Thoughts on a Salesforce Informatica Acquisition — Portfolio Comparison

As I read discussions on LinkedIn and elsewhere on what a Salesforce acquisition of Informatica may mean, discussions often center around:

• How does Informatica’s portfolio compare to and complement MuleSoft’s?
• How the additional enterprise data management capabilities could be beneficial to Salesforce?

Having worked with products from both portfolios in the past, I thought I’d offer a breakdown of key capabilities, leveraging DAMA (Data Management Association) International’s DMBOK (Data Management Body of Knowledge) framework.

Then, I decided to go broader and include Data Cloud, core platform, and even some Tableau capabilities. After all, if you love what Salesforce offers, these are the considerations you will have.

Image created by ChatGPT

Data Integration and Interoperability

• MuleSoft: Specializes in API-led connectivity, facilitating integration through application networks where APIs connect applications, data, and devices. It allows organizations to build application networks that increase the speed of new technology adoption and decrease integration costs.
• Informatica: Known for its robust ETL capabilities with PowerCenter, which handles high-volume data integration across heterogeneous systems. PowerCenter excels in comprehensive data integration processes including batch, real-time, and streaming, supporting complex transformations and data consolidation.

Verdict? A tie.

In practice, I wonder if and how this may change Data Cloud implementation patterns, specifically whether ETL or ELT will win the day.

MuleSoft may shift its focus to the right as an activation solution, powering business applications vs. focusing on data movement.

Data Quality

• MuleSoft: Indirectly supports data quality by maintaining data integrity through standardized APIs that ensure consistent data flow and structure across systems, reducing errors from manual integrations.
• Informatica: Offers a suite of data quality tools that include advanced profiling, cleansing, and matching capabilities. These tools help organizations ensure the accuracy, completeness, and consistency of data across systems, which is crucial for operational efficiency and regulatory compliance.

Verdict: Informatica? Not so fast— we also should look at Native Apps.

Informatica offers powerful data quality solutions, from profiling. However, many Informatica customers still invest in other data quality solutions, which warrants understanding.

Native data management apps, specifically designed for Salesforce environments, ensure tight integration and alignment with Salesforce data structures, trust, and metadata models. Their user-friendly interfaces cater to Salesforce admins and business users, promoting broader user engagement in data quality initiatives.

“Native” AppExchange “Data Management” Apps — A 4 Stars+ Sampling

Informatica offers many advances features, such as machine learning-driven insights and cross-system data stewardship enhance deep data quality analysis. However, Informatica requires significant technical expertise, and users are typically in IT v.s. lines of business. They typically have higher initial and ongoing costs compared to most AppExchange solutions.

Verdict? Yes, and.

I expect smaller and mid-market customers will start with native solutions with simplified business applications and data architectures, while larger organizations will need both.

Master Data Management (MDM)

• MuleSoft: Lacks a native MDM solution but can integrate with external MDM systems, including Salesforce’s built-in capabilities, to manage and synchronize master data across the ecosystem.
• Informatica: Provides a comprehensive MDM solution that supports the creation of a single source of truth for critical business data. It enables complex hierarchy management, business process management, and data stewardship, thereby supporting detailed, rule-based workflows that can handle multiple data domains (like customer, product, and supplier data).
• Data Cloud: Identity resolution features should be evaluated under the MDM category through its ability to unify and manage customer data from multiple sources. Unlike traditional MDM solutions that focus on creating a single golden record, Data Cloud employs a flexible “Key Ring” approach that accommodates multiple identifiers per entity, allowing for a more dynamic and adaptable data management strategy that supports a wide range of business use cases.

Verdict? Yes!

I am personally a fan of the key ring approach combined with contextual views where only data permissible is aggregated and visualized for different personas. That said, Data Cloud does not offer hierarchy management and data stewardship features and current identity resolution is primarily focused on Individual and Organization data.

I expect organizations to start with Data Cloud and then extend their capabilities through an MDM such as Informatica’s.

Key considerations will include TCO, driven heavily by data volume and rate of change.

Data Governance

• MuleSoft: Enhances data governance by enabling secure data sharing through APIs that enforce governance policies at the data access level. This ensures that data consumption across systems adheres to compliance standards and business rules.
• Informatica: Delivers robust data governance capabilities that integrate with its data quality and MDM solutions. These include policy management, compliance tracking, and role-based access controls that ensure data is used ethically and in accordance with regulatory requirements.
• Salesforce CRM: Salesforce CRM’s Data Dictionary acts as a central repository that organizes and defines all data elements, their meanings, and relationships within the Salesforce environment. This aids in maintaining consistency and clarity across the platform, ensuring that all stakeholders have a clear understanding of data operations and governance.
• Tableau Data Catalogue: Tableau Data Catalog enhances data governance by offering a centralized view of the data used across Tableau environments. It helps manage data quality, usage, and security policies by providing detailed, searchable metadata, data lineage, and usage metrics, which improve trust and compliance in data handling.
• Salesforce Data Cloud: Leverages a library of object definitions essential to its Data Model Objects (DMOs) and data mapping, simplifying ‘data kits’, simplifying implementation and integration tasks. Data Spaces provide further data segregation capabilities, whereas Calculated Insights provide metric definitions similar to what you may define in Tableau or CRM formulas.

Verdict? Watch this space. Why? Because we still have a ways to go in metadata management within and across solutions.

Metadata Management

• MuleSoft: Through Anypoint Platform, it offers metadata discovery and management functionalities that help in documenting API and integration assets, facilitating easier maintenance and faster onboarding of new developers and technologies.
• Informatica: Provides extensive metadata management tools that automate the discovery, classification, and lineage of data across complex systems. Its Enterprise Data Catalog uses machine learning to scan and catalog data assets across the enterprise, enhancing visibility and governance.
• Salesforce CRM: Salesforce CRM facilitates metadata management through its Metadata API, which allows developers and administrators to retrieve, deploy, create, update, or delete customization information, such as custom object definitions and page layouts, directly from their Salesforce orgs. This API enables the handling of the metadata (or structure) of the data stored in Salesforce, crucial for ensuring that data fields and objects align with business processes.
• Tableau: Tableau enhances metadata management with Tableau Catalog, part of Tableau Server and Tableau Online. It provides comprehensive visibility into the metadata of data used in Tableau, allowing users to track data usage, analyze impact, and maintain governance. The catalog automatically gathers metadata about the data sources and their fields, including data lineage, helping users understand data origins and dependencies.

Verdict? Yes and Informatica is the winner here.

For transparency, ChatGPT helped me summarize the capabilities across the four product lines for I appreciate its “neutrality”. What you are hopefully seeing is, Informatica is the only one with a metadata solution that is not tied to an implementation use case. That said, you will keep using and managing metadata in all of them.

Distributed metadata is a challenge and innovation opportunity for companies. Salesforce has at least one patent in this space.

Federated access to distributed metadata will surely evolve alongside AI applications in the future.