The Total Economic Impact™ Of Informatica Intelligent Data Management Cloud

Data Team Capacity Improvement

Evidence and data. Interviewees reported that IDMC improved the capacity of their organizations’ data teams by centralizing data management, governance, and stewardship activities. They shared the following experiences:

• The head of master data management at a pharmaceutical company shared that IDMC streamlined and automated data stewardship workflows, driving a 25% capacity improvement for their organization’s data stewards. Prior to IDMC, activities like record reconciliation, data validation, and governance were performed manually across multiple systems and business functions. With IDMC, this interviewee’s organization gained a centralized platform for managing R&D, commercial, supply chain, and financial data, enabling data stewards to govern data from a single source of truth and leverage automated capabilities such as matching, deduplication, and master record management. As a result, the interviewee’s organization improved data quality by up to 40% and increased overall data steward productivity.

• The global director of data governance at an electronics company described how IDMC improved data governance workflows, driving an estimated 10% productivity improvement for data stewards and governance personnel at their organization. With IDMC, their organization gained a central platform for data governance, including a standardized business glossary, lineage mapping, data catalog, and access management features, enabling data stewards to more efficiently identify, investigate, and resolve data quality issues while maintaining consistent governance practices across the organization. As a result, this interviewee’s organization implemented formal data quality resolution SLAs, reducing resolution times from approximately one week to 24 hours for critical issues and 48 hours for noncritical issues. The interviewee noted that IDMC also improved efficiency for engineering teams by increasing visibility into data lineage and data sources, reducing the effort required to understand where data originated and how it moved across systems. As a result, engineering teams realized an estimated 5% productivity improvement.

• The chief information officer at a telecommunications provider shared that migrating from a legacy on-premises integration environment to IDMC improved the productivity of their data engineers, architects, and data stewards by approximately 20%. With IDMC, their organization modernized its data integration architecture, providing a centralized platform for managing data pipelines and workload performance. In fact, the interviewee reported that processing times for key data flows improved by approximately 30% on average, with some workloads achieving up to 50% faster performance. The interviewee also reported that the organization improved data quality by 30% to 40%, reducing the volume of data quality issues and inquiries that data stewards needed to address.

• The digital capability director at an energy company reported that their organization consolidated install-base data from eight separate back-end systems into a central master data management environment using IDMC, simplifying data management and reducing architectural complexity. The interviewee shared that the initiative improved data quality by more than 30% and enabled 53% faster resolution of data quality issues. The interviewee also noted that infrastructure and application rationalization significantly improved operational efficiency, estimating that teams became nearly twice as effective and enabling the organization to reduce duplicate roles.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization has 15 data stewards.

• Each data steward experiences a 30% capacity improvement due to IDMC.

• The average fully burdened annual salary for a data steward is $111,000.

• The composite organization has 60 data engineers and 53 data architects.

• Each data engineer or architect experiences a 20% capacity improvement due to IDMC.

• The average fully burdened annual salary for a data engineer and data architect is $153,000.

Risks. The scale of this benefit may vary from organization to organization based on:

• The number of data stewards, engineers, and architects at an organization.

• Variation in salaries, which can depend on factors such as location and seniority.

• The maturity of an organization before adopting IDMC.

• The extent to which the organization expands or matures its use of IDMC over time, which may increase the scale of productivity benefits realized over time.

• Effective change management.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $8.4 million.

Data Consumer Productivity Savings

Evidence and data. Interviewees reported that IDMC improved productivity for data consumers by improving data quality and accessibility. They shared the following experiences:

• The global director of data governance at an electronics company estimated that data consumers experienced a productivity improvement ranging between 5% to 15%. With IDMC, their organization established a master data record, including a standardized data catalog to help users locate and understand data, lineage mapping to trace data relationships across systems, and standardized business definitions to improve consistency. These capabilities reduced the time users spent searching for information, validating data sources, and reconciling records. The interviewee described a customer research initiative in which determining total spend for a major customer previously required six weeks of manual investigation due to logical duplicates across systems, inconsistent business definitions, and difficulty tracing data across sources. With IDMC, the interviewee reported that their organization could answer similar questions in as little as one day.

