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Data strategy needs data governance

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Ultimately, it all comes down to data. This is according to Chris Millet, a Baker Tilly director specializing in client engagement and managed services during a talk Thursday at the Finance and Accounting Technology Expo in New York City. While there are powerful technology solutions available to professionals today, especially AI-based ones, none will perform their best without strong data sources paired with a robust governance framework. This not only avoids risk but drives the organization’s strategic goals going forward. 

“We should be looking at our data as a strategic asset. It really allows us to transform our data into a competitive advantage for our organization, and really bring it into alignment with our overall business goals and strategy, and make sure that we’re looking at that data quality and governance, and really unlock that potential in a robust way,” he said. 

He emphasized the importance of centralizing disparate data sources as policy, as he believes it is only when we are able to look at all the data together that we are able to understand the full picture of what is happening in an organization. When information is stored across separate silos that don’t connect or communicate, it becomes difficult to trust the system, as there is no single source of truth. 

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“[If] none of my systems talk to each other, I can’t really understand what’s happening in this system versus what I am trying to accomplish overall and understanding my data. What is that single source of truth that I can really trust?” he said. 

Of course, even if one does manage to centralize one’s data sources, it does no good if the data itself is suspect. This is where governance comes in, and he said there are numerous solutions available to help. Regardless of which ones someone chooses, he said they should be able to automatically point out where data hygiene is less than perfect. 

“[It] can say, hey, you’ve got this many transactions that are missing this data point. You’ve got this many records that don’t have this piece of information that’s critical to your analysis. And so your tool should highlight those things and be able to direct you to where you need to fix your data, because it doesn’t matter what comes out on your dashboards or reports or AI [if it is] garbage in, garbage out,” he said. 

While there are many ways to approach data governance, the very first step should be defining data ownership, according to Millet. Ask who owns the data from what sources. Does the warehouse team own the operations data? Does the finance team own the finance data? Does the sales team own the customer relationship data? Who actually owns the governance around the data that’s going to be consumed down the line? This also includes defining which users have access to which data sets, not just the inputs and outputs. This gives a true sense of both where the data is coming from and who is consuming it. 

He also talked about how, regardless of what solutions are deployed, they should also highlight compliance gaps. 

“And I’m not just talking about external compliance needs. You have third parties out there that may have certain requirements of you around your data, but I’m talking just as much or more about your internal compliance to go along with your internal data strategies and policies, and then lastly, enhancing the data integrity. When we understand there might be something wrong, [we can] go ahead and improve that and iterate over time so the more I can use my tools to help me identify the exceptions to my rules, the better and better I should get, and the more I can trust that data long term,” he said. 

Millet added that organizations should make sure their data strategies and tools will scale as the organization grows. 

“We must be able to grow. This is not something we’re going to implement today, and then two years later, we’ve got to rip [it] out and do something else. It should be something that I can grow with over time, and be able to spread across multiple business systems,” he said. 

Finally, he stressed the importance of stakeholder engagement. Implementing the most sophisticated, powerful solutions will mean nothing if employees won’t use it. 

“If [you’ve ever switched] to a new system, whether it’s NetSuite or another system, we’ve probably all heard, ‘I don’t know what’s happening here, the reports aren’t right.’ Whatever the situation is, stakeholder engagement can only be there if they’re trusting what they’re seeing, and that the data strategy addresses that,” he said. 

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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