Customer Feedback Forum: Data Governance and Stewardship Best Practices

Beth Fitzpatrick, Director Product Marketing, Data.com

David Hughan, VP Professional Services, Data.com

Eric Kasserman, Solution Engagement Manager, Data.com

Christina Belding, Solution Engagement Manager, Data.com

Driving Growth and Success through Business Strategy

Company

Strategy

Profitable growth by leveraging

existing customers and

products and expanding

internationally

Data Strategy

Have the right accurate data at the

right moment to drive growth,

optimize business process, and

scale

Getting Started with Data Governance

There is SO much to do, where do we even start?

Keep it simple, there is no magic bullet and things WILL change as you go through this

journey

Step 1-

Assess Your Entire Portfolio

Catalog Your Assets

1 2 3

Step 2-

Understand Your Options

Not every application should be run

in Salesforce.com

Step 3-

Plan Your Approach

Create Your Team, Create Your

Plan, Validate It And Socialize

Ok, so a little more than just 3 steps…

It’s really not hard to do, once you know where to start

1- Catalog all portfolio and data assets

2- Identify the 6-18 month out treatment plan for each

3- Identify current state “health and wellness” of data

4- Identify future state “health and wellness” of data

5- Identify ancillary modules or capabilities that will be used

6- Identify data volumes and paths (new records, updated records)

7- Identify business stakeholders, IS stakeholders, organizational stakeholders

1

2

3

Data Assets – They Are YOUR Assets

You own them, manage them, care and pay for them, but they grow and need your guidance

For each data asset, determine the following Considerations

What kind of data is it? Public, proprietary, private?

Master record, transactional record (dated), structured, unstructured,

etc.?

Who is the business audience that consumes the data? What level of importance is the asset to them – functionally speaking?

Who owns the data (authorship) Does this data allow you to be actionable- if so how?

Where does new data come from (primarily)? Is the data fed in from other systems?

Where do updates come from-primarily? New record updates, data enrichment from 3rd parties, etc?

How much data do you have, how fast does it change, what different

varieties exist….the answer is the trustworthiness of the data.

Data volume, velocity, variety= veracity (trustworthy)

Data- Lifecycle and Process

These areas reflect common touch points in the data lifecycle

Acquisition

Quality

HygieneGovernance

& Stewardship

Reporting & Dashboards

•Document all sources- streamline manual and offline

record creation

(Manual, purchased lists, spreadsheets, W2L, ERP, etc.)

•Structured hierarchies using DUNS number

will help you see the full picture of an

organization and where you penetrate.

•Define ‘must have’ and ‘nice to have’

attributes that define your records.

•Matching and cleansing with trusted external

sources is paramount.

•On-going process that requires a strategy; data

goes bad over time – use automated cleansing

routines (off-line even if large data sets are involved)

•Work with stewardship team to divide and conquer

•Define roles for governance team

•Define the company’s data assets

•Determine integration points and

paths- make sure to identify which

systems are masters

•Identify if there are any legal,

compliance or security restrictive

actions

•Data should be reportable

•You should be able to

track inbound data

•It’s difficult to manage

what you cannot measure

Portfolio Assessment

• Catalog Applications First

– Identify core functionality –what is the value to the organization?

– Identify current state health and wellness

– Identify on-going financial costs (licensing, support, etc.)

• Identify Consumers, Users, Stakeholders

– What people use the application, how many are there?

– Who and how many support resources are needed?

– Who and how many development/IT resources are needed?

• Create Optimum Future State Direction

– Even if you can’t do it for a while, include it for consideration

What exactly does this mean- what to include?

General Functionality Data Support / Operational

Questions To Aide In Your AnalysisThese critical areas should also be cataloged when evaluating your portfolio

• What area of your business does the

application currently provide value for?

• How long has it been since a significant

update was made to the application’s

capabilities?

• Is the application needed for a regulatory,

legal or compliance purpose?

• Does the data in the application provide

critical information that still meets your

business growth needs?

• How do new records get added, how are

records updated?

• Can you extract data easily?

• Does data from the application feed into

another system – does it receive data from

another system?

• Would you need to provide additional support

to users if changes were made?

• Is it currently difficult to obtain resources to

provide support?

• Is the application evolving and getting

updates, or is it staying still?

• Does the application contain highly sensitive

or proprietary information?

