Ask an Architect: 5 Steps to an Effective Salesforce Data Management Strategy (Part 1)

Ask an Architect, Effective Salesforce Data Management Strategy

This is the first of a monthly blog series we call “Ask an Architect.” In this series, we will be answering common questions from some of our most ambitious customers using Salesforce.

We have millions of records and constituents with high expectations around service and engagement; how do we develop an effective data management strategy?

While it doesn’t sound sexy, effective CRM data management is an integral part of delivering a remarkable constituent experience across all channels, particularly for organizations with large data volumes. 

And the Salesforce platform offers a number of features that make it easy to develop a common sense approach to data management that can deliver happier constituents, a more effective user experience, improved organizational agility, and reduced maintenance & cost.

An effective CRM data management strategy is founded on a solid understanding of your business process, user behavior and technology, and succeeds when you combine it with governance and disciplined execution.

Consider these 5 steps when building your Salesforce Data Management Strategy:

  1. Take Only What You Need: CRM-Relevant Data
  2. Optimize your Big Objects: Large Data Volume Optimization
  3. Use Data where it Lives: Federate and Integrate non-CRM data
  4. Travel Light: Data Archiving
  5. Govern with Discipline: Master Data Management                                                                                                                  

In this blog, we are focused on Steps 1 and 2:

1. Take Only What you Need: CRM-Relevant Data

Limit the data imported into CRM by clearly differentiating and importing only core CRM data. The following is a summary of various types of data, and a potential approach you to manage it.

Result: Reduces data clutter, improves user experience and may reduce cost.

Types of Data Examples High Level Approach

CRM Data

Data that drives constituent engagement for university programs like recruitment, admissions, students success, employee enablement, alumni relations, athletics and advancement.

Typical must-have data for all constituents e.g. biographical or demographic data and communication preferences.For example, in a student success solution, it is valuable to import degree and GPA data, but not donations.

Import all, or enough data to run daily operations

This data populates standard or custom Salesforce objects (or “tables”) and isWhat users see when viewing a ContactWhat is available in dashboards and reportsA driver of key processes, including communications and constituent “touches.”

Non-CRM Transactional Data

Data that supports CRM operations but is mastered elsewhere

Coursework/LMS data, external system data such as event details, donation amounts and other financial transactions.

Manage data in its system of records

External Data/ Apps – Integrate/Federate, Mashups, Einstein Analytics etc.
Create CRM integrated app via Heroku/Heroku Connect

Non-CRM Additional Data

Data that offers additional insights/research into constituent relationship

Usage statistics from dining hall, gym, study room reservation systems – other indicators of student success on campus.Also includes system generated – Web Analytics IoT – Device/Sensor data etc.

Manage data in its system of records
External Data/ Apps – Integrate/Federate, Mashups, Einstein Analytics etc.
Create CRM integrated app via Heroku/Heroku Connect OR
Salesforce IoT Cloud

Historical Data

Data that is outdated and cannot be used in operational context

Typically 5-7+ years old data, also includes data such as Individual Email Result (IER) data from Marketing Cloud and others that may be irrelevant after 3-6 months.

Move data out of CRM

Data Archiving, use of Warehouse/Data Lake
Import summary/roll-up of transactional historic data

What makes any data a good fit for CRM data?

Answering “Yes” to one or more of the following implies that this data may be CRM-relevant, and requires a more careful consideration.

Considerations Tell me more Examples

Will Salesforce be the “System of Record” for this data?

Typically data that is mastered in other systems can be shown in Salesforce without replication (Refer section #3)

Data mastered in an institution’s source system such as SIS, Advancement, HR etc.

Will this data be required for key business processes that are implemented on Salesforce?

Data that is referenced for key Service processes such as in Case object, Campaigns etc.

Academic programs, periods, assignments, inventory data, Do Not Call preferences etc.

Are there automation use cases (within Salesforce) that are based on changes to this data?

Data that is used to drive Salesforce workflows, lightning process builders, email templates, Apex triggers etc.

Total Current or Fiscal Year Giving, Total # Enrolled Credits

Are there any Salesforce specific reports, dashboards, and KPI (Key Performance Indicators) that use this data?

Reports and dashboards in Salesforce require data to be stored within Salesforce.

Fundraising Goals, Total New Donors, Total New Students, Total Returning Students

Will users need to have activities, tasks or chatter feeds around this data?

Activities/Tasks/Chatter feeds require the parent record data to be stored within Salesforce.

Team collaboration on a major or restricted gift

2. Optimize your Big Objects: Large Data Volume Optimization

Proactively identify and optimize your CRM for objects with millions of records (typically referred to as Large Data Volume or LDV). Use the LDV best practices guide to optimizing performance.

Result: Improves CRM’s internal performance and user experience.

LDV Techniques What is it? When to use it?

To understand effective execution plans for SOQL queries.

Use the Query Plan tool to optimize and speed up queries done over large volumes.

A nightly process to collect statistics from the database in order to know and access data better.

Use it to understand data growth, and plan for data maintenance, LDV strategy accordingly.

Salesforce can create skinny tables to contain frequently used fields and to avoid joins. Can be requested for custom and some standard objects.

Useful to resolve specific long running queries – typically for objects with millions of records. They can enhance performance for reports, list views, and SOQL.

Salesforce supports custom indexes to speed up queries. They can be requested by contacting Customer Support.

Useful for specific SOQL queries that need to work selectively using a non-indexed field.

Divisions can segment organization’s data into logical sections, making searches, reports, and list views more meaningful to users.

For organizations with extremely large amounts of data that can be logically segregated (by region, territories or others).

Here is some additional information on LDV techniques.

LDV Techniques Level of Effort/Complexity Examples

Complexity: Low to Medium
Effort: Low (Days to Week)Requires being code-savvy to get meaningful analysis. Accessed via Dev. console, and fairly simple to use.

Reports or list views that time out or take a long time to return results. Run SOQL from report to troubleshoot.

Complexity: Low
Effort: Low (Days)Fairly simple to access via Setup menu, and understand the results from.

Use to make informed decisions about growth of their database by object.

Complexity: Low to Medium
Effort: Low to Medium (Week)Analysis can take time. Contact Salesforce support to determine if this is appropriate for your instance.

User registration that may require additional info (from custom non-indexed fields) that is taking too long to return results.

Complexity: Medium
Effort: Low to Medium (Weeks)Analysis can take time. Utilize query plans to determine need for Index. Contact Salesforce Customer support to get these implemented (on a case by case basis).

Most commonly used custom fields, membership ID,

Complexity: Medium to High
Effort: High (Weeks to Months)Can require extensive analysis and expertise from experienced professionals, and is irreversible. Testing in a sandbox may be needed.

Breaking out Contact/Account information by School or College.

This is part 1 of a 2-part blog series on data management for large data volumes. Keep an eye out for the next installment of this series on February 19, 2018. Or join us for our “5 Steps to an Effective Salesforce Data Management Strategy” webinar on February 27, 2018.

This blog is also part of our larger “Ask an Architect” content series. To learn more about engaging a Customer Success Architect in your organization, please contact your Account Executive.

Picture of Saurabh Gupta

Saurabh Gupta

Saurabh is an Enterprise Architect and seasoned entrepreneur spearheading a Salesforce security and AI startup with inventive contributions recognized by a patent.

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