How to Gain Business Insight with Your Own Data

#Datafabric

The New Frontier: How to Gain Insight with Interwoven Quality DataWebinar with Jake Dolezal, Mike Franko & Donato Diorio

Data as a Story

✤ Data is an asset

✤ Data tells stories

✤ Humans have used stories as our principle means of communication for thousands of years

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Each system tells a story

✤ CRM tells customer stories

✤ ERP tells enterprise stories

✤ Point-of-sale transactions are vignettes of customer purchases

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Quality matters

Tell the…

✤ Most complete

✤ Most accurate

✤ Most consistent

Story possible#Datafabric

I have great quality data, so now what?

✤ Even with data of the highest quality, it still only tells a small isolated story.

✤ An eye-witness account is biased by its limited point-of-view.

✤ It may be correct, but it is only one perspective

✤ But what about the other interactions that lead to that purchase? What happened? Who did they talk to? What did they see or hear? What influenced them?

✤ Don’t be shortsighted and only see part of the story

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Bringing data together…the old way

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Conventional integration cannot keep up!

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Introducing the Data Fabric…

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Single point of entry for all data

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Data Practice Evolution

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• Companies  solve  the  problems  themselves    • Focus  on  symptoms  vs.  cause    • Every  project  starts  anew  • Very  little  automation  • Service  costs  are  astronomical  • Over  time  &  budget  

The  first  rule  of  any  technology  used  in  a  business  is  that  automation  applied  to  an  efficient  operation  will  magnify  the  efficiency.  The  second  is  that  automation  applied  to  an  inefficient  operation  will  magnify  the  inefficiency.    -­‐Bill  Gates

Data Evolution: Do-it-yourself era

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• Focus  is  still  symptom  vs.  cause  • Product  is  reviewed  more  than  the  

problem    (whether  is  it  dedupe,  data  fill,  etc  )  • Product  fit  is  the  focus  • Gaps  in  the  product-­‐solution  are  

ignored  • Leads  to  partial  solutions  • Less  consultative  sales  process

The  problem  always  looks  like  a  nail  when  you  only  have  a  hammer.

Data Evolution: Product era

Data Evolution: Consultant era

• Consultants  are  product  specialists,  process  generalists  

• Learning  on  client’s  time  • Abundance  of  “try  and  fix”  iterations  • Longer  process,  most  expensive    • More  expensive  than  doing  it  in-­‐house  • Gaps  in  the  product-­‐solution  are  ignored  • Failure  rate  is  similar  to  early  CRM  

implementations

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Data Evolution: Expert era

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• Experts  in  product  and  process    • Quicker  process  • Less  expensive  than  doing  it  in-­‐house  • “Try  &  fix”  iterations  much  less  

common  • Success  is  common,  break  downs  

happen  when  advice  is  ignored

Data Evolution: Assessment era

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• Data  assessment  starts  the  process  • Decisions  based  on  data  facts  vs.  

expert  hunches  • Fewer  “try  and  fix”  iterations  • Assessment  provides  better  visibility  

into  solution  set  required  • Fastest  time  to  start  and  complete  

project  • Once  you  assess,  ready  for  expert!  

9 lost secrets of Clean Data

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What is Clean Data?

Minimalist:    Only  what  you  need,  uncluttered  

Integrated  :  Supportive  of  your  CRM  

Complete  :  URL,  emails,  address,  points  of  contact  

Expandable  :    Data  catalysts,  URL  &  social  links  

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Based  on  total  record  count  across  all  silos,  choose  an  appropriate  number  of  random  records  from  silos  to  perform  a  data  test.    What  is  the  state  of  the  data?

Company  record  completeness   (url,  address,  city,  etc)  

Contact  record  completeness  (name,  email,  phone,  bio,  etc)  

Contact  record  depth/company  (number  of  contacts  per  company)

Secret 1: Data Test

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A  look  at  CRM,  Email  systems,  lead  databases  and  any  silos  of  information  which  drives  the  business.    The  end  goal  is  to  have  a  solid  understanding  of:

Business  process  Vendors  used  Data  flow  Known  problems    Process  gaps    

Silo  interconnectivity  Silo  latency/data  age  Silo  normalization  Silo  record  count  Potential  problems

Secret 2: Silo Review (not just CRM)

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Develop  a  CRM  Data  Plan  which  is  crucial  to  the  entire  project.    The  CRM  Data  Plan  is  used  for  cleaning,  enhancing,  de-­‐duping,  and  eventually  protecting  the  CRM  from  additional  duplicates.    (Data  ShieldTM  )

Secret 3: CRM Data Plan

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Websites  are  the  future  backbone  of  company  data.  Fill  in  URLs  for  company  records.    Some  companies  have  multiple  brands  and  multiple  websites.    This  step  is  critical  to  keeping  company  and  contact  information  fresh.  URL  fill  is  critical  to  resolve  ambiguous  company  names  for  later  deduping.

Secret 4: URL fill

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Secret 5: Email Capture

A  large,  untapped  source  of  contact  information  and  connection  strength  lies  buried  in  email  archives.    Select  email  contacts  based  on  email  counts,  names,  companies  or  connection  strength.    Selected  contacts  and  companies  are  held  for  final  data  reintegration.

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Secret 6: Normalize your Data

Using  the  CRM  Data  Plan,    all  extracted  data  silos  are  normalized.    Normalized  data  is  ready  for  deduping.  

Deduping  will  be  600%  more  effective  if  Normalization  is  done  first.      

(6%  dupes  vs  <1%)

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Secret 7: Address correction

Provides  standard  address  correction.    This  step  can  be  done  before  or  after  the  data  load  back  into  the  CRM.    Very  large  address  appends  are  best  done  before  loading.

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Secret 8: Company ProfilingScan  the  public  web,  in  real-­‐time  for  contacts  at  each  unique  company.    Data  returned  includes  names,  titles,  emails,  phone  numbers,  professional  bios  and  social  network  links.

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Secret 9: Market Mapping

Add  segmentation  tags  into  the  account  record  of  your  CRM.    Industry-­‐only  categorization  is  not  enough  for  the  demands  of  marketing  automation  that  requires  segmentation  for  effective  campaigning.

tags

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Q&A

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