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IBM’s Vision user conference brings together customers who use its software for financial and sales performance management (FPM and SPM, respectively) as well as governance, risk management and compliance (GRC). Analytics is a technology that can enhance each of these activities. The recent conference and many of its sessions highlighted IBM’s growing emphasis on making more sophisticated analytics easier to use by – and therefore more useful to – general business users and their organizations. The shift is important because the IT industry has spent a quarter of a century trying to make enterprise reporting (that is, descriptive analytics) suitable for an average individual to use with limited training. Today the market for reporting, dashboards and performance management software is saturated and largely a commodity, so the software industry – and IBM in particular – is turning its attention to the next frontier: predictive and prescriptive analytics. Prescriptive analytics holds particular promise for IBM’s analytics portfolio.

The three basic types of analytics – descriptive, predictive and vr_NG_Finance_Analytics_09_too_much_time_to_prepare_dataprescriptive – often are portrayed as a hierarchy, with descriptive analytics at the bottom and predictive and prescriptive (often referred to as “advanced analytics”) on the next two rungs. Descriptive analytics is like a rear-view mirror on an organization’s performance. This category includes variance and ratio analyses, dashboards and scorecards, among others. Continual refinement has enabled the software industry to largely succeed in making descriptive analytics an easy-to-use mainstream product (even though desktop spreadsheets remain the tool of choice). Today, companies in general and finance departments in particular handle basic analyses well, although they are not as effective as they could be. Our research on next-generation finance analytics shows, for example, that most financial analysts (68%) spend the largest amount of their time in the data preparation phases while a relatively small percentage (28%) use the bulk of their time to do what they are supposed to be doing: analysis. We find that this problem is mainly the result of issues with data, process and training.

The upward shift in focus to the next levels of business analytics was a common theme throughout the Vision conference. This emphasis reflects a key element of IBM’s product strategy: to achieve a competitive advantage by making it easy for most individuals to use advanced analytics with limited training and without an advanced degree in statistics or a related discipline.

The objective in using predictive analytics is to improve an organization’s ability to determine what’s likely to happen under certain circumstances with greater accuracy. It is used for four main functions:

  • Forecasting – enabling more nuanced projections by using multiple factors (such as weather and movable holidays for retail sales)
  • Alerting – when results differ materially from forecast values
  • Simulation – understanding the range of possible outcomesvr_NGBP_08_predictive_analytics_underused_in_planning under different circumstances
  • Modeling – understanding the range of impacts of a single factor.

Our research on next-generation business planning finds that despite its potential to improve the business value of planning,  only one in five companies use predictive analytics extensively in their planning processes.

Predictive analytics can be useful for every facet of a business and especially for finance, sales and risk management. It can help these functions achieve greater accuracy in sales or operational plans, financial budgets and forecasts. The process of using it can identify the most important drivers of outcomes from historical data, which can support more effective modeling. Because plans and forecasts are rarely 100 percent accurate, a predictive model can support timely alerts when outcomes are significantly different from what was projected, enabling organizations to better understand the reasons for a disparity and to react to issues or opportunities sooner. When used for simulations, predictive models can give executives and managers deeper understanding of the range of potential outcomes and their most important drivers.

Prescriptive analytics, the highest level, help guide decision-makers to make the best choice to achieve strategic or tactical objectives under a specified set of circumstances. The term is most widely applied to two areas:

  • Optimization – determining the best choice by taking into account the often conflicting business objectives or other forms of trade-offs while factoring in business constraints – for example, determining the best price to offer customers based on their characteristics. This helps businesses achieve the best balance of potential revenue and profitability or farmers to find the least costly mix of animal feeds to achieve weight objectives.
  • Stochastic Optimization – determining the best option as above but with random variables such as a commodity price, an interest rate or sales uplift. Financial institutions often use this form of prescriptive analytics to understand how to structure fixed income portfolios to achieve an optimal trade-off between return and risk.

General purpose software packages for predictive and prescriptive analytics have existed for decades, but they were designed for expert users, not the trained rank-and-file. However, some applications that employ optimization for a specific purpose have been developed for nonexpert business users. For example, price and revenue optimization software, which I have written about is used in multiple industries.  Over the past few years, IBM has been making progress in improving ease of use of general purpose predictive and prescriptive analytics. These improvements were on display at Vision. One of the company’s major initiatives in this area is Watson Analytics. It is designed to simplify the process of gathering a set of data, exploring it for meaning and importance and generating graphics and storyboards to convey the discoveries. Along the way, the system can evaluate the overall suitability of the data the user has assembled for creating useful analyses and assisting general business users in exploring its meaning. IBM offers a free version that individuals can use on relatively small data sets as a test drive. Watson is a cognitive analytics system, which means it is by nature a work in progress. Through experience and feedback it learns various things including terminologies, analytical methods and the nuances of data structures. As such it will become more powerful as more people use it for a wider range of uses because of the system’s ability to “learn” rather than rely on a specific set of rules and logic.

