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Salesforce1 data gov lunch anaheim deck


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Salesforce1 Data Governance Lunch

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Salesforce1 data gov lunch anaheim deck

  1. 1. Best Practices for Data Governance and Stewardship Inside Salesforce Beth Fitzpatrick, Director Product Marketing, David Jenkins, VP Data Intelligence, Traction on Demand
  2. 2. Safe Harbor 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, 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 product or service availability, 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. The risks and uncertainties referred to above include – but are not limited to – risks associated with developing and delivering new functionality for our service, new products and services, 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 any litigation, risks associated with completed and any 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 products, and utilization and selling to larger enterprise customers. Further information on potential factors that could affect the financial results of, inc. is included in our annual report on Form 10-K for the most recent fiscal year and in our quarterly report on Form 10-Q for the most recent fiscal quarter. These documents and others containing important disclosures are available on the SEC Filings section of the Investor Information section of our Web site. Any unreleased services or features referenced in this or other presentations, 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., inc. assumes no obligation and does not intend to update these forward- looking statements.
  3. 3. Who Do We Have Here Today? Who Owns Data in Your Organization? Sales Marketing IT Support Data Operations Sales Operations
  4. 4. Governance and Stewardship Common understanding Rules/policies that are designed to maintain data order. Quality, management, policy, risk management Thresholds and Measures Rules and Systems Assignments/actions and personas designed to uphold data governance Obligations and role responsibility Motivation to participate. Culture
  5. 5. David Jenkins VP Data Intelligence – Traction on Demand
  6. 6. •  Downstream “Target” Why do we care about data? •  Upstream “Source” Where is it from? Motive Trust Knowledge Intent Where is it consumed Timeliness Usage Insight Action
  7. 7. •  Getting ahead with –  Integration –  Analytics –  Stewardship/Governance •  Getting ahead with –  API –  Advanced use cases –  Building data from change Why do you care about Data? •  Getting started with Salesforce –  Cleansing –  Migration –  Adoption •  Getting started with –  Record creation –  Record management –  Introduction
  9. 9. Let’s talk about data quality
  10. 10. What Challenges are You Facing Today?
  11. 11. What We Have Found With Customer Data Name Phone Bob Johnson 415-536-6000 Bob Johnson 650-205-1899 Rob Johnson 415-536-6100 Bob C. Johnson 408-209-7070 Bob Johnson 415-536-6000 Rob Johnson 650-205-5555 Bob T. Johnson 650-780-9090 Robert Johnson (415) 536-2283 90%Incomplete 74%Need Updates 21%Dead 15+%Duplicate 20% Useless
  12. 12. The Ever Changing World of Data Source: D&B Sales & Marketing Research Institute 120 businesses change addresses 75 phone numbers change 30 new businesses are formed 20 CEO’s leave their job 1 company gets acquired or merged In 30 minutes
  13. 13. Data Governance Drives Quality Data So You Can Confidently ….. Whitespace Analysis / Cross-sell & Upsell Market Analysis & Customer Segmentation Territory Planning & Alignment Prospect & Target New Accounts Lead Scoring & Routing Revenue Roll-Ups
  14. 14. Data Governance is an Investment (vs. Expense) Where you choose your investment goals, manage your risks Source: DAMA DMBOK Data Management Functions Environmental Elements Data Governance Goals & Principles
  15. 15. Moving from talking to doing
  16. 16. Assess -  Get a sense of the state of your current data -  Who are your users – reports/adoption -  What fields are being used - fieldtrip -  What do they do – integration/workflow/dependencies/docs/conga etc. -  How is the overall quality – 3rd party, self check -  What do your users “use” it for – ask them/stalk them -  What tools are dependent – Integrations/downstream -  What analytics are important – dashboards/reports/BI Goal: get inventory and current state
  17. 17. Clean It Up -  Initiate some “level 1” cleansing -  Standardize outliers (normalize) -  Self append (inferred fixes) -  Baseline duplicate management (careful of dependencies/history considerations) -  Kill useless records – FHD – Flag,Hide,Delete -  3rd party append (internal and external) -  Advanced duplicate management Goal: get your baseline in order
  18. 18. Develop a strategy -  Two choices – distributed or managed -  What will work within your “culture” today -  What is sustainable looking forward -  Recommendation – develop a distributed data management model Goal: get your baseline in order
  19. 19. Levers •  Forced business processes – contract generation/automated replies/dashboards •  Entitlement and ownership – labeling, ownership, naming •  SWAT team – call for help – tactical support team •  Gift of time •  Gift of focus and analytics •  Gift of assignment X
  20. 20. Guiding Principles
  21. 21. Data Quality Guiding Principles •  Know where you’re going and make hard decisions on priorities. •  Ownership: Clear ownership of core data. •  Definitions: Widely understood definitions of account, customer etc. •  Objectives: Agree on areas of focus and how it will be used. 1. Agree on a Clear Vision and Ownership •  Highlight focus areas for data quality in the system. •  Flag governance status and quality score clearly. Use icons. •  Leverage validation rules, record types, profiles and dependent pick lists. •  The “Give” (and take). 2. Articulate Priorities
