Best Practices For Data Quality 11736z

Best Practices for Data Quality

Salesforce.com Customer Success March 2009

Agenda  Business Driver  Best Practices Overview

 Importance of Data Quality  Data Quality Management – Data Culture, Analyze, Plan, Standardize, Clean & Enrich, Integrate & Automate, Maintain

 Tools and Resources  Additional Information: Data Considerations – De-duping, Merging, Migration, Integrations & Mapping, Reporting, IDs

Business Driver  All organizations buy a CRM tool to derive clear quantitative metrics on their business. Having bad data causes frustration, poor adoption, and may lead to bad decisions due to inaccurate reports/metrics. The drive to have accurate data for an organization is critical since it can provide better and accurate visibility to increase revenue, reduce costs, increase customer profitability, and usage. It is important to understand Data Quality Management best practices using Salesforce.

Best Practices Overview  Every successful implementation of Salesforce should have accurate data quality as a CRM goal. This is the key in generating the right metrics and truly understanding your customer. This presentation touches on all of the aspects of creating and maintaining good data quality.

Importance of Data Quality Pitfalls of Bad Data

 Inaccurate report metrics  Bad information wastes s time and effort  Marketing wastes money and effort pursuing bad prospects

 Understanding your “customer” is impossible  IT wastes time sifting through information and trying to make sense of it

 Operations has difficulty reconciling data against financial and other backend information  get frustrated, you lose valuable buy-in and adoption Analysts rate bad data as one of the top 3 reasons for CRM failure

Importance of Data Quality The Cost of Bad Data

75% of respondents

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75% of commercial businesses believe that they are losing as much as 73% of revenue due to poor data quality

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Experian - QAS U.S. Business Losing Revenue Through Poorly Managed Customer Data

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41% of costs U.S. businesses more than Poor datarespondents quality $600 billion annually

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Data Warehousing Institute.

Data Quality Management Best Practices

Data Quality Management Best Practices  Data Culture  Analyze  Plan  Standardize, Clean & Enrich  Integrate & Automate  Maintain

Installing a Culture of Data Quality 1

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Introduction

Adaptation

Standardization

Anything goes, adoption before data integrity

Recognize usage trends, Adapt standards to reality

Train to common « best practices »

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4

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Automation

Integration

Reward / Repression

Make everybody’s job easier, and make the company more efficient

Build tools to help multi department tasks / processes

Reinforce best practices, with a carrot AND a stick

Analyze: Data Profiling  Understand your data sources – Where is everything coming from

 Understand your data’s weaknesses – Rate your data; consider completeness, accuracy, validity, relevance, integrity, level of standardization and duplication – Pinpoint your problems and find ways of improving this

 Understand your mapping and usage of data – Entity Level Mapping (, Opportunity, ) – Field Level Mapping (state, city etc) – Don’t duplicate information between entities

Data Quality Analysis Example: Phone Numbers Not valid

Not complete

Not standardized

Plan: Data Quality Management Strategy  Create your Data Quality Plan  Identify and Prioritize Goals  Define Reports and Dashboards  Find Sponsors and Owners  Establish Budget  Select Tools (i.e. for De-Duplication)  Commit Resources

 Create Communication Plan  Provide Rewards and Disincentives

Standardize, Clean & Enrich 1

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Standardize

Cleanse

Enrich (Optional)

De-dupe

Validate

Names

Find & Replace

Company Name & Address

Identify, Match & Score

Load to Sandbox

acme incorp.-> Acme Inc

Hot  High Cold  Low

Addresses

Naming Conventions

US, U.S, U.S.A -> USA

Postal Standards

Acme-Widgets-453

Data Transformation Mergers, acquisitions, spin-offs

Archiving & Filtering

J. Smith, John Smith – 80%

Hierarchy Data

Merge

Acme Inc HQ Acme UK

J. Smith, John Smith -> John Smith

Demographics

Re-parent Child Records : Division, Opportunity,

Validate & Modify

Load to Production

Standardize  Create naming conventions and data standards and train all s  Enforce standards with validation rules and pick-lists  Implement procedures to standardize data before mass-importing

 Examples: – s names: Inc vs. Incorp., INC, incorporated; Ltd vs LTD, Limited – Opportunity names: i.e. Name – Product: “Acme – 250 Tschotchkes” – Country/State: use validation to standardize TX vs Texas, USA vs. U.S. – Postal Code: use validation rules for proper format in US/CAN: xxxxx-xxxx – info: use pick lists for roles, titles, department: Marketing vd. Mktg

Look for useful validation rules in Help & Training!

