The science of data quality salesforce user group

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The ideal of perfect data is is something that needs to be understood. Donato presents the underlying factors that data quality depends upon.

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The science of data quality salesforce user group

  1. 1. The  Science  of  Data  Quality Donato Diorio Founder & CEO Broadlook Technologies www.broadlook.com
  2. 2. Key  trends  in  CRM Social Cloud Metrics/ Dashboards Big data & sales intelligence Mobile Analytics Collaborative selling Empowered Customers
  3. 3. Everything     depends  upon  
 Data
  4. 4. The  Evolu=on  of  Sales  Desire I  want...More  data(lists)   I  want...  Be3er  selec5on(databases)   I  want...  More  contacts  per  company(zoom)   I  want...  Fresher  contacts(Jigsaw)   I  want...  More  informa5on(LinkedIn)   I  want...  More  knowledge(many  sources)   I  want...  More  process  (crm)   I  want...  Sustainable  process Data Information Knowledge Process
  5. 5. Data  decay  metrics
  6. 6. Data  decay  happens • Change  in  5tle,  promo5on   • Change  in  working  loca5on   • Change  of  phone  number   • Add  mobile  phone  number   • Change  of  department   • Change  of  area  code   • Change  of  email  format   • Merger  or  acquisi5on
  7. 7. Data  industry  response • • • • • • • • • Buy  data  from  mul5ple  sources   Refresh  with  editors     12  month  cycle  (top  10K  companies)   12-­‐18  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
  8. 8. Giving your CRM data a score Data  Quality
 X Compe==ve   Advantage
 =  CRM  Success
  9. 9. CRM  Data  Quality Points Factors 4 3 2 1 <30  days <60  days <90  days <180  days 95% 80%  + 70%  + 60%  + Multi-­‐venue All
 available Basic  +  2   social Basic  +  1   social Basic
 (email+phone) 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 Fresh Accurate Total data quality score: Your score
  10. 10. CRM  Compe==ve  Advantage Points Factors 4 3 2 1 Targeted target  by  self   description hand  built keywords SIC  code Custom built  on-­‐ demand Complete 95%+ mashed  from   pulled  from   many  sources larger  sample 80% 60% 40% Exclusive no   anyone  can   limited  access competitors buy  access Transparent Sources   transparent sources   known sources   available By  a  person Marke5ng
 automa5on email      Verified free Total competitive advantage score: Your score
  11. 11. Compe==ve  Advantage Where  is  your  CRM  data? 24 12 0 12 Data  Quality 24
  12. 12. Quadrant  Key ! Qualita5ve  / Event  driven ! Qualita5ve Cyclic Quan5ta5ve  / Cyclic Quan5ta5ve/ commodity ! ! Compe==ve  Advantage  Data  Quality  &  Compe==ve  Advantage 24 Influence Rela5onship n io at m to au g in et rk a 12 CRM+
 90  days new! CRM! lead CRM+
 180  days m CRM+   360  days Cold  Call 0 Warm  call 12 Data  Quality 24
  13. 13. A  data  experts’  advice... “I want my list built on-demand, generated based on how companies describe themselves. It needs to be 95% inclusive of my target market. I want 3 points of contact for each company, multiple ways like email and social networks to reach out them and you can’t sell this list to anyone else”
  14. 14. CONNECT with Donato Email: ddiorio@broadlook.com Twitter: @iDonato LinkedIn: linkedIN.com/in/donatodiorio Blog: www.iDonato.com www.broadlook.com

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