The Role of Data Modernization in Finance Transformation

Key Elements of a Modern Finance Transformation

Finance is one of the significant aspects of any business. The team help to analyze behaviours and trends, current marketing indicators, and past results to identify the organization’s growth.

Finance transformation involves modernizing the finance function to make it data-driven and digital-first. The objective is to be a highly optimized and automated finance function capable of delivering faster, more strategic value to the business.

Redefine Finance Organization for a More Autonomous Future

The finance industry is currently in a multi-year shift towards becoming a digitally driven, self-governing entity. This will allow finance to provide more value as businesses adopt digital workflows and make digital investments. However, there are various obstacles to overcome, including digital conservatism, limited budgets, a shortage of skilled workers, and fatigue due to frequent changes in the finance sector.

Despite these challenges, CFOs can invest in digital leadership positions within finance as one solution to these problems. CFOs can modernize a finance organization by automating and optimizing finance processes through robot proceed automation, data and analytics, AI, and cloud-based ERP systems to embed digital capabilities in finance workflows.

What are the Components of a Modern Finance Transformation?

Data modernization predictions ensure many financial institutions are investing heavily in digital transformation initiatives, such as artificial intelligence, machine learning, and more. To lead toward finance digital transformation, the following components should be considered.

Organizational Structure

This includes defining operating models, roles, and responsibilities. A finance Shared Service Organization (SSO) digital program facilitates transition through:

  • Defining a technology roadmap
  • Identifying synergies
  • Driving cross-departmental digital enablement

There are talent implications for this revised, shared service operating model. Due to increased specialization in recent years, the unique insights that finance staff have into the individual business units they serve have reduced. Although finance has a unique feature to see patterns across business units or “portfolio-level insights.”

Digital Cohesion

Moving away from siloed digitization towards digital cohesion needs a finance organizational structure that embeds closeness between digital support staff and end users. Methods to do so:

  • Invest in a finance IT team: It decreases dependency on enterprise IT and develops finance-linked technology expertise. It allows a more informed process for discovering and choosing digital investments for finance. For any organization, such investments are a high priority.
  • Create an indirect reporting line between a digital champion and finance IT: A digital champion for finance is an individual who operates digital decision-making and accountability- along with recruiting and leading a focal digital transformation team. Building a reporting line between this individual, IT, and the CFO allows more transparency across digital investments.

Digital Finance Competencies

There are five common types of digital competencies in finance:

Technological literacy: Explained as the potential to exploit digital technology to lead to better outcomes, technological literacy allows finance staff to utilize machine learning, robot process automation, and natural language processing. This helps finance leaders to know which digital software solutions exist and how they help automate finance activities.

Digital translation: Finance leaders need to explain- or translate- critical features related to digital processes, technologies, and systems to stakeholders. Digital translation increases the quality and applicability of financial and non-financial data for decision-making. Further, it explains data-driven insights and where they come from.

Digital learning and development: It enables employees to keep pace with the increasing evolution of digital technologies and attain new digital knowledge via guided learning and practical and technological application.

Digital bias management refers to the ability to comprehend and reduce bias in applying ML. With appropriate digital bias management knowledge, you can:

  • Discover bias in data.
  • Validate algorithms before they go live.
  • Discover ML training data sources that reflect the proper business context.

Digital ambition: The capabilities that finance staff need related to digital ambition include:

  • Practising new approaches and technologies when there is no clear objective.
  • Knowing about a technology that is rolled out to the broader team.
  • Overcoming difficulties with new technology or through stakeholders resisting using it.

Cross-Functional Connections

Networks across subfunctions across the finance team drive collaboration and help ensure financial mechanisms run smoothly. They help to ensure that finance digitalization is cohesive and not siloed in subfunctions. Networks facilitate breaking down silos and cross-functional coordination. Finance managers should support current and new networks which:

  • Confirm all voices are listened to and valued.
  • Offer routes for information sharing.
  • Allow peer-to-peer learning.
  • Define clear usage rules.
  • Judge ideas by their value and not only through who contributed to them.

Manage support networks by involving and championing them as a channel for sharing digital lessons and insights. Few networks must have formal status as a unit of excellence or community of practice; others must be informal to allow rapid connections.

Benchmarking and Spend

This involves you to analyze your spending against your peers.

Today’s modern finance companies have chosen a cost optimization approach- against cost-cutting- that includes structured productivity and efficiency improvements. Moving ahead, the finance leader will progress toward value optimization driven by business-based analytics and reporting strategies.

Spending benchmarks are the critical analytics that informs value optimization. The top 10 benchmarks- mentioned here as cross-industry numbers are:

  1. Finance spending as a percentage of revenue. This underlying metric allows finance managers to comprehend their costs in relation to peers. A baseline enables the function to investigate ways of decreasing expenses, reallocating resources, and/or developing new capabilities.
  2. Distribution of finance spending by asset grouping. The allocation of funds across employees, technology, and outside consultants demonstrates the finance function’s preferences regarding different expenditures. According to Gartner’s cross-industry report, internal personnel and third-party contractors’ remunerations comprise over 80% of finance expenditure.
  3. Distribution of finance spending by process grouping. The data serves as a benchmark for evaluating the proportionate allocation of the finance budget towards activities like budgeting, forecasting, analytics, and reporting. Reports state that the largest portion of the budget, 26%, is directed toward accounting and reporting.
  4. Invest in external experts and support. Finance organizations commonly engage in process outsourcing and seek consultation from external advisors. According to Gartner’s benchmarks, 22% of external expenditures are allocated towards outsourcing, while 78% is designated for consulting services.
  5. Finance headcount per billion in annual revenue. Through this benchmark, finance executives can evaluate the extent of their staffing investment relative to their counterparts. As per the analysis, the benchmarked finance headcount amounts to 97.5 full-time equivalents (FTEs) per one billion USD in annual revenue.
  6. Distribution of headcount by role. Headcount distribution enables managers to assess their company makeup compared to their peers. Companies with higher proportions of senior staff sometimes have higher personnel speed.
  7. Distribution of headcount by process grouping. Assessing headcount by process categories can aid in pinpointing inefficiencies. Typically, the largest percentage of personnel, at 31%, are employed in transactional finance.
  8. Finance compensation spent per staff employee. This benchmark compares incentives and staff pay with those of peer companies. The median finance compensation per employee is around $115,000.
  9. Emerging finance technology investments. Benchmarks for tech spending aid finance executives in determining whether their investment level corresponds to the company’s objectives. The primary areas of technology investment in the finance function are cloud-based enterprise resource planning (ERP), with 64% of organizations planning to invest, and advanced data analytics, which is a priority for 57% of companies.
  10. Finance technology spends to run, develop, and transform the business. This concept was introduced in 2020 for benchmarking IT spending.

Maturity Measurement

This step needs you to assess your progress toward autonomous finance. Making progress on modernization needs investment in critical areas. CFOs and their teams should calculate performance across the core management activities and objectives, which includes digital finance.

While evaluating, rate activities depending on significance, maturity, and prioritization. This signifies the finance function’s current maturity level for core management activities and overall maturity score that helps CFOs prioritize digital investments.

Bottom Line

Digital finance transformation enables the service industry to automate manual tasks and free finance talent to emphasize more value-added activities. AI and data and analytics for finance also offer vital insights to assist decisions in the company’s revenue-generating business units.


Anjali Goyal

Anjali Goyal is a content writer at TechEela. She helps businesses increase their online presence with optimized and engaging content. Her service includes blog writing, technical writing, and digital marketing.

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