Broader use of optimization is the next frontier for business software vendors. Created and used appropriately, optimization models can deliver deep insights into the best available options and strategies more easily, accurately, consistently and effectively than conventional alternatives. Optimization eliminates individual biases, flawed conventional wisdom and the need to run ongoing iterations to arrive at the seemingly best solution. Optimization is at the heart of a network management and price and revenue optimization, to name two common application categories. Dozens of optimization applications (including ILOG, which IBM acquired) are available, but they are aimed at expert users.

IBM’s objective is to make such prescriptive analytics useful to a wider audience. It plans to infuse optimization capabilities it into all of its analytical applications. Optimization can be used on a scale from large to small. Large-scale optimization supports strategic breakthroughs or major shifts in business models. Yet there also are many more ways that the use of optimization techniques embedded in a business application – micro-optimization – can be applied to business. In sales, for example, it can be applied to territory assignments taking into account multiple factors. In addition to making a fair distribution of total revenue potential, it can factor in other characteristics such as the size or profitability of the accounts, a maximum or minimum number of buying units and travel requirements for the sales representative. For operations, optimization can juggle maintenance downtime schedules. It can be applied to long-range planning to allocate R&D investments or capital outlays. In strategic finance it can be used to determine an optimal capital structure where future interest rates, tax rates and the cost of equity capital are uncertain.

Along the way IBM also is trying to make optimization more accessible to expert users. Not every company or department needs or can afford a full suite of software and hardware to create applications that employ optimization. For them, IBM recently announced Decision Optimization on Cloud (DOcloud), which provides this capability as a cloud-based service; it also broadens the usability of IBM ILOG CPLEX Optimizer. This service can be especially useful to operations research professionals and other expert users. Developers can create custom applications that embed optimization to prescribe the best solution without having to install any software. They can use it to create and compare multiple plans and understand the impacts of various trade-offs between plans. The DOcloud service also provides data analysis and visualization, scenario management and collaborative planning capabilities. One example given by IBM is a hospital that uses it to manage its operating room (OR) scheduling. ORs are capital-intensive facilities with high opportunity costs; that is, they handle procedures that utilize specific individuals and different combinations of classes of specialists. Procedures also have different degrees of time flexibility. Without using an optimization engine to take account of all the variables and constraints, crafting a schedule is time-consuming. And since “optimal” solutions to business problems are fleeting, an embedded optimization engine enables an organization to replan and reschedule quickly to speed up decision cycles.

Businesses are on the threshold of a new era in their use of analytics for planning and decision support. However, numerous barriers still exist that will slow widespread adoption of more effective business practices that take full advantage of the potential that technology offers. Data issues and a lack of awareness of the potential to use more advanced analytics are two important ones. Companies that want to lead in the use of advanced analytics need leadership that focuses on exploiting technology to achieve a competitive advantage.

Regards,

Robert Kugel – SVP Research

For most of the past decade businesses that decided not to pay attention to proposed changes in revenue recognition rules have saved themselves time and frustration as the proponents’ timetables have slipped and roadmaps have changed. The new rules are the result of a convergence of US-GAAP (Generally Accepted Accounting Principles – the accounting standard used by U.S.-based companies) and IFRS (International Financial Reporting Standards – the system used in much of the rest of the world). Now, however, it’s time for everyone to pay close attention. Last year the U.S.-based Financial Accounting Standards Board (FASB, which manages US-GAAP) and the Brussels-based International Accounting Standards Board (IASB, which manages IFRS) issued “Topic 606” and “IFRS 15,” respectively, which express their harmonized approach to governing revenue recognition. A major objective of the new standards is to provide investors and other stakeholders with more accurate and consistent depictions of companies’ revenue across multiple types of business as well as make the standard consistent between the major accounting regimes.

The impact of the new standards will vary greatly depending on the nature of the business. The rules cover companies that enter into contracts with their customers to transfer goods or services or enter  into contracts for the transfer of nonfinancial assets – unless those contracts are within the scope of other standards (for example, insurance or lease contracts). Their scope includes most industrial and business services as well as many that have direct-to-consumer relationships. Specifically, aerospace and defense, automotive, communications, engineering and construction, entertainment, media, pharmaceuticals and technology industries are likely to feel the greatest impact. Investment management companies that receive performance-based incentive fees will also be affected. However, it’s likely that some “contracts” covered by the new standards are informal. For instance, there is an implied contract when the seller of a video console ships a product with a game that has only half of the levels but promises to provide the remaining levels at a later date.

