Sustainability is no longer an optional initiative—it is a business imperative. Across industries, enterprises are rethinking operations, supply chains, and customer engagement strategies to meet environmental, social, and governance (ESG) goals. At STL Digital, we believe that agentic AI—autonomous, goal-driven AI systems—has the potential to unlock the next wave of sustainable transformation. By combining intelligence with autonomy, enterprises can reduce their environmental footprint while boosting efficiency, resilience, and profitability.
But what does this shift look like in practice? And how are leading analyst firms evaluating its potential? Let’s explore how enterprises can embrace AI innovation to drive green growth.
Why EDM is the unlock for modern Finance
EDM is not a single tool. It’s a fabric of capabilities—data governance, master data management (MDM), data quality, metadata lineage, and access controls—designed to make data findable, accurate, and usable by business teams. When Finance operates on this fabric, three shifts occur:
- From report requests to self-service: Standardized definitions for revenue, margin, customer, and product let analysts build their own models and dashboards with confidence—no more “whose number is right?” debates.
- From reconciliation to insight: Automated data quality and lineage reduce manual checks. Time saved goes into scenario modeling, driver-based planning, and working capital optimization.
- From siloed systems to shared semantics: MDM aligns ERP, CPM, CRM, and data lake entities so Finance can trust cross-functional numbers during close, forecast, and audit.
Industry research reinforces the direction. AI-driven automation and integrated data intelligence are emerging differentiators—capabilities that compress the cycle from data ingestion to business insight and put governed self-service within reach for functions like finance. Gartner also underscores that data governance is a business capability — focused on outcomes and accountability — not just hygiene, a framing that’s essential when Finance takes stewardship of critical metrics.
The dependency dilemma: why Finance over-relies on IT today
Finance teams often depend on IT to reconcile mismatches, update hierarchies, and provide extracts because underlying data pipelines and governance are still fragmented. Without Enterprise Data Management (EDM), every “minor” change becomes a costly IT initiative.
IDC’s latest infrastructure analysis underscores the magnitude of this issue. As the report notes: “Structured Database/Data Management workloads continued to drive the largest share of enterprise IT infrastructure spending in the second half of 2023 (2H23)… Organizations spent $7.2 billion on compute and storage hardware infrastructure to support this workload in 2H23, which represents 7.8% of overall enterprise IT infrastructure spending”
Despite the high level of spend, it also observed that “Structured Databases/Data Management was one of the few workloads where spending declined in the second half of the year, falling 1.3% compared to the same period in 2022.” Meanwhile, newer categories such as “AI Lifecycle workloads accelerated during the second half of 2023, growing 26.6% compared to 2H22 and representing 7.2% of overall spending.”
For Finance, these figures paint a clear picture: unmanaged, traditional data workloads still consume the largest chunk of IT budgets, yet they are stagnating compared to fast-growing areas like AI. That means continued reliance on IT for financial data processes not only slows agility but also locks organizations into legacy spending patterns. EDM offers the way out—reducing dependence on IT by giving Finance governed, self-service access to trusted data.
What good looks like: an EDM blueprint tailored for Finance
A practical blueprint breaks dependency without breaking governance:
1) Institutionalize a Finance data product catalog
Treat recurring analytical needs—actuals vs. budget variance, cash flow forecasting, DSO/aging, cost-to-serve—as “data products.” Each product has:
- Clear metric definitions and owners
- Certified datasets with freshness SLAs
- Lineage from source systems to reports
- Self-service access policies
2) Centralize MDM for core Finance entities
Unify customer, supplier, product, legal entity, and chart-of-accounts hierarchies. MDM is pivotal to extracting more value from enterprise data by simplifying how organizations organize and access key entities—exactly the friction that slows finance.
3) Automate quality checks where errors originate
Build controls into ingestion (not just consumption): completeness, conformity, referential integrity, outlier thresholds, and period-close locks. Embed issue triage workflows that route to the right steward.
4) Make lineage transparent and explorable
When a number in a board deck looks off, analysts should click to see its source tables, transformation logic, and currency conversion rules. This collapses the “what changed?” cycles that otherwise pull in IT.
5) Govern by policy, enable through platform
Design access rules once (e.g., geo restrictions for payroll, role-based visibility for P&L by division) and apply them uniformly across BI, notebooks, and planning tools. Outcome-driven data architecture is particularly useful to ensure governance accelerates, not blocks, value creation.
Operating model: give Finance the keys (with guardrails)
Reducing dependence doesn’t mean bypassing IT. It means redefining roles:
- Finance Data Owner (CFO delegate): Accountable for metric definitions, acceptance criteria, and change approvals.
- Data Stewards (within Controllership/FP&A): Maintain reference data, certify datasets, manage quality exceptions.
- Data Engineering (IT/Platform team): Provides the shared data platform, reusable pipelines, and security frameworks.
- Analytics COE (cross-functional): Coaches on modeling techniques, dashboard UX, and advanced analytics patterns.
This federated model reflects the data-driven enterprise— technology, literacy, and governance democratized across business functions rather than centralized bottlenecks.
Tooling principles: choose what reduces friction for Finance
Even if specific vendor choices vary, evaluate platforms through a Finance lens:
- Business-friendly governance: Data & Analytics Governance Platforms are enabling business roles (not just IT) to create and monitor policies—critical if controllers and FP&A leads are to their own definitions.
- Integrated MDM & reference data: Native hierarchy management for legal entities, cost centers, and GL accounts—plus golden records for customers and suppliers.
- End-to-end lineage & impact analysis: Visualize dependencies from ERP to planning to dashboards when a field or mapping changes.
- Metadata-driven automation: Templatize recurring patterns—period close, FX conversions, eliminations—to reduce manual steps.