Governance- DMBOK (Data Management Body Of Knowledge)

Data Governance – At The Organizational Level

• BoardDirection

• Owners

• Enterprise Architecture

• Change ManagementGovernance

• Data Quality

• Master and Reference Data

• Reporting and Analytics

• Data Management/Architecture

• Models and Meta-Data

Management

• Working Groups (SMEs)

• Domain Working Groups (SMEs)

• Business Intelligence

• Repository / ETL Tools

• Enterprise Applications (Security, Compliance, Risk Management, Lifecycle Management)

Teams

• Stewards- Quality Analysts

• Stewards- Custodians

• Business Analysts – Providers

• Architects

• Modelers & Analysts

Roles

Governance Structure – Processes and Activities

Governance Committee

Council & Organization

Terms and Definitions

Working Groups

Alignment Liaison

Roles and Responsibilities

Owners

Stewards

Custodians

Data Governance Office

Data Management

Policies and Processes

Principles

Policies

Standards

Processes

Program

Maturity Matrix

Strategy

Scope

Business Case

Implementation

Reporting and Assurance

Performance Measurement

Continuous Improvement

Evidence Repository

Communications

Governance Structure – People (Organizational Areas)

Governance Committee

Sales

Customer Management

Lead to Opportunity

Quoting / Ordering

Sales Operations

Price Books /

Pricing Strategy

Pipeline Reporting

Territory Planning

Data Acquisition

Marketing

Lead Generation

Content / Publishing

Events

Branding

Marketing Ops

Data Acquisition

Event Planning

Event Reporting

Reporting/ Analytics

Customer Intel

Information Systems

Data Model

Data Quality

Analytics

Support

Application Development

Executive

Strategic Vision

Product Direction

Services Direction

Organizational Growth

Funding

Meet Your Governance Team – Data CustodiansQualities for Data Custodians

Understand the importance and criticality of the data assets in their purview. They are often super users or core SMEs and

can articulate details to others of ‘how things work’.

Social/Personal:

Work with average end users to correct data, and or make recommendations to prohibit recurrence of problems

Ability to converse with data stewards to effectively problem solve

Are collaborative- can work with others to ensure buy in and effective change

Technical:

Understand high level data model; can define and potentially write reports for data assets they understand, but they may

not be super technical (don’t expect them to be developers).

Not afraid to get their hands dirty (updating data when necessary)

YOU WILL NOT GET A CAPE, TROPHY, RAISE OR PUBLIC RECOGNITION

AS A DATA CUSTODIAN

Meet Your Governance Team – Data StewardsQualities for Data Stewards

Understand that Data Stewards are those business people within your company that can provide the knowledge behind

many of your applications. They often know the reasons ‘why’ things are done ‘that way’.

Social/Personal:

They work well with end users (correcting data, isolating problem areas) as well as with custodians and other stewards and

executives. Great data stewards are good communicators, but are even better facilitators.

Ability to converse with mid-line management on potential risk factors

Technical:

Usually you will not be getting your hands dirty, however may need to be involved in helping data custodians validate

changes and prioritize elements as determined by the governance board.

Data Stewards should be knowledgeable about basic report writing and understanding complex models in the organization.

YOU WILL NOT GET A CAPE, TROPHY, RAISE OR PUBLIC RECOGNITION

AS A DATA STEWARD

How Do The Teams Work Together?

Most conflicts are resolved at the operational level, however when additional guidance is

needed, it’s recommended to have the ‘council’ assist.

High Level Plan To Get You Started

Planning

• Identify Stakeholders

• Identify Goals, Objectives, Vision & Drivers

• Complete Application Portfolio Catalog

• Create Business Case for Change

Baseline & Target

• Define Data Policies, Standards & Org Structure & Roles

• Define Governance Process

• Establish Current Maturity Level & Target Maturity Level

• Check Baseline Against Target

• Review and Confirm Data Architecture

Roadmap

• Identify and Prioritize Projects & Activities

• Develop Data Management Roadmap

• Conduct Gap Analysis

• Establish Rollout Plan and Organizational Readiness (Impact)

Rollout

• Fill Governance Roles and Socialize Structures

• Develop Policies and Standards

• Train and Communicate Throughout The Organization

• Monitor Performance and Obtain Feedback

•Business Case For Change

•Data Strategy

•Data Principles

•Terms of Reference

•Role Definitions

•Data Management Roadmap

•Gap Analysis

•Organization Readiness

•Rollout Plan

•Feedback Processes

•Communication & Training Plan(s)

•Standards and Policies

•Data Governance Maturity

Assessment

•Conceptual Models

•Process Definitions

Deliverables for Your Governance Plan

Moving from Talking to Doing

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

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

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

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

Let’s Talk About Data Quality

Getting Tactical

Moving from talking to doing:

• 9 declarative elements in SFDC that are excellent

governance/stewardship enablers

Check the www.tractionondemand.com blog for additional details

Data QualitySecurity

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

Data QualityValidation 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

Data QualityRecord 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

Data QualityDependent 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.