• The head of master data management at a pharmaceutical company described how unifying operational and financial data into a master record improved efficiency for reporting teams, using their organization’s transparency-reporting processes as an example. The interviewee explained that the Physician Payments Sunshine Act required their organization to accurately aggregate and report payments and other transfers of value made to healthcare professionals recorded across systems. Prior to Informatica, their reporting teams manually reconciled transactions and engagement data to determine whether records belonged to the same individual or activity. With a centralized master record, teams could work from a single source of truth, significantly reducing reconciliation and validation efforts. As a result, the interviewee’s organization improved reporting efficiency, reduced manual effort, and increased confidence in the accuracy of regulatory reporting.

• The digital capability director at an energy company estimated that data consumers experienced approximately 40% productivity improvements following their organization’s data modernization initiative. The interviewee attributed these gains primarily to the consolidation of install-base data from multiple legacy systems into a centralized architecture in which IDMC and a downstream data lake served as key components. Previously, users were required to locate, compare, and reconcile information across multiple systems before conducting analysis because no trusted enterprise view of customer and asset data existed. By establishing a single source of truth and standardized data foundation, the interviewee’s organization improved data accessibility and consistency, enabling users to perform analytics and generate insights with significantly less manual validation and reconciliation effort.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization has 750 data consumers that is composed of business and data analysts and data scientists.

• IDMC improves the productivity of data consumers by 10%.

• A productivity recapture rate of 50% is applied to account for the portion of time savings that is redirected toward productive work.

• The average fully burdened annual salary for a data consumer is $101,000.

Risks. The scale of this benefit may vary from organization to organization based on:

• The number of data consumers that regularly rely on enterprise data to perform analytics, reporting, and decision-making activities, and their roles. Forrester’s analysis uses business and data analysts and data scientists as a proxy for data consumers. In practice, employees across various roles and business functions may also consume data impacted by IDMC.

• The maturity of existing data management and governance capabilities before the implementation of IDMC.

• The effectiveness with which an organization adopts and utilizes IDMC.

• The degree to which time savings can be converted into productive activities by data consumers.

• The actual salaries of data consumers, which vary based on factors such as role, location, and seniority.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $8.0 million.

Reduced Risk Of Compliance Violation

Evidence and data. Two interviewees shared that IDMC improved governance over their organizations’ data. They shared the following experiences:

• The global director of data governance at an electronics company reported that IDMC strengthened their organization’s data governance and compliance capabilities. By adopting IDMC, their organization gained better visibility and control over where sensitive data resided, how it moved across systems, and how systems and data assets were connected, enabling the organization to more effectively manage regulatory and compliance requirements. For example, the interviewee described how teams previously relied on manual processes when responding to privacy-related requests, such as requests to delete personal data, increasing the risk of oversight and noncompliance. With IDMC, teams were able to trace personal data across systems, understand downstream dependencies, and respond to privacy-related requests with greater confidence and accuracy. The interviewee estimated that these capabilities reduced their organization’s risk of a data-related compliance violation by approximately 20% and noted that auditors also benefited from improved traceability.

• The digital capability director at an energy company noted that their organization manages sensitive asset information, including data associated with critical infrastructure and regulated facilities, requiring strong security, governance, and compliance controls. IDMC strengthened their organization’s governance capabilities by centralizing data quality rules, survivorship rules, and other data governance processes within a single platform. They said: “Informatica has strengthened our governance and control over data quality and survivorship rules, helping us significantly reduce data-related operational risks.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The average cost of a GDPR fine is $2,640,000.⁴

• The composite organization faces a 10% annual risk of experiencing a GDPR-related compliance violation and resulting fine.

• By adopting IDMC, the composite organization reduces the likelihood of a GDPR-related violation by 20%.

Risks. The scale of this benefit may vary from organization to organization based on:

• The volume and sensitivity of data processed by the organization.

• An organization’s existing data governance, privacy, and compliance maturity prior to implementing IDMC.

• The effectiveness with which an organization adopts and operationalizes IDMC.

• Variations in applicable regulatory requirements, potential penalty amounts, and enforcement activity across geographies and industries.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $112,000.

Reduced Data Infrastructure Costs

Evidence and data. Two interviewees reported reductions in data infrastructure costs following the adoption of IDMC. They shared the following experiences:

• The chief information officer at a telecommunications provider described how migrating from an on-premises ETL environment to IDMC reduced their organization’s data infrastructure costs. Prior to the migration, their organization maintained dedicated infrastructure to support data integration workloads and periodically invested in hardware upgrades to meet growing data volumes and performance needs. By adopting IDMC, the interviewee’s organization transitioned to a cloud-based operating model that simplified infrastructure management and reduced hardware investments. The interviewee estimated that the modernization reduced infrastructure-related costs while also improving the scalability and performance of data integration workloads.

