over 60 days. In the case below, the first 500 records are 8 weeks old (5.68% inaccurate) upon list delivery. 96.8% #DataBadLuck
  29. 29. Buying data...why, how and gotchas 86.5% #DataBadLuck 59.5%
  30. 30. Your data vs. your vendor’s • Your data is less complete • Your data has a better competitive advantage • Use their data to fill in your data #DataBadLuck
  31. 31. MYTH My data is awesome! #DataBadLuck
  32. 32. CRM Data Quality Points 4 3 2 1 Fresh <30 days <60 days <90 days <180 days Accurate 95.00% 80% + 70% + 60% + Factors Basic Basic + 2 social Basic + 1 social (email+phon e) Multi-venue All available Built fast <14 days <60 days <90 days <180 days Normalized Enforced Plan + culture Has plan no Scored Custom rules Accessible rules white box scoring black box scoring Total data quality score: #DataBadLuck Your score
  33. 33. CRM Competitive Advantage Points 4 3 2 1 target by self description hand built keywords SIC code built on-demand mashed from many sources pulled from larger sample Complete 95%+ 80.0% 60.0% 40.0% Exclusive no competitors limited access anyone can buy access free Sources transparent sources known sources available By a person Marketing automation email Factors Targeted Custom Transparent Verified Total competitive advantage score: #DataBadLuck Your score
  34. 34. Where is your CRM data? Competitive Advantage 24 12 0 12 Data Quality #DataBadLuck 24
  35. 35. What is your data potential? Qualitative / Event-Driven Qualitative Cyclic Quantitative / Cyclic Competitive Advantage Quadrant Key 24 12 Quantitative/ commodity Influence Relationship g tin ion CRM+ ke at ar m 90 days m to CRM+ u a 180 CRM+ 360 days days Cold Call 0 new CRM lead Warm call 12 Data Quality #DataBadLuck 24
  36. 36. MYTH Preventing Duplicate Records Based on Email is Sufficient #DataBadLuck
  37. 37. Preventing Duplicate Records Based on Email is Sufficient • No. Not even for sending emails. • Email addresses are not social security numbers • True story: I had four email addresses at one company #DataBadLuck
  38. 38. MYTH My sales team lets me know what they need #DataBadLuck
  39. 39. The Evolution of Sales Desire I want...More data (lists) I want... Better selection (databases) I want... More contacts per company (zoom) I want... Fresher contacts(Jigsaw) I want... More information (LinkedIn) I want... More knowledge (many sources) I want... More process (crm) I want... Sustainable process Data Information #DataBadLuck Knowledge Process
  40. 40. Questions? If you have questions or would like to begin a free trial, please contact us! Join the RingLead Usergroup +1-888-240-8088 sales@ringlead.com www.RingLead.com RingLead.com/LinkedIn Follow us on Twitter @ringlead @iDonato @michaelforce Become a Fan on Facebook RingLead.com/Facebook Subscribe to our YouTube Channel RingLead.com/YouTube #DataBadLuck

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