It has taken years to reach agreement because creating a codified approach to revenue recognition across all industries was a complex undertaking. It has required the standards bodies to go to the conceptual heart of revenue recognition to devise a common, workable approach. Implementation has been delayed to provide the affected corporations additional time to address the often significant process and systems changes they must put in place to adapt to the new standard. As of now, it looks likely that publicly held companies will begin applying them for annual reporting periods beginning after Dec. 15, 2017, and private companies a year later. Software vendors have been preparing for the new revenue recognition rules, some for several years.

Another main objective of the new standards is to simplify preparation of financial statements. That is unlikely to happen as companies affected by the new standards deal with the transition. In the long run, how well a finance and accounting organization uses software to manage contracting, invoicing, billing and the revenue recognition process will have a significant impact on the amount of work involved. Here software can help; it is a natural fit for principles-based accounting standards. In the absence of prescriptive rules, auditors must be able to confirm that a corporation’s accounting treatments were appropriate and applied consistently. Because softwarevr_ss21_errors_in_spreadsheets codifies these treatments and automates their application, it functions as a high-level control that, in theory, should make governance and auditing of this aspect of a company’s books much easier to enforce. Moreover, while the new standards may be more useful for investors, they may make a company’s statutory accounting numbers less practical for managing the business. To address this issue, software can reduce the workload involved in preparing management accounting. It also can facilitate a planning process that must be able to view the pro-forma numbers for future income statements, balance sheets and cash flow in both statutory and management contexts. We urge organizations to avoid the use of spreadsheets for this and other enterprise purposes. Our benchmark research finds that various types of errors are common even in the most important spreadsheets; errors that relate to revenue are likely to be material.

For those not familiar with the accounting issue being discussed, here is a quick summary of what has been driving the change. Revenue recognition accounting issues have a long history in the U.S. because of the early rise of a market for publicly traded software companies in this country. Rules grew in scope and specificity in response to successive scams and frauds involving inflated sales numbers perpetrated on investors. Many software companies’ revenue recognition policies were extremely aggressive until 1991, when the first rules were put in place. Those first iterations proved too feeble, and further revisions attempted to prevent abuse, but in the process the rules have become highly detailed and unwieldy. From the standpoint of U.S. companies, replacing the increasingly complex and onerous rules became necessary. It’s easy to make mistakes trying to abide by regulations that include more than 100 requirements governing recognition of revenues and gains. The new approach is aimed at reducing this complexity. For its part, IFRS relied more on principles to guide accounting treatment. Although that approach was less complex, it was also less comprehensive and harder to interpret. Dissatisfaction grew because for different reasons, US-GAAP and IFRS each were failing to present a consistent picture of the economic health and performance of companies, which is a core purpose of accounting. And harmonizing revenue recognition rules between the two systems has been a major objective of eliminating differences between US-GAAP and IFRS.

The particulars of the new standards are so detailed that I will highlight only three key points. First, “Topic 606” and “IFRS 15” employ a novel customer-focused framework that represents a fundamental conceptual change in how revenue is measured. Under the new approach, revenue is recognized when the customer gains control of some combination of a good or a service and not necessarily (as has been the case) when the customer acquires title to or takes physical possession of the asset or when a service has been billed. This interjects a considerable amount of opinion into the process. Moreover, sellers are required to parse the sales contract (which may be formal or implied) to identify separate performance obligations (if any) to the customer and allocate the transaction price to these individual performance obligations. Auditors will have to agree that the approach is sound and applied consistently. Complications to the process arise when a contract even implicitly includes multiple performance obligations such as the correct installation of machinery or periodic updates to firmware needed for the machinery to operate properly. To be sure, a good deal of uncertainly will be resolved over the next three years during the phase-in period, but the change is likely to be a major distraction to many companies’ finance and accounting organizations over the next five years.

Second, it will be interesting to see to what extent the new rules improve transparency of financial statements. The new framework hews to an approach that has academic consistency on the revenue line but can bend accounting’s matching principle (matching revenue to related expenses). For example, at this stage in the evolution of the rules, companies that have multiyear contracts will recognize revenues in some ratable fashion over the life of the contract but – in contrast with past practices – will have to recognize some costs related to the contract immediately. Wireless communications vendors, for instance, might find that the handset subsidy they currently provide to customers would have to be expensed at the start of the contract rather than over the life of the contract because the matching principle would no longer apply to the way revenues are classified. In this respect, the new approach is a more accurate reflection of the cash flows of the transaction but not of the economics or the nature of the customer relationship. Professional investors will be able to see through the numbers because they have the training to do so; ordinary individuals will not. To the extent that the new standards create a gulf between reported results and those that are meaningful to running the business, they will proliferate the reporting of non-GAAP results to investors.

