Data Quality and Integrity

Transcription

1 Data Quality and Integrity Mary Lou Cummings Dun & Bradstreet

2 Agenda Common Data Issues Adverse Impacts Contributing Factors Technology or Business Problem? Time for A Paradigm Shift Data Governance Strategy Evaluating 3 rd Party Referential Data Summary Biased Perspective

3 Common Data Issues Spacing Punctuation Abbreviations Spelling Naming Conventions Placeholder/Plugged Data (9999) Unique Characters Missing Information Data Entry Errors Errors Staleness/Decay Conflicting Content (Area Code=952, State=NY) Duplicates Others?

4 Common Adverse Impacts

5 Bad Company Address Returned Direct Mail Returned Shipments Waste, $ & 5

6 Bad Address Poor Campaign Results Communication Failures Waste & $

7 Bad Phone # Poor Sales Effectiveness Telemarketing Challenges Missed Sales

8 Bad Personal Data Missed Connections Missed Opportunities Waste & $ Missed Sales

9 Duplicates Multiple sales reps chase the same prospect Prospect confusion Counter-productive Channel conflict Commission conflicts Etc.

10 Partial Picture Hard to expand influence within an account Can t make strategic decisions Who really are your biggest customers? SGG Inc. $600K Corporate Headquarters Jones Jones Inc. Inc. $350K $350K (Single Location) Smith Co. Smith Co. $100K $100K (Subsidiary) Gorman Co. Gorman Co. $200K $200K (Subsidiary) Gupta Inc. Gupta Inc. $300K $300K (Subsidiary) Chen & Co Chen Co $150K $150K (Single Location)

11 Data is Suspect Low Adoption Taints IT Reputation Confusion Unproductive Discussions

12 Manual QA Labor Intensive Inefficient Human Errors

13 Contributing Factors

14 Contributing Chaos Data disparity across systems Organizational silos Multiple customers views Unstructured data within enterprise systems Exponential growth in volume and types of data

15 Exponential Data Growth

16 Corporate Data is Volatile!

17 Volume of Change in 30 Minutes # of Business Addresses? 120 # of Business Telephone Numbers? 75 # of Company Names? 15 # of New Companies Formed? 30 # of Companies that Closed? 10 # of CEOs that Leave Their Job? 20 * Source: D&B and Sales & Marketing Institute Based on 52 weeks, 5 workdays, 8 hour day.

18 Additional Factors Cloud Computing SaaS DaaS Growing Demand for Customer Intelligence Social Media Real-time Mobile Economy Adoption

19 Data Quality Technology or Business Problem?

20 Data Quality: A Business Investment A Business Issue, not a Technical Issue Strengthen the Business Operational Efficiency Competitive Position Pro-action vs. Reaction Strategic Decision-making

21 Data-driven World Sales Marketing Data Quality

22 BUYERS ARE IN CONTROL 70% of the B2B buying cycle is complete by the time sales come into the picture! Source: Sirius Decisions 22

23 The Cost of Poor Data Quality An average of $8.2M/company wastes from Squandered Resources Operational inefficiencies Missed sales Unrealized new opportunities Gartner Study 96% of addresses and contact data within CRMs are partially inaccurate D&B Analysis 12% of companies use data-driven intelligence to guide their corporate sales strategy

24 Cost of Poor Data Quality Correction 100x Resolution 10x Prevention 1x

25 Time for a Paradigm Shift

26 Case Study: Efficiency Food Distribution

27 Do you like to cook? 27

28 Imagine if you had to go directly to the source

29 It would be very labor intensive and highly manual.

30 And VERY time consuming. Possibly painful

31 Fortunately, you can get everything you need in one place

32 Where the quality is great and guaranteed to be fresh

33 And everything is organized, accessible and easy to find

34 Now you get what you need, when you need it delivered right to your front door!

35 Case Study Non Strategic Activity

36 Payroll 1949 Company founded 1961 IPO with 300 clients, 125 employees and revenue of $400K 1985 Passed $1B in revenue Today - $10B+ in revenue, 600K+ clients Why? Companies figured out that payroll was not strategic, just necessary.