Cleanse  Cleanse your Data – Correct inaccuracies and inconsistencies – Find and replace bad or missing data – Remove or merge duplicates – Leverage all s to fix data (it’s their data) – Archive irrelevant and old data – Leverage automated routines/tools – Routinely reconcile Salesforce data against other data points/systems

 Prioritize your data control process – Fix high visibility/usage information first (duplicates, addresses, emails) – Fix business specific information next (opportunity types, stages etc)

– Remove duplicate fields (don’t repeat info on ) – Remove irrelevant fields

Enrich: Data Augmentation  Add missing information from 3rd party sources – Phone, emails, address info, executive information, – Company demographics, i.e. SIC, Industry, Revenue, Employees, Company Overview, Competitors, Fiscal Year

 Understand what data would provide additional value – Poll your sales and marketing s and see what is needed

 Add internally available intelligence – Order history – Purchasing Pattern

– Up-sell opportunity, i.e. products not yet owned

Integrate Acct Master based on lifecycle

s • Quick Arrow • View Central • Siebel

Product • SAP • Oracle (Custom)

Pricing • SAP

Data Warehouse • ???

 Understand your Masters

Leads/Oppty

• Catapult • IMI • Volume • View Central

Internet

Integration Tools

– Master (Unique ID stored on all other systems)

EAI/Middleware • Tibco, WebMethods (Alcatel) • BizTalk

ETL • Assorted

Standards based Integration • SOA/Web Services • XML

Internet

Internet

SFA Data Enrichment

– Product Master

 Avoid stale and bad information from spreading – Integrated solutions make it easier for s and more reliable for customers

– Create links or integrated apps to avoid duplicates in many systems – Use and monitor ‘review dates’ for key objects, i.e. plans – Archive or flag old/irrelevant data, i.e. s not updated in last x months – Use workflow/approval processes before updating key fields

 Create a true “360” view of your customer – Link order entry, fulfillment apps to Salesforce.com

 Make some information read only – Use processes like “case submission” to update master information

: Five paths to integration success A comprehensive family of technologies built on top of the Force.com Web Services API

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Salesforce AppExchange

Native Desktop Connectors

Native ERP Connectors

Integration Partners

Developer Toolkits

Automate  Salesforce.com partners can help! – Leverage 3rd parties such as D&B, Hoovers and others to periodically import and automatically update records – Inside Scoop or other partners to augment and cleanse information

 Workflow can help! – Emails requesting missing information automatically sent to owner when a record is incomplete 

Force.com can help! – Generate your own alerts through the API – Script adds missing information – Script updates erroneous information

 Create integration points – Master/Product Master/Address Masters – Address Cleansing – Keep Relationships automated

Data Management Applications

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Force.com Appexchange app considerations list not all encoming

Low Complexity

Composite Apps • Enterprise Mash-ups • Rich interface

Application Integration • Real-time integration •Multi-step integration • Human workflow

Data Integration • Data migration • Data replication • Bulk Data Transfers

Data Cleansing • Data de-duplication • Data assessment

Scontrol

Medium Complexity

High Complexity

Data Quality Management Best Practices Native tools for managing data quality Web-to-X

Excel Connector

Data Loader

Analyze and cleanse data

Leverage tools to prevent duplicates before ing to Salesforce real-time

Features

Use Validation Rules and Workflow

Import data from various file sources

Data Quality Analytics

Use reports and dashboards to measure data quality

Maintain your Data Safeguard your cleansed data and prevent future deterioration

Train

• • • • •

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Training Naming Conventions Address Conventions Dupe. Prevention Process Data Importing Policies

Enforce

• • • • •

Required Fields Default Values Data Validation Rules Workflow Field Updates Web-to-Lead Restrictions

Monitor

• Data Quality Dashboards • Data Quality Reassessment • AppExchange Tools

Data quality decays rapidly & enterprises should follow a methodology that includes regular measurement of data quality with goals for improvement & deployment of process improvements & technology

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Maintain Data Quality: Train and Communicate 

s are trained that data integrity is a collective responsibility

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s are trained on how data will be used (establish reasons for why data needs to be clean and accurate)

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Communicate data quality goals and progress updates

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Communicate policies and procedures

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Data is always changing so Data Quality processes are on-ongoing!

Maintain Data Quality: Enforce 

Make sure Data Ownership and Sharing is accurate – Critical to keep data in the right peoples hands – Designate i.e. super or geography lead to own regional data quality – Make sure your hierarchy, groups, teams etc are kept up to date – Proactively have meetings with management and stakeholders to understand org changes

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Define your CRUD rights on each profile – Give s access rights to only the information they should have

Maintain Data Quality: Monitor  Use Reports and Dashboards to monitor and identify erroneous/missing data  Data Quality owners spot check and monitor data on a regular basis  Create Alerts and workflow to monitor data  Define centralized processes for mass loads

 Implement Procedures and Policies  Enlist everyone and hold them able  Exception reports run monthly to find incomplete records or records with incorrect pick list values

Improvement Checklist 

Do you understand what data you have in Salesforce? – Where is it coming from? What is wrong? What is the business impact?

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Have you cleaned your data? – Identify data owners, ensure permissions are up to date (CRUD) – Remove duplicates (manually and through tools or partners)

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Have you integrated and automated your data? – Do your applications tie together? – Are you using workflow for notifications? Are validation rules in place?