Data QualityApproval 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.

Data QualitySystem / 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.

Data QualityAdd 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

Data QualityKill 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.

Data QualityDe-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

Data QualityMake 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

Customer Feedback Forum Best Practices

Data Quality Best PracticesTips and Tricks for Improving Data Quality

Data Quality Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3

Duplicates from multiple systems (like SAP)

Duplicates in the existing data bases: Recommend an analysis and de-dupe project to get to a baseline. Recommend Demand Tools as product to assist in ongoing

Duplicates from incoming systems (ERP, etc..): Need to work with these system owners to identify the common key to use for prevention

Lead source creating duplicates: Determine lead process and use of the lead object in conjunction with a baseline de-dupe

Need control points to evaluate data accuracy and quality

Identify the specific fields for your organization that are of value (by object); write reports to ascertain where the data is non-standard or missing, then have validation rules created to maximize the entry going forward.

Be a Formula Ninja to help with Data Quality: http://www.youtube.com/watch?v=r1T767LzrZY#t=2377

Lack of data quality record delete strategy

Archiving policy should be established and communicated. It should consider all existing records with/without transactions and treament should be defined accordingly based on business processes

Auto delete within 60 days if no activity- record retention

No good definition of an account today

Organizations should standardize on the definition of an account with a 3rd party referential data source like D&B

Don’t try to recreate your ERP in your CRM . ERP's handle many complex relationships. Determine what is required in CRM and port data accordingly

Salesforce is a dumping ground for new data

Leverage Lead Object to filter out the garbage. Potentially do an offline cleanup first and the build out a process to manage ongoing

Compliance and Administration Best PracticesTips and Tricks for Improving CRM Compliance, Security and Adherence

Compliance Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3

How to determine and manage centralized and decentralized processes

Use delegated administration Salesforce functions to provide support for users. Establish governance team with input from business units that cross geographic boundaries to enable adequate support in end user time zones

Identify what areas of the business should be managed and supported via business specific teams, versus the overall management of the org

Use Centralized support when there are 3rd party system integrations being used, decentralized when users can be managed 1 admin to every 400 users

Security best practices for compliance and regulatory adherence; profile updates

Structure your governance plan that dictates the process that needs to be followed when initially assigning users to profiles; what validation needs to be done when profile updates are necessary, who should sign off on it from a functional side and technical side

Identify what data fields are necessary to be maintained due to regulatory requirements, or hidden from certain profiles/users

Compliance of data acquisitions and routing processes

For any data sets that cannot be automatically updated within Salesforce (like D&B information) assign a data steward that can periodically run updates and check for inconsistencies

Always include a data source on files that are assigned to users-this will help you identify if source data is problematic or if internal SF actions have updated (i.e. workflow field updates change values). Turn on activity history tracking only for critical fields

Compliance and Administration Best PracticesTips and Tricks for Improving CRM Compliance, Security and Adherence

Compliance Issue Best Practice Tip #1 Best Practice Tip #2

Data acquisition purchases vs. user obtained and organizational data

Put in place formal process for users to submit alternate data sources to administrator for uploading to Salesforce

Establish ROI on all purchased data sources first- understand what your business benefits are versus the overall maintenance, care and feeding of additional data assets. Assign a SME or data steward to each data asset and work with them to establish proper maintenance routines

Centralization issues- one group doing all updates and data loads

Limiting the number of people who can upload data is a best practice. Only certified admins with a clear understanding of the system and the data being considered should be permitted to load data

All data loads should be tested and signed off in a sandbox org prior to loading to production

Opportunities not created until deals close

This is a top down driven approach. Tie to compensation. Need to do this to create a pipeline visibility and forecast

Success Metrics Best PracticesTips and Tricks for Driving Success and User Behavior

Success Metrics Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3

Identifying and defining fields and metrics for accuracy

Identify where field population enhances organizational reporting; define what you need to report on first, then go back and fill in missing fields with information

Use consistent pick lists, run reports for missing information (eliminate 'unknown')

Create dashboards that can be scheduled for refresh to show progress each month

How to deploy a stewardship model without dedicated resources or funding

Turn your users into stewards. Leverage field tracking to track changes and owners to create an audit trail for updates and changes to records. Create field requirements on all records to ensure that proper data is entered at point of creation to avoid duplicates and shell records

Deploy a de-dupe tool and search before create to build in data quality tools that prevent user abuse and limit the need for a high level of effort by users

Create an archiving strategy to assist with currency and quality of data. Implement a flagging strategy so that users can denote records they want to keep. If the record is not maintained or updated it can move to a hidden status prior to archive. Follow similar steps as outlined in the data quality section

How do we motivate the right behavior from sales

In many cases sales and other departments for that matter are motivated by 1) Recognition 2) Compensation 3) Impact to the business. All three should be considered when putting a data governance plan together

Create a hero board that highlights those that are driving data improvements. Potentially leverage a spiff to drive record completeness and accuracy

Link opportunity ownership to data accuracy and completeness. An opportunity requires certain data points for creation and without an opportunity a contract cannot be created nor can a rep be compensated.