  22. 22. Data Quality Guiding Principles •  Give users the tools to be successful. •  Search before create. Warn if duplicate. •  A common key adds power: D-U-N-S •  Easy enrichment: MDM,, Address Validate. •  Empower reps: social stewardship. 3. Ensure Usability at Point of Entry •  Governance and Stewardship teams support quality. •  Monitoring and approval of key information : Several approaches •  Management of bulk-loads. •  SME/ Gatekeeper for integrations. 4. Have Experts Support the Process
  23. 23. Data Quality Guiding Principles •  Get rid of the noise. •  Develop and apply an archiving policy (ie both at account and overarching level). •  Regular de-duplication cycles based on pre-agreed scenarios (eg CRM Fusion demandtools initially then dupeblocker). •  Conduct regular field audits (eg fieldtrip, Traction Field Audit Tool). 5. Conduct Regular Housekeeping •  Foster a culture of Data Stewardship. Celebrate success. •  Define measures and score – automatically. •  Report and stress single KPI – by org, BU, User. •  Measure improvement over time. 6. Measure . . . And Hold Accountable
  24. 24. Tactical Examples
  25. 25. Getting Tactical Moving from talking to doing: •  9 declarative elements in SFDC that are excellent governance/stewardship enablers Check the blog for additional details
  26. 26. Data Quality Security What: Leverage SFDC field level security to restrict access to certain data validation fields. IE approval status, record condition. Why: Allocate responsibility in determining what is “trusted” to a certain group of people. Hide fields to enable usability. How: • Set up custom profiles for ALL – catalogue access • Manage Field Access • Then create Permission Sets Hide/Restrict access to certain fields that are strategic in nature
  27. 27. Data Quality Validation Rules/Dependencies What: Block the ability for users to enter misaligned values via validation rules. Leverage rules to create gentle blocks and encourage correct process. Why: If you give people workarounds, they’ll use them. Typically workarounds = bad data and no governance How: •  Conditional Validation statements using mixed AND/OR •  English: if the record type is Prospect and the state/prov is empty require it. •  Give GREAT explanations and embed brand
  28. 28. Data Quality Record Types/Layouts/ Visual Indicators What: Use record types to segment an object based on status to ensure only relevant information is presented based on stage in process. Why: Don’t show users information that is meaningless within the context they are operating. - RT/Layouts by status - RT/Layouts by type How: •  Establish your profiles •  Establish your types of records (account type) •  Establish your status/progress by type •  Use icons to clearly indicate stage/ quality •  Determine what is relevant by type/status •  Develop custom page layouts for each •  Create WF to auto move RT based on defined actions
  29. 29. Data Quality Dependent Picklist Fields What: Only show relevant values on a particular record. Don’t give users incorrect choices Why: Noise. Makes your system look poorly thought through. Easy logical fix How: Set up profiles Set up record types Create fields, assign values by RT Create additional dependent fields, follow same path Use Excel to map your matrix out.
  30. 30. Data Quality Approval Workflows What: Prior to record lock, or pass over to integration leverage approval workflow as final gate. Why: Not all data gets migrated Apply expensive resources to sample Ensure data that is propagated is good How: •  Set up profiles •  Set up record types •  Set up page layouts •  Set approval workflow. Apply submit for approval button to specific layouts. Block progress without approval via validation.
  31. 31. Data Quality System / User Fields What: Create custom fields to allow users to enter basic information without disturbing sync data. Leverage formula fields to differentiate Why: Battle user frustration Open up usability without losing DQ Small step in managing biz expectation How: Save standard fields for native synchronizations and leverage custom fields for variable data.
  32. 32. Data Quality Add a Data Quality Score What: Establish a basic point scoring formula to provide data quality ratings on records Why: Expose your “trust” in a record and detach the typical link between data quality and adoption. Set user expectations on records Create positive motivation to improve How: Create a single formula field to score completeness from priority fields Conditional statement that evaluates: - Consistency - Recency – last changed, last activity - Completeness - No duplicates - 3rd party validation - Represent point ranges with a graphic – one score - Use Analytic Snapshots to measure over time - Report by Rep for accountability
  33. 33. Data Quality Kill Suspects What: Simply put, most systems have 2x the data they need. Clean house! Why: Eliminate noise Give ownership to users Invest resources in high profiles prospects How: Never delete first 1.  Isolate suspects 2.  Flag for elimination and color code 3.  Hide with security 4.  Wait 5.  Backup 6.  Delete !! Warning. This record has been flagged for deletion. Please update details with complete information by #formula to prevent removal.
  34. 34. Data Quality De-dupe What: Follow a consistent method/ process when de-duping and NEVER deter Why: Duplicates are easy to eliminate, and very expensive to restore should you have made a mistake How: Main Order 1.  Accounts vs Accounts 2.  Contacts within Accounts 3.  Contacts between Accounts 4.  Accounts vs Accounts 5.  Leads 6.  Leads to Contacts Search before create Address correction
  35. 35. Data Quality Make it Easy What: Consider how record generation be easy and convenient. Why: If data entry is easy and there is value in entering details, supports workflow, people will do it. How: Search before create – DDC API applications Address tools Clicktools forms to flatten SFDC record generation Experian QAS/ Postcode Anywhere Workflow to infer values Social search