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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 prescriptive – 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 outcomes 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.
Robert Kugel – SVP Research
Revenue recognition standards for companies that use contracts are in the process of changing, as I covered in an earlier perspective. As part of managing their transition to these standards, CFOs and controllers should initiate a full-scale review of their order-to-cash cycle. This should include examination of their company’s sales contracts and their contracting process. They also should examine how well their contracting processes are integrated with invoicing and billing and any other elements of their order-to-cash cycle, especially as these relate to revenue recognition. They must recognize that how their company structures, writes and modifies these contracts and handles the full order-to-cash cycle will have a direct impact on workloads in the finance and accounting department as well as on external audit costs. Companies that will be affected by the new standards also should investigate whether they can benefit from using software to automate contract management or in some cases an application that supports their configure, price and quote (CPQ) function by facilitating standardization and automation of their contracting processes.
The soon-to-be-implemented revenue recognition standards (called ASC 606 or “Topic 606” in the U.S. and IFRS 15 in most other developed countries) will fundamentally change how companies that use contracts in business account for revenue from them. They will not affect those that rarely if ever use formal or implied contracts in the normal course of business. And almost all corporations that use standard contracts that cover a straightforward transaction (such as a one-time sale of some good or service) where the terms are satisfied within a relatively short period of time are likely to find little change to their accounting treatments and processes. However, corporations that don’t fall into these categories will benefit from a thorough re-examination of the structure and wording of their sales contracts and the processes they use for creating, negotiating and reviewing sales contracts.
To minimize the impact of the new revenue recognition standards on finance department workloads, companies ought to standardize sales contracts and automate as much of the order-to-cash cycle as possible. Although strictly speaking the new revenue recognition process requires companies to manage contracts one-by-one, companies can treat sets of similar contracts or similar performance obligations that are part of a contract in the same way if the overall impact on its financial statements will not be materially different from applying the same approach to the individual contracts or individual performance obligations. In other words, the specific wording of the sales contract is irrelevant if the substance of the contract or individual performance obligations that are part of that contract are substantially the same. Thus the objective of reviewing a company’s sales contracts is to find ways to achieve the highest possible degree of standardization (that is, making contracts, parts of contracts and performance obligations under those contracts identical) or commonality (achieving sufficient similarity to apply the identical accounting treatment). At the end of the review, controllers should be able to implement a process that can map all contract elements to a set of accounting treatments that are at least plausible under the new principles. (Compared to current U.S. accounting standards, the new approach to revenue recognition is more principles-based and far less prescriptive.) Such standardization is extremely helpful in a principles-based accounting approach because it will ensure consistency in how the company treats specific types of contracts and their specific elements. Doing so will facilitate internal reviews and external audits. It will also lay the groundwork for automating the classification of contracts and contract elements, which can reduce finance department workloads as well.
Up to now, a major concern in drafting sales contracts has been covering all the legal bases. Typically, how to organize a contract has been at best an afterthought. This will need to change. CFOs and controllers should insist that all of their sales contracts (or contract templates) be structured in a fashion to make it easy to account for them. By analogy, in the manufacturing world, engineers often constrain their designs to make a product easier or less expensive to produce (for instance, by using similar components across multiple products or relaxing tolerances) or cheaper to maintain (by making replaceable components easier to access). With the advent of the new revenue recognition standards, legal departments or outside counsel must now pay attention to the structure and wording of contracts to facilitate accounting and auditing processes. In negotiating the wording of a sales contract, company representatives must be trained to be sensitive to changes that can have a material impact on revenue recognition from the standard and also to understand when such changes will not make a difference. In the new revenue recognition regime, “sloppy drafting” now includes needless complexity or lack of standardization in a sales contract, not just ambiguities and omissions. Moreover, as much as possible, the wording of the contract should include language that clarifies the accounting treatment by the seller. For example, explicitly stating whether intellectual property that is part of a performance obligation is either symbolic or functional simplifies accounting and auditing by eliminating a potential ambiguity. Making the distinction explicit is useful because that characteristic determines whether revenue from that intellectual property must be recognized over the term of the contract (if it’s symbolic) or at a point in time (if it’s functional). It’s important for finance executives to work with their legal department or outside counsel to appreciate the importance of having sales contracts that minimize workloads for their department. Our benchmark research on recurring revenue suggests that it’s common for people working in one part of a business to be unaware of issues their colleagues in other parts face. For example, when it comes to invoicing the research finds a major disconnect between the finance department and the rest of the company. Nearly half (47%) of participants working outside of finance and accounting said they are satisfied with their company’s ability to produce invoices for their recurring charges, compared to only 29 percent of those in accounting roles. The gulf between the two reflects the reality that when parts of a business process are performed without regard to their impact on finance department operations, invoicing becomes a highly labor-intensive effort. Indeed, of those not satisfied with their invoicing, four out of five (79%) said it requires too much work, two-thirds (68%) said it involves too many resources, and more than half (54%) said it takes too long.
To be sure, standardization is either difficulipt or impractical for very large or complex transactions. And, in some cases, customers will insist (successfully) on writing the sales contract “on their paper.” (That is, the buyer’s side provides the contract that forms the basis of the negotiated result in order to better control the negotiating process and minimize the risk of the buyer being subjected to unfavorable terms and conditions.) However, unless these instances are common and unavoidable, they amount to exceptions that do not affect the need for consistency and commonality in a company’s sales contracts. Even in the case of exceptions, corporations should define and document a standardized framework and process for determining how to handle the accounting in the five