- Open integration: Standards-based connectors to enterprise applications (ERP, CRM, CPM) and data lakes/warehouses.
- AI-assisted stewardship: Emerging GenAI solutions can suggest data mappings, detect anomalies, and recommend governance policies. We forecast rapid growth in AI governance software, which aligns with the need to manage model integrity as finance scales AI use cases.
Use cases that immediately pay off for Finance
Faster close and audit readiness
Automated data quality and lineage shrink reconciliation time, while certified data products standardize the narrative across FP&A, Controllership, and Treasury. Forrester’s TEI methodology is often used to quantify time saved and risk reduced when business teams adopt governed platforms—useful when building your EDM business case.
Rolling forecasts and scenario agility
With harmonized master data and policy-driven access, analysts can build scenarios on demand—interest-rate sensitivity, inflation impacts, supply-chain shocks—without waiting for new IT pipelines.
Profitability and cost-to-serve
When customer and product dimensions are consistent across order-to-cash and procure-to-pay, finance can attribute costs precisely and steer pricing or discount policies faster.
Working capital optimization
Clean, conformed invoice and payment data enables better DSO/DPO analytics and cash forecasting, improving liquidity.
ESG and regulatory reporting
With traceable lineage and controls, you can assemble auditable metrics (e.g., financed emissions, supplier diversity) using the same EDM backbone.
Building the business case: cost, risk, and speed
EDM investments should be justified like any major program: clear baselines, KPIs, and an adoption roadmap. Forrester’s market research on data governance trends emphasizes why centralized governance underpins AI-fueled use cases; that linkage strengthens the ROI case by connecting today’s pain relief to tomorrow’s growth enablers. Simplifying data management yields infrastructure efficiencies, not just analytic speed.
Metrics to track
- Close cycle time (days) and post-close adjustments
- Time-to-insight for new analyses (e.g., M&A, new product lines)
- Volume of certified datasets and data product adoption
- Percentage of issues auto-resolved at ingestion
- Reduction in IT tickets related to data access, extracts, and reconciliations
- Audit findings related to data lineage or control failures
A 120-day roadmap to reduce IT dependence
You don’t need a big-bang transformation. Start small, prove value, scale.
Days 1–30: Align & define
- Establish finance data ownership and stewardship roles.
- Identify 3–5 high-value data products (e.g., revenue analytics, opex transparency, cash flow).
- Baseline current KPIs (IT tickets, reconciliation hours, close timeline).
Days 31–60: Stand up the platform slice
- Deploy core governance workflows and glossaries for target metrics.
- Connect priority sources (ERP/CPM) to a governed zone in your lakehouse or warehouse.
- Implement MDM for one entity (e.g., customer).
- Publish lineage for one end-to-end report.
Days 61–90: Automate quality & certify
- Add automated quality checks, anomaly alerts, and stewardship workflows.
- Certify the first two data products with freshness SLAs.
- Roll out role-based access policies tied to finance roles.
Days 91–120: Scale & industrialize
- Extend to additional entities (supplier, product) and a second domain (e.g., opex).
- Train analysts on self-service and build a community of practice.
- Publish a scorecard to demonstrate reductions in IT requests and time-to-insight.
This phased approach mirrors patterns seen in world-class digital finance functions that rebalance ownership between business and IT while raising overall maturity.
How EDM changes the day-to-day for Finance
- Controllers spend less time chasing mismatches and more time strengthening controls and narratives.
- FP&A shifts effort from data wrangling to driver-based modeling and sensitivity analysis.
- Treasury improves cash forecasting by harmonizing bank, AR/AP, and order data.
- Internal audit gains confidence with transparent lineage and certified datasets.
- Business partners receive consistent insights, accelerating decisions on pricing, promotions, and capacity.
Connecting EDM to the broader tech landscape
EDM isn’t confined to analytics—its impact spans IT solutions and enterprise applications:
- ERP & CPM: Harmonized dimensions prevent downstream rework; close and consolidation benefit from standardized mappings.
- CRM & CPQ: Consistent customer and product hierarchies drive cleaner pipelines and more accurate revenue forecasting.
- Data platforms & BI: Governed, metadata-rich datasets power trusted dashboards and ad-hoc analysis.
- AI & ML: High-quality, well-governed data is the prerequisite for trustworthy models; research shows that governance maturity is increasingly tied to AI success.
Practical tips to sustain momentum
- Codify definitions in a business glossary that lives where analysts work.
- Automate lineage capture; don’t rely on manual diagrams.
- Design for change: Assume hierarchies will change monthly. Make remapping easy and auditable.
- Publish a certification badge in BI tools so users know which datasets to trust.
- Incentivize usage: Tie adoption of certified data products to planning and performance processes.
- Measure & market wins: Share KPI improvements—days off the close, fewer IT tickets, faster M&A modeling—to secure continued sponsorship.
Finance at the Data Helm
Finance teams no longer need to rely on IT as gatekeepers to critical data. With a well-governed EDM strategy—rooted in master data management, domain-aligned governance, and self-service pipelines—finance can own reporting, analytics, and scenario modeling independently.
By reducing dependency on IT, finance accelerates insight, supports better enterprise applications, leverages data analytics consulting effectively, and primes the organization for future-forward AI solutions. Senior leaders should empower finance to lead EDM initiatives, align with digital transformation goals, and unlock enduring enterprise value.
Learn how enterprise data management (EDM) empowers finance teams to reduce reliance on IT, accelerate decision-making, and unlock value from data analytics consulting, AI solutions, IT solutions, and enterprise applications. STL Digital brings deep expertise in EDM implementation, enabling finance leaders to harness the full potential of their data ecosystem for agility, accuracy, and competitive advantage.
