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What is Finance Analytics?

Finance analytics, also known as financial analytics, provides differing perspectives on the financial data of a given business, giving insights that can facilitate strategic decisions and actions that improve the overall performance of the business. Related to business intelligence and enterprise performance management, finance analytics impacts virtually all aspects of a business, playing a critical role in calculating profit, answering questions about a business, and enabling future business forecasting.
 
Challenges with Financial Analytics
In many ways, CFOs find themselves pursuing two contradictory goals. As a cost-center for the business, finance must honor stringent cost reduction imperatives and flat budgets. Yet at the same time, growing regulatory and management requirements demand that CFOs provide unprecedented levels of financial transparency and decision support.
 
CFOs are being asked to integrate big data at a time when their own financial house is probably not entirely in order—or at least not in an order that provides necessary, actionable insight into detailed results. Too often, CFOs rely upon a web of unnecessarily complex, disconnected financial systems that require significant manual, error prone reconciliation and validation labor. This can lead to inconsistently or inaccurately reported results, as well as the internal “data struggles” that erupt when divisions have conflicting definitions of net revenue, gross margin or selling expense. The resulting arguments delay management’s decisions and negatively impact their quality.
 
As stewards over the financial data reported to both regulators and external stakeholders, CFOs must step into the breach and advocate improved data management practices that settle conflicts involving any information that impacts the financial statements. It is the only way to ensure that their companies can operate with a reliable, transparent single view of total company performance.
 
CFOs need more analytical capabilities than ever before because General Ledger data is no longer enough to meet regulators’ and stakeholders’ demands for transparency. Financial, management, and regulatory reporting all require greater sub-ledger detail (e.g. accounts receivable, inventory, and accounts payable) than in the past, as well as the ability to integrate increasing amounts of non-financial data (e.g. warranties, supplier, and customer). With the right infrastructure and a data-driven orientation, Finance can assist all aspects of the business in making more informed decisions. One of the key things that we see CFOs tasked with is Optimizing Order to Cash and Procure to Pay Processes. Meeting this challenge requires detailed linkages between financial statements and the Sub Ledger details which aggregate up into the General Ledger. This requirement not only meshes well with Finance’s traditional financial data steward role, it is often a natural progression at the growing number of companies whose CIOs report to the CFO.
 
Core Capabilities of Finance Analytics
To ensure their departments are data-driven, CFOs should work with IT to embark on a phased journey toward a simplified finance systems architecture that eliminates redundancy, leverages integration and maximizes automation. By uniting all of finance’s diverse data sources—from point of sale devices, consumer billing or mortgage loan systems to brand-name and homegrown ERPs, to accounting hubs and rules-based cost allocation calculation engines—around a single, integrated data repository, the CFO organization can transform its effectiveness and efficiency.
Many financial organizations achieve this state by redesigning their financial systems architectures with five core capabilities in mind: agility, sustainability, extensibility, predictability, and accountability.
 
  • Agility: relates to the CFO’s ability to respond to and promote change.
  • Sustainability: financial analytics built on a decision-making environment that can be continuously updated and evolved with minimum effort.
  • Extensibility: architectures designed with an eye on future data types that, when married with initial ones, will generate incremental business value.
  • Predictability: hinges upon how revenues and costs interact, giving CFOs detailed operational insight to identify and act on priority activities that can improve future profitability and help avoid unnecessary costs.
  • Accountability: a framework that aligns strategy and execution across the enterprise with the goal of running the business on an agreed fact base through a common set of metrics.