Among the relationships that technology teams have with other business departments, the potential for improved IT-finance collaboration is quite possibly the most under-explored. It\u2019s especially poignant when we consider the extent to which financial data can steer business strategy for the better.\n\nTake, for example, the ubiquitous and unassuming concept of free shipping in ecommerce. Today, it is a no-brainer for all online retailers, but a few years ago, it wasn\u2019t as obvious.\n\nJason Child, now CFO of SaaS company Splunk, tells the story of his time at Amazon\u2019s Financial Planning & Analysis (FP&A) department. Way back in 1999, his team did a cost-benefit analysis of the free shipping model, which is arguably one of the key drivers of Amazon\u2019s stupendous growth.\n\nThey tested free shipping as a lever against a 10% discount on each order and found that the former generated twice as much business.\n\n\u201cThere was a small group of us that had a meeting with Jeff Bezos, and we asked how we can make this affordable every day including the impact of cannibalization, which is people already paying for free shipping,\u201d recounted Child. \u201cFP&A came up with the idea of a 5-day delay, where those who wanted free shipping would face a 5-day delay so it would be a separate class.\u201d This led to the birth of Amazon Prime, which now has 200 million members paying $13 each per month.\n\nThis is the impact of data-driven financial analysis \u2013 or what is termed FP&A \u2013 in the business context. FP&A has the potential to transform the value proposition, operational model, strategic direction, or even the business model of a company.\n\nHowever, like most data-driven practices, FP&A is bound by the shackles of reporting, control, and compliance. Research by DataRails showed that inefficient data processes and dysfunctional financial reporting costs US businesses a staggering $7.8 billion a year. Out of that, $6.1 billion is lost to low-value, manual data processing and management while $1.7 billion of revenue is left on the table because of Amazon Prime-like innovation not happening.\n\nLet\u2019s study the challenges that a lack of timely and accurate data places on financial planning and explore how automation can help you surmount them.\n\nPoor quality data\n\nOne of the most common problems finance teams face is the quality and reliability of the data they collect. Even though they usually have access to accurate sources of data, the data is prone to inaccuracies over time as it is shared with and analyzed by multiple people or teams. More so when there\u2019s manual copying-pasting involved.\n\nThe end result is that there is no single source of truth accessible to the CFO and senior management, which slows down (or worse, introduces errors into) the decision-making process.\n\n\u201cFinancial institutions are operating in a complex, data-hungry environment. Unfortunately, they have fallen behind when it comes to automation and data integration practices, despite industry-wide recognition of the merits associated with an effective data strategy,\u201d said Wayne Johnson, CEO & Founder of Encompass.\n\nData virtualization \u2013 integrating data from multiple sources, across multiple applications and in multiple formats \u2013 provides a clear path to information unity here. Analysts can retrieve and manipulate data without knowing where it is physically located.\n\nFailure to act on real-time data\n\nCooperation between IT and Finance has never been more important in scenario planning, as companies try to move from crisis-mode to recovery-mode in the wake of the COVID-19 pandemic.\n\nAccording to a survey by Workday, nearly half of C-suite respondents were worried that their organizations couldn\u2019t analyze real-time data to make timely decisions or respond quickly enough to unpredictable market changes. Finance executives are struggling to generate, reconcile, access, and mine high volumes of data.\n\nThis doesn\u2019t come as a surprise, because less than half of those involved in annual budgeting and planning activities say they use digital technologies to perform their analyses. Compare that to sales and marketing, where over three-quarters of team members routinely make use of automation.\n\n\u201cIt\u2019s no use if you answer a question two months from now when you have to make an important decision on pricing or channel tomorrow,\u201d mused Valerie Martin, Finance Director at Autodesk.\n\nLost productivity\n\nStrategic FP&A is critical for integration, performance management, risk analysis, forecasting, and modelling across multiple business functions. The truth, however, is that finance teams are spending too much time performing manual tasks such as account reconciliation and financial close \u2013 in other words, sorting and organizing data instead of analyzing it.\n\n\u201cSince COVID-19, the role of financial planning and analysis has gained even greater momentum as businesses seek better understanding of their numbers. However, despite more than a decade of efforts, the daily life of an FP&A professional still involves strategy-sapping manual processes, including identifying and correcting errors, updating reports, and collecting data,\u201d lamented Prof. Mikhail B. Pevzner of the University of Baltimore\u2019s Merrick School of Business. \u201cThis is essentially depriving both companies and the wider US economy of billions of dollars of economic opportunity.\u201d\n\nInaccurate forecasts\n\nOperations, productivity, integration, technology, everything takes a back seat to the bottom line. Revenue forecasts are always top-of-the-mind for CEOs, because that\u2019s what dictates the flow of capital in the present.\n\nAnd yet, a paltry 1% of the world\u2019s biggest companies hit their finance forecasts precisely, per a KPMG study.\n\nThe corresponding loss in investor confidence is devastating. The study also found that whenever the revenue deviated significantly from predictions, the company\u2019s share price suffered for up to four quarters.\n\nWhile cloud-based financial forecasting solutions and ML-based algorithms can help you collect, mine, and gather data as well as run different scenarios, having optimized and consistent processes is often as important as having the best technology.\n\nAutomate your planning and plan your automation\n\nGartner estimates that by 2024, three-quarters of all new FP&A projects will extend their scope beyond the finance domain into other areas of the enterprise. Cloud-based solutions are already growing their automation capabilities to extend financial planning and analysis to different functions such as HR, sales, and supply chain management.\n\nConventional systems that also perform finance operations (such as ERP) depend on manual entries to a large extent and are prone to errors and discrepancies. However, the rise of AI-based software has accelerated finance automation, which Gartner defines as \u201ctechnology that integrates machine learning and artificial intelligence for use in areas such as financial analysis, payroll administration, invoice automation, collections action, and preparing financial statements, reducing the need for human intervention in these activities.\u201d\n\nCompanies that use finance automation can speed up and improve processes such as financial close, a lengthy, effort-intensive monthly process for recording and official reporting of transactions. Automating some or all of the multiple steps and submissions in this process improves accuracy and saves time spent on menial tasks.\n\nFurther, supporting technologies such as document automation and robotic process automation (RPA) enable auto-generation of documents from pre-existing text and forms as well as screen scraping and OCR to extract, validate, and consolidate financial data.\n\nKPMG estimates that businesses can realize cost savings of up to 75% by automating finance operations, given faster turnaround times and less human intervention.\n\nThat said, automation doesn\u2019t do away with the human element in financial planning. On the contrary, it enables financial analysts move away from everyday reporting to focus on big-picture analytics and dynamic planning.