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Crunch time series for CFOs: It's time to get serious about data

It seems fundamental: Of course, data is vital. No finance leader would say differently—until you ask about other priorities. There’s cost management and performance. Growth. Talent. Compliance. And on goes the list. But can you master those areas if you don’t have your data under control? No matter what people say or do, data really is central. Do you treat it that way? It’s time for CFOs to get serious about data.

Data and analytics: the anchors keeping everything in place

Everywhere a CFO turns, something underscores the need to care about data prioritization right now. Compliance with new regulations and demands for transparency. Supporting agile and effective decision-making amid rapid change, and reacting to market and stakeholder demands as business cycles continue to shorten.
Data is even central to the hiring and upskilling that keeps a finance organization on its toes—and in hiring and retaining talent with data skills, the competition includes not only Finance but the whole business world.

When finance treats data as a first-tier priority, it can excel across more than one dimension. But where to start—and how? Our Crunch time report takes an in-depth look at how CFOs can change their organization’s approach to data by putting itself to the test and finding a North Star strategy in the answers.

It’s always Crunch time: It’s time to get serious about data.

Time to put your priorities to the test

What does “getting serious about data” look like—especially if you believe you already are? Ask yourself whether you:

  • Consider all data important

  • Don’t have finance roles and career paths for “data people”

  • Focus only on financial data

  • Spend most of the time in spreadsheets

  • Don’t have a formalized finance data organization

  • Aren’t thinking about automating data

It’s time get serious about data prioritization if…

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.

Data prioritization: the path ahead

Data is an asset. Acquiring it and managing it carries costs. You should expect a return on that investment—and no investment produces a return if you take it for granted. From sourcing to cleansing to governance, often across multiple legacy systems, data is a resource you need to take control of and put to work.

From the top down, your Finance organization should have a North Star data strategy. Where do you want to go? How can you get there? What benefits can you realize? A clear data strategy is a necessary bedrock for defining roles and responsibilities, determining priority levels, and establishing accountability. You’ll also need a person at the strategic table who governs the data lake and is the custodian of the company’s data management policy.

It's Crunch time.

Getting serious about data prioritization is no longer an incremental need for Finance. It’s a transformative one—or a reason transformation might fail. Data is raw material, and it doesn’t turn into information, insight, plans, or decisions until it’s managed and interpreted. Doing that at human scale is simply not feasible today.

For many Finance organizations, data is an area in which they have to play catch-up. But that just means they have more opportunity waiting to seize. The good news is there are more tools than ever to carry that process forward.

It won’t be easy. But then: If the way you approach Finance data isn’t hard, you’re not serious about data prioritization. The work is there. The benefits are clear. Time to get started.

It’s crunch time for finance

Explore other reports and guides in our Finance in a Digital WorldTM “Crunch time” series, and read case studies about digital transformation in the finance function. Whatever your interest, one thing is clear: From cloud computing and robotics to analytics, cognitive technologies, and blockchain, a new class of digital disruptors is transforming how the work of finance gets done.

Explore the crunch time series

Deloitte can help

Changing the world from the inside out.

Our Finance Labs explore the “art of the possible” and define your Finance Transformation strategy, bringing to life potential use cases, road map priorities, and future-state benefits. Contact us to learn more.

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David Pierce

David Pierce

Managing Director | Analytics + Information Management
+1 703 251 4088
Putri Sukardi

Putri Sukardi

Managing Director | Risk and Financial Advisory
+1 513 319 3103
Srikanth Tamma

Srikanth Tamma

Managing Director | Enterprise Performance - SAP Package Technologies
+1 214 840 7992

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