Data Management Best PracticesTips and Tricks for Improved Data Management

Data Management Issue Best Practice Tip #1 Best Practice Tip #2 Best Practice Tip #3

Hierarchies- without restrictions so you can view accounts with open opptys

Establish what fields you wish to have in a hierarchy- i.e. legal, by brand (tradestyle), etc.

Establish and review account sharing/access rules (i.e. read only for all users); make sure every user has a manager in SF and the manager is active

Using permission sets and role hierarchies, allow management team members more visibility into entire family trees

Blocking account creation for reps

Allow reps to create "shell" or "propspect" records. Enfore rules that require a minimum amount of info for the account to become a "customer" or allow opportunities to be created/closed on the account

Rep requests a record to be created and request routes to stewardship team. They create the account once vetted or use dupe blocker with DDC on insert

Use dupe blocker with Data.com on insert to prevent dupes from being created and drive user behavior to import records from Data.com rather than manual creation

Segmentation: governments, affiliates, hierarchies; non-standard categorization; routing, analysis and validation (territory assignments)

Apply consistency across the board with segmentation rules; make fields required if they are needed for segmentation purposes

If territory alignment changes frequently consider using territory management. This will allow you the ability to move accounts with related items to new users including open opportunities

Data Management Best PracticesTips and Tricks for Improved Data Management

Data Management Issue Best Practice Tip #1 Best Practice Tip #2

Data performance; data volume management and handling of data; Eloqua activities (email views being logged as activities)

Establish effective governance to identify and put in place clean up policies- i.e. deletion routines when there is no activity on an account or contact within a certain period of time (flag, hide, delete); conduct weekly (or for large orgs- monthly) data exports to quickly identify records that should be removed. Flag records with a potential removal date and status. Then once that date has occurred, hide records (workflow field update to change record type), and eventually delete after org export has been done

For Eloqua and marketing system integrations, keep nurturing and drip activities in those systems and only pass qualified records back to Salesforce when thresholds are met

Lead source and categories

Define and segregate leads by inbound sources; every lead should have a source so you can track metrics on results when converted

Use lead scoring to help with routing and governance (i.e if you are missing critical fields, update lead assignment rules to have sketchy leads go to an inbound data steward queue first (then supplement before sending to sales users)

Not using leads in Salesforce

Using leads allows your company to maintain two separate lists - one for prospective customers and one for existing customers. You can store your prospects as leads, and then once a lead becomes qualified, you can convert it to an account, contact, and, optionally, an opportunity

Leads are especially useful if your company has two separate teams - one that handles lead generation and mass marketing and one that handles sales. The lead generation team can concentrate their work on the Leads tab, and the opportunity team can use the Account, Contact, and Opportunity tabs

Data Management Best PracticesTips and Tricks for Improved Data Management

Data Management Issue Best Practice Tip #1 Best Practice Tip #2

How to build out hierarchies

D&B upward linkage is a very effective way to understand account family structures. This understanding enables effective territory management, cross sell/upsell opportunities as well as roll up reporting to understand total sales/exposure to a particular business

Define and determine the role of a legal hierarchy vs. an internal company defined hierarchy. Some customers need to understand both and reflect legal ownership, as well as the unique view of the account from the account management perspective.

Marketing automation integration

Marketing automation integration can be handled in two main manners 1) Bring all records into SFDC, Clean and enrich, then push applicable data back to MA tool

2) If a subset of records is desired in Salesforce, Data.com API's can be utilized to enrich data within the MA tool. Note licensing restrictions may apply. In both cases enriched data is critical for lead routing, scoring and reporting

Need simplified view of customer; too many related objects; inline dashboards might be good

Ongoing system governance and system audits to prohibit and weed out unneeded configuration and technical debt is key to a successful system

MDM team that owns the golden record- not visible via Salesforce views today

Record visibility is determined via Salesforce sharing and role hierarchy set-up. Any integration should keep these rules in mind

Each platform owner owns data - 34 instances of Salesforce

To enable cross platform visibility, a CDM/MDM strategy should be considered. Salesforce to Salesforce integrations as well as consolidation orgs are considered for these needs. Clean, keyed data is the foundation to enable such cross-org visibility