You consider all data important

When in doubt, capture as much as you can, and sort and refine it later. Right? Wrong. The more intake you have, the more effort and bandwidth it takes to load, define, and govern it.

What serious looks like:

• Define the data that helps you understand the value drivers of your business and support decision-making aligned with your business, helps you run your business, and helps to inform your leadership.
• Establish a data model and curated data sets that provide a standardized way of capturing and reviewing data aligned to the different ways the data will be used.
• Create and use common standards and data tiers that harmonize data from different sources, determine what data matters, and dictate how to cleanse and prepare it.
• Align your data to a realistic view of which downstream processes it is meant to inform.
• Adopt robust governance that can meet continually changing data as well as scenarios like divestiture or management structure change.
• Match requests for information to the performance management expectations that data is meant to support—no more, no less.

You focus only on financial data

Your work helps drive enterprisewide decision-making linking tax, statutory accounting, financial planning to forecasts and models spanning commercial, supply chain, operations, talent and beyond. Your data sources are just as diverse. If data streams don’t align, knowledge can’t turn into insights.

What serious looks like:

• Identify operational data that can work alongside financial data to help inform management decisions.
• Plan for tax, statutory accounting, and other functions from the start, not as an afterthought.
• Present financial results in context with proactive inclusion of relevant data—for example, you might have taken a loss, but it was less than your competitors’.
• Integrate and coordinate data from systems outside finance.
• Align from the beginning on how data originates from vendors, customer orders, SKUs, and similar sources with the appropriate attributions.
• Establish clear and tailored controls based on the type, source, and usage of data.
• Develop accountability standards for the use of operational and third-party data.
• Look further afield for newly relevant sources (like ESG, DEI, and sustainability data) that affect your organization.

You don’t have a formalized finance data organization

Formalized ownership of data standards and data quality is key to effectively managing data, and without ownership and governance, its power is lessened. The team that oversees data should be able to name all the stakeholders that use it, and its sources of truth. And the people who use data should understand how it’s created and delivered.

What serious looks like:

• Establish the business case and identify innovative and feasible funding mechanisms to help formalize and transform the organization.
• Develop a culture of accountability around data that has policies and procedures regarding roles and responsibilities, with goals and objectives tied to performance.
• Define a clear governance structure with data stewards accountable for specific data sets.
• Govern data at the rate you create it.Tie data decisions back to business reasons.
• Shift your mindset from cleaning up data one time to an ongoing approach that creates, cleanses, and maintains data in pace with the business.
• Build adequate tools and a technology infrastructure to store, process, analyze, and report on data.

You don’t have Finance roles and career paths for “data people”

If the career path you offer data professionals in your Finance organization is nonexistent, then you’re going to lose talent. Similarly, if you have Finance employees who feel their job titles make them “non-data” people, you need to do more. When you compete for data talent, you’re competing with the whole world—so approach it that way.

What serious looks like:

• Start by knowing what business problems your data needs to address and how this can inform your talent needs and data competency development approaches.
• Establish career paths that align data capabilities with functions within the Finance organization.
• Turn to influencers from across the organization to help enhance internal Finance data capabilities including supporting upskilling and knowledge sharing.
• Prepare to adapt roles based on industry changes, such as ESG reporting.
• Develop a talent acquisition and retention plan for data people that offers competitive compensation, benefits, impact, mentorship, and trainings, etc.

Your team is spending more than half their time in spreadsheets

If teams are building your reports in spreadsheets and slide decks, you are not using a vital resource: your ERP, which likely cost tens of millions. It can help your organization source, curate and use data in ways that support not only traditional reporting, but also leading-edge functions such as predictive analytics and machine learning.

What serious looks like:

• Be patient and fix issues with your technology enablement over time.
• Pursue ERP integration one process at a time instead of all at once, and prioritize.
• Interrogate the ways ERP integration can deliver more timely and useful insights, and work backward from business needs.
• Prioritize data availability in decisions about sourcing, formatting, and hosting.
• Keep your core ERP clean, with a common set of processes for each enterprise, and have a plan of what data is housed in your periphery systems (e.g., EPM, reporting).

You aren’t thinking about automating data

The volume of data and the ways it’s used continue to grow, and automation is also increasingly the key to maintaining that availability. But “more” doesn’t translate to “better” in a strictly linear way: There is a tradeoff between how rich your data is and how efficiently Finance organizations can operate in transaction processing and closing the books. Keeping up with scale means automation.

What serious looks like:

• Establish controls and rules that enforce checks on data at the point of creation.
• Enable self-healing data supported by ML-driven data quality standards.
• Invest in self-corrective and self-healing technologies to manage master data coherently through AI/ML-powered chatbot-based workflows and assets that can evolve with your data needs.
• Empower humans to work with machines with a focus on exceptions that require human intervention and context.
• Develop reconciliation and controls to improve and continuously maintain data quality.