37 Data Governance Strategy

38 Data Governance Strategy Point of Entry On-going Maintenance

39 Data Quality Standard Accuracy Completeness Timeliness + Cross-border Consistency

40 360 View Labor Intensive Inconsistent Quality High Cost

41 Stop the Madness

42 Improving Your Data A Single Source of Standard, Verified Information Eliminate Duplicates Correct Information Biased Current Information Perspective Leverage independently verified information 3 rd party referential data set Consolidate multiple sources of information Triangulate and verify Global unique identifier Persistent key links corporate entities automatically

43 Importance of Persistent Key Link to corporate family hierarchies Append additional data Demographic Contact Predictive Information

44 Who is Responsib for Data Quality? 44

45 Best Practices What A Business Issue, not a Technical Issue Strengthen the Business Operational Efficiency Competitive Position Pro-action vs. Reaction Strategic Decision-making How C-level Sponsorship Cross functional Line and IT Experts IT Plays a Consultative Role Where Biggest ROI Opportunities Credit Management Vendor Management Sales Marketing Business Development When Ongoing/Continual (DaaS)

46 ROI Metrics that Matter Key Performance Indicators (KPIs) Link data quality to ROI Focus on business processes with greatest impact to customers Decrease costs Increase revenue Drive profitability Combination of tangible and intangible

47 Sample Data Quality ROI Reduce marketing campaign costs by Precise targeting Better prospect nurturing Relevant and timely information

48 Data Quality Scorecard Improvement Dimension Benchmark Baseline Target Annual Impact Reduce Data Latency 5-10 days Reduce Duplication 10-20% Improve Deliverability 4-5% Improve Response Rate 1-2% Shorten Sales Cycle days Reduce Error/Defect Rate 3-5% Increase Pipeline Close Rate 2-5% Increase Up-sell/Cross-sell 2-3% Increase Customer Retention 4-5% Reduce DSO 2-3 days Reduce Comp Adjustments 3-5% Reduce Vendor Costs 10-20%

49 The CRM Data Quality Imperative 70% of CRMs in Europe fail to meet objectives Contributing Factors Not planning for change Not defining clear objectives Failure to align the business operations across sectors Poor data quality Poor user buy-in Not attaching measurements to objectives No incentive for employees to use the system Insufficient training Over customization of CRM

50 Deliver on the CRM Promise Clear picture of Today s reality Future potential All customers All geographies All industries Faster, more informed decision-making Streamlined, value-added processes Improved financial insight and profitability

51 Inherent Challenges Legacy System Silos Exploding Volume of Data Diverse, Numerous Types of Information Sources Human Factors Complexity Recommendation: Get Outside Help Biased Perspective

52 Evaluating 3 rd Party Referential Data

53 Referential Data Source Key Components Coverage Global Depth Consolidation Linkage Predictive Indicators APIs

54 SIC & NAICS Codes SIC = Standard Industrial Classification Developed in the 1930 s by the U.S. Government. Widely adopted standard 4-digit coding system used to classify, define and analyze industry structure. NAICS = North American Industry Classification System Replaced SIC and accommodated North American perspective (beyond US borders) Primary Use Segmentation analysis and targeting 54

55 SIC Example A company that sells only to drafting material manufacturers. Code Digits Description # in DB 39 2 Miscellaneous Manufacturing 78, Lead Pencils & Art Goods 1, Artists Equipment Drafting Materials 32

56 Summary

57 Key Components A Single Source of Truth Focus 1. Prevention 2. Resolution 3. Correction Repeatable, automated maintenance process Integrate/consolidate disparate data through the use of a unique, persistent record ID Maintain company data with periodic and/or real-time updates 57

58 Call to Action Make the Business Case Assemble Small, Cross-functional Team Executive Sponsor Key Business Unit Members IT Members Get Outside Help Leverage Microsoft Partners Capitalize on 3 rd Party Referential Data Understand Current State Establish Data Governance Strategy and Tactics Initial Overhaul Long-term Maintenance Plan Prepare Organization Communicate Train Carrots instead of Sticks

59 Additional Reading CRM, Data Quality and the Cloud EnterpriseAppsToday Successful CRM Hinges On Data Quality DestinationCRM.com What Price CRM Data Quality? CIO.com Data quality: 'The cornerstone of CRM ComputerWorld The Bane of CRM: Data Quality InfoManagement Direct Quality data does NOT guarantee quality information, but quality information is impossible without quality data. Unknown

60 Questions? 60

61 Thank You Mary Lou Cummings