• The digital capability director at an energy company described how a broader data modernization and rationalization initiative, in which IDMC served as a supporting component of their organization’s data architecture, reduced its data infrastructure and operating costs. Prior to the transformation, install-base data was managed across multiple back-end systems, requiring significant infrastructure investments and support resources. By consolidating these systems into a centralized architecture, their organization reduced the number of technologies and systems requiring ongoing support while simplifying data management processes. The interviewee estimated that the initiative reduced the ongoing cost of running data management solutions by approximately 50%, noting that the savings were primarily driven by system consolidation and rationalization.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization incurs $5 million in annual run costs for legacy data integration infrastructure prior to IDMC, including hardware, software, maintenance, and personnel required to operate and support the environment.

• The composite organization modernizes its data infrastructure with IDMC, migrating legacy and on-premises data integration workloads to a cloud-based architecture. Through infrastructure rationalization, lower maintenance requirements, and the phased retirement of legacy environments, the organization reduces data infrastructure run costs by 35% in Year 1, 40% in Year 2, and 45% in Year 3.

Risks. The scale of this benefit may vary from organization to organization based on:

• The size and complexity of the organization’s existing data infrastructure environment.

• The number and costs of legacy systems, integration tools, and infrastructure components that can be consolidated or retired.

• The cost of ongoing infrastructure administration and maintenance activities required before implementation.

• The pace at which infrastructure modernization initiatives are executed and legacy environments are decommissioned.

Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.9 million.

Unquantified Benefits

Benefits that provide value for the composite organization but are not quantified for this study include:

• Improved data quality and trust. Interviewees highlighted that their organizations established a single source of truth across previously siloed data domains, driving higher trust in their organizations’ data assets. As a result, operational stakeholders and leadership gained greater confidence in the accuracy of key business entities, enabling more reliable, data-driven decision-making, accelerated business intelligence (BI) reporting, and a stronger foundation for strategic growth initiatives.

• Improved business outcomes. Several interviewees shared examples of how IDMC drove business outcomes for their organizations. The head of master data management at a pharmaceutical company shared that connecting data across research, clinical, and commercial systems improved clinical trial recruitment and contributed to faster product launch cycles. The digital capability director at an energy company shared that unifying install-base data into a master record improved visibility into asset lifecycle information, enabling additional cross-sell and upsell opportunities while providing customer care teams with better visibility into customer assets and service history. The global director of data governance at an electronics company shared that their organization was in the process of centralizing product and inventory data across its three ERP systems using IDMC. Once complete, the interviewee expected the initiative to improve inventory visibility and allocation, increase quoting accuracy, and enable more informed supply chain management decisions. For the composite organization to quantify the business outcomes enabled by IDMC, it would need to connect specific use cases to measurable improvements in revenue, profitability, or other measurements.

• Better scalability and performance. The chief information officer at a telecommunications provider reported that IDMC improved the scalability and performance of their organization’s data environment. By migrating from an on-premises integration environment to IDMC, this interviewee’s organization improved processing times for key data flows by approximately 30% on average, with some workloads achieving up to 50% faster performance. The interviewee also described how moving from a CPU-based licensing model to a cloud-based consumption model simplified workload management and enabled the organization to scale processing capacity more efficiently. For the composite organization, improved performance supports faster delivery of data to business users and more timely analysis, and greater scalability allows the composite organization to accommodate larger data volumes and evolving business needs without additional hardware investments.

• Avoided AI development costs. The director of data governance at an electronics company reported that their organization would avoid an estimated $1.0 million in development costs for a customer risk modeling project by leveraging IDMC’s MDM capabilities instead of developing a separate customer-matching solution.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement IDMC and later realize additional uses and business opportunities, including:

A data foundation for AI initiatives. Interviewees shared that IDMC helped their organizations reduce the complexity of managing and unifying data across previously fragmented systems, providing a governed data foundation to support their organizations’ AI initiatives. They detailed examples of how their organizations leveraged IDMC’s data management capabilities to enable AI-related initiatives. For example, the head of master data management at a pharmaceutical company described how their organization leveraged IDMC’s MDM and reference data management capabilities to establish standardized healthcare professional master records and a semantic layer, providing the foundation for future agentic AI initiatives.

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).