Data Excellence in Corporate Finance: Why CFOs Can’t Wait for the New ERP

The ERP Trap: Don’t Wait, Start Now

The notion that a new ERP platform—like SAP S/4HANA—will instantly resolve finance’s data issues is a myth. In practice, these major implementations become multi-year journeys marked by disruption, complexity, and hidden costs. More than 60% of S/4HANA rollouts run past deadlines and budgets, and during those years, business needs and risks keep evolving

As a CFO or finance leader, waiting for a “perfect” technology platform to fix legacy problems is a luxury fast-moving organizations simply don’t have. The business partners and the market’s demands for finance leadership won’t pause for the new ERP. Real progress on data culture, governance, and analytics has to start now.

Why CFOs Must Prioritize Data Culture Now

·      ERP implementations take time: Even a typical S/4HANA rollout may run 18–36 months or more, during which fragmented data and manual processes persist.

·      “Standard template” launches erode quality: Trying to force a one-size-fits-all solution creates shadow systems and manual workarounds for the +30% of business needs that don’t fit the template. This means that a shadow system(s) will be created to meet the customization need for the business and other stakehodlers.

·      Data quality is a continuous journey: System upgrades don’t automatically solve for inconsistent definitions, outdated records, or process gaps.

·      Business risks are immediate: Market volatility, business model changes, regulatory shifts, and stakeholder demands require proactive response—now, not after the next go-live.

·      Culture trumps tech: The teams with strong data stewardship, shared accountability, and true collaboration outperform—regardless of the platform.

·      AI and analytics depend on good data: Trustworthy data and governance are prerequisites for successful automation and insight, with or without next-gen ERP.

Practical Steps While ERP Projects Progress

·      Cross-functional data governance: Set clear ownership in finance, accounting, and operations for definitions, stewardship, and access—independent of the system in place.

·      Incremental quality checks: Use automated validation and reconciliation tools outside the ERP to catch errors and build trust.

·      Promote data literacy: Train finance teams to question assumptions and validate sources, not just rely on spreadsheets and legacy extracts.

·      Leverage analytics tools: Implement cloud-based analytics or data warehouses that can sit above fragmented systems and support quick wins.

·      Share early successes: Highlight and communicate how small data quality improvements can drive faster reports, fewer errors, or better forecasts.

Why Data Excellence Matters in Finance

In every finance role I’ve held, data has driven the core of what allowed us to move beyond gut feel. Trust in numbers—not just intuition—was what built teams’ credibility with business partners, leadership teams, auditors, and board. I’ve seen mistakes born from overdue or incomplete data triggering uncomfortable investment decisions, cost money, and erode confidence. Accurate, reliable data is the bedrock for finance: accounting, compliance, scenario modeling, risk management, and timely decisions

What Is a Data-Driven Culture Looks like in Practice?

A real data-driven culture isn’t just talk. It’s embedded habits and behaviors. The best teams I’ve worked with made data as natural a part of the conversation as business context. Whether in treasury, budgeting, or investment, every analyst and manager felt responsible for data reliability, not just IT. Yet, the problem is widespread: a Bean & Davenport survey showed 72% of executives still say their companies lack a strong data culture; more than half admit they don’t treat data as an asset—even in finance, where our entire role orbits assets and liabilities. In my experience, teams that fail to change this quickly get outpaced by those that use data aggressively for scenario planning and competitive insights.

Common Data Challenges (and What Actualy Works)

Any finance leader will recognize these pain points:

·       Incomplete Data: Missing cost or revenue data can distort financial forecasts and cause errors in reporting, limited investments and M&A business cases that can can lead to flawed integration models.

·       Inconsistent Data: When subsidiaries, divisions, or different systems use different naming conventions for accounts, cost centers, or currencies, consolidated reports are difficult and error-prone. For example, if one region reports “OpEx” monthly while another uses “Operating Expense” quarterly, rolling up actuals versus budget becomes a risk.

·       Outdated Data: Using last month’s closing balance, rather than real-time cash positions, can risk treasury decisions or overdraft fees. On the flip side, data from older systems are not accessible limiting the data available to train AI models.

These are exacerbated by the pace of automation and AI adoption; FP&A teams frequently find manual data cleaning and legacy spreadsheet processes can’t keep up with transaction volumes or business demands.

Personal Corporate Example:
We had consistent forecast errors in the monthly/quarterly sales and discovered that the divisions were using different dates for data extractions and sales adjustments among other reasons. By standardizing classifications, dates, and automating quality checks, finance improved both forecast reliability and audit confidence.

The Data Misuse: Finance Pitfalls

·       Misinterpreting Data: Relying solely on headcount to assess productivity without analyzing functional classifications, differences across countries, project ROI, or restructuring cases, leads to misleading cost allocation decisions.

·       Metric Misuse: If FP&A teams focus only on EBITDA but ignore cash-flow volatility, they risk missing signals of underlying financial health.

·       Overreliance on Models: Automated financial forecasting can overlook qualitative business risks, such as regulatory changes, market access (lifescience), VAT changes, or sudden market shocks.

Data Silos and the Integration Slog

Siloed finance data remains a daily headache, especially in matrixed organizations. I’ve worked in environments where treasury, regional finance, and corporate FP&A operated parallel systems and didn’t even speak the same data “language.” Routine reporting became a herculean, manual cross systems reconciliation affair—until we forced the issue with cross-team one source of truth, customized data lakes,  and standardization.

Governance and Compliance for Finance Data

Finance leaders must enforce rigorous controls:

·       Define access roles so only authorized teams can view sensitive financials, internal audit findings, or payroll data.

·       Embed real-time monitoring for report changes, audit trails, or model versioning.

Governance also includes ethical stewardship of sensitive data—especially under SOX, GDPR, or industry regulations. Finance teams need systematic policies for handling, archiving, and anonymizing personal or transaction data.

Moving the Culture Needle – One Step at a Time

In my experiance, culture doesn’t shift through manadates alone. It takes::

·       Storytelling: Frame successful audit cycles or budgeting turnarounds as data-driven wins, building pride and buy-in across finance functions.

·       Peer Learning: Onboarding new FP&A analysts with peer-led sessions on data lineage, systems map, automated reconciliation, and scenario modeling boosts adoption and trust.

·       Collective Responsibility: Cross-functional “fix teams” can tackle root-cause reconciliation failures, linking process owners, IT, and finance to own systemic corrections.

Translating Data into Products: What Worked

Making data practical has meant building:

·       Executive dashboards grounded on aligned data lakes with daily updates for real-time scenario planning.

·       Chatbots to answer live financial FAQs or automate account reconciliation queries

·       Predictive models for forecasting revenue, cost spikes, or cash balances

This hands-on DaaP (Data as a Product) enables finance teams to convert raw financial data into business insights that support faster, more accurate decisions across the organization.

Closing Thought

In today’s fast-moving business landscape, CFOs and Finance Leaders should lead from the front, acting now cannot wait for the promise of ERP rollouts  to realize their data aspirations. They should lead from the front to cultivate a robust data culture is now—building the foundation on people, process, and governance that will enable smoother technology transitions and sustainable finance excellence.