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Have you augmented your data? – Have you added information to help your sales s?

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Do you monitor your data? – Get the reports, dashboards and automation in place to monitor the health of your data

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Do you have a good data quality culture? – Is everyone trained and contributing to your data quality? Do s trust the data?

Tools & Resources 

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AppExchange - Data Quality tools and offerings –

Data Quality Analysis Dashboards

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Integration & Data Management

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Data Cleansing

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De-duplication Tools - Search term “Data Quality”

Salesforce.com Data Tools –

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Apex Data Loader and Excel Connector

Dreamforce Data Quality Sessions –

Data, Data Everywhere

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No More Bad Data

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Wrangle Data & Pump up the Configuration

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Turning Around your Data Quality Dilemma,

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Data Data Data: Start your Spring Cleaning Now

Salesforce Professional Services –

Data Quality Assessment and Cleansing Solutions

Thank You

Additional Information

Data Considerations 

Addressing duplicate records – There will most likely be overlapping/duplicate data – De-dupe either before or after you import the data from one system into the other • Prior to importing into master – Export both data sets, merge into one and identify duplicates

– Merge/delete duplicates, import clean file

• After importing into master – Leverage de-dupe tools in salesforce.com – Leverage de-dupe tools from partners (www.salesforce.com/appexchange) – Use a custom field to flag each records source system

• Establish controls and processes to minimize dupe creation and to remove dupes on an ongoing basis • Consider existing integrations and system of record for your data

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Develop rules for merging data – When there are two records for the same entity (i.e., ), which one ‘wins’? • Newest record? Most complete record? Record from one of the databases? Most recently updated?

– Determine who will own the records if there are duplicates • Impacts sharing rules, reporting, etc. • Leverage for data cleansing that will ensue

Data Considerations  Establish plan for migrating data – Determine when master system becomes live/system of record (i.e., stop entering data into other system) – Set date when you will extract all data from the system being merged – How long will the merge take? How will you deal with interim data? New data blackout dates? Temporary data ID? How will you communicate to s? – Ensure you have a complete copy of both data sets before attempting any merging … just in case!

Note – if you have not done this type of work before, it is challenging.

Data Considerations  Create mapping tables – Every record in Salesforce is assigned a unique 18-digit alphanumeric, case sensitive id by salesforce.com – Relationships between records are established based on these IDs (i.e., Activity related to a ) – These IDs will change when you import data from one system to another, as the system will assign it a new ID – In order to re-create the relationships between records (i.e., import Activities and associate to the appropriate ), you need to create a mapping table that will allow you to associate the OLD ID with the new one

Data Considerations 

Create Mapping Tables (cont.) – Create a temporary/mapping field on each object you will need to map for the old id (i.e., OLD ID, LEGACY ID)

– Export all your data from the instance to be retired • You can do this via the Weekly Export service, reports, the API, Excel Connector, AppExchange Data Loader or request a one-time full extract from customer • Don’t forget about attachments and Documents! – Consider “dumping” these to a file server with a unique naming strategy and use Custom Links from the salesforce.com objects to access

– When importing the data into the master , map the Id to the OLD ID field – You will then be able to export the new Id, OLD ID and Name to act as your mapping table

Data Considerations 

Created Dates –

All records imported/migrated will have a Created Date = to when the import occurs

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To retain original dates, create a custom field to import into (i.e., Original Create Date)

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If you are updating via the API, the new 7.0 version will allow you to set the Created and Last Modified Dates: http://www.sforce.com/resources/tn-17.jsp Note: You must Salesforce to enable this feature.

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History Tables –

Stage History for Opportunities / Case History for Cases

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Data cannot be migrated into these tables, this information must be stored elsewhere if you bring it over (“Note” field is not Reportable, so custom field is recommended)

Unique Ids (system generated) –

Record Ids are unique and cannot be imported

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Imported records are assigned new Id, it is a good idea to import the old Id into a custom field for mapping purposes

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Features that reference (i.e., Custom Links) unique ids of other objects (i.e., a report) must also be updated

Data Considerations 

Reports – When reporting on migrated data, date filters must take into standard and custom date fields (i.e., Create Date and Original Create Date) – Other filters on existing reports must be reviewed to ensure they are still relevant/apply to all data

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Record Types (EE/UE only) – If one of the salesforce.com instances leverages record types, all records added from the other instance must be assigned a Record Type – Record Types can be updated through the API, not through the import wizard

– Record Type assignment must also be aligned with Profiles

Data Considerations 

What if data is inadvertently… – Deleted • Restore from the Recycle Bin (retained for 30 days) • Restore missing data from backups

– Merged • There is no way to “un-merge” data • Clean up/work with merged records, OR • Delete and restore from back ups

– Imported incorrectly • Mass transfer (if you can) • Delete and re-import into proper area • Consider tagging batches with a custom field indicating the load/batch number in case you need to reverse

Advanced Data Quality