Third, while the objectives of the revised revenue recognition standards are laudable and will benefit some investors in some respects, they may complicate corporate financial management in companies materially affected by the new standards. Those that use written contracts or discover they have somewhat contractual relationships with their customers will experience a more complicated revenue recognition process that demands more stringent control of a more complex and detailed record-keeping regime. Importantly, the customer-first framework is divorced from longstanding internal processes, management reporting needs and tax management. So accounting and planning for taxes, commissions, bonus plans and debt covenants may require more attention. Because of this, almost all companies that adopt the new methods of revenue recognition will have to adapt their financial management software and accounting practices to handle the new rules. They will need to review contract wording and related processes as well.

Let me repeat that there is a natural relation between principles-based accounting standards and software. In addition to ensuring consistency in treatment and facilitating governance and control, software also is capable of automating the process of presenting a company’s results from multiple perspectives in a consistent fashion. This is important because many companies will find that their statutory books alone will not provide the right numbers to manage their business. Although public company managements will want to see how their numbers look to Wall Street, they may find that these figures are inconsistent with business practices required to achieve sustainable long-term objectives. Software can systematize the simultaneous translation of events into increasingly divergent financial and management accounting contexts.

Many ERP and financial management software vendors have been preparing for the new revenue recognition rules. I see three main requirements for such companies in preparing to handle the new revenue recognition rules.

  • One is process management for the five-step process that determines when revenue can be recognized. That begins with identifying the “contract,” which may be formal or informal. After that it must identify the performance obligations established by the contract, determine the contract price, allocate that price to the individual performance requirements and recognize revenue when that obligation is satisfied.
  • An integrated contract management system or tight integration with third-party contract management products (including sales force automation) can ensure that the data regarding the contract can pass back and forth easily between systems. Invoicing and billing software must be elastic enough to capture the full palette of transaction information in order to provide the appropriate treatment of the debits and credits for that specific transaction. And it is necessary to handle any subsequent events related to that transaction such as cancellations, adjustments and modification of contracts. Processes vary across industries, and even companies in similar businesses may have slightly different contracting processes. So accounting software vendors must provide customers with unlimited freedom to define the characteristics they need to capture in the invoice and the specific accounting treatment of all of the components of that record based on those characteristics.
  • Finally vendors must have a method for instantly recasting results and plans accurately and consistently to satisfy financial and management accounting revenue requirements as well as cash flow for treasury management and a taxable view for tax management purposes. ERP and other software vendors will take different approaches in making this feasible depending on their software architectures. Thus it’s important for finance executives to understand that the approach a vendor might be touting is simply an adaptation to its existing architecture, rather than the best method for their company’s purposes.

It’s not clear at this point how much disruption the new revenue recognition standards will create in finance and accounting organizations. One reason is that it’s hard to know how external auditors will behave. In the United States, the shift to a principles-based methodology will challenge a generation of auditors who grew up in a rules-heavy environment. The framework is straightforward, but the implementation of the Sarbanes-Oxley Act is a cautionary tale of how unnecessary complications can needlessly disrupt the accounting function.

vr_ss21_spreadsheet_maintenance_is_a_burdenWhat should be abundantly clear, though, is that companies should avoid using desktop spreadsheets as much as possible in handling the revenue recognition process. Because of the complexity, it will be a nightmare to try use desktop spreadsheets for all but truly one-off calculations and prototyping work. Our research finds that in many situations spreadsheet maintenance is a burden, and this certainly will be the case here. Revenue recognition will be in a state of flux for years because of the highly predictable spate of rulemaking and “clarifications” of these treatments and maybe even requirements to recast numbers. Automating and controlling the flow of contract information from the negotiating phase all the way to invoicing can preclude the need for multiple adjustment, allocation and reconciliation steps. It can facilitate the process of creating statutory and management accounting statements and analysis of the difference between them as well as planning, budgeting and reviewing.

I recommend that companies that might be affected by the new revenue recognition standards review the adequacy of their ERP and financial management software to handle the new rules, including how it captures contract information in the sales process and passes it to the accounting system. They should examine their vendors’ plans for adapting to the new regulations to determine whether they are adequate for their specific needs. They should recognize that while software companies will be tempted to spread fear, uncertainty and doubt to generate business, some businesses really are in danger of being overwhelmed by the new rules.

Regards,

Robert Kugel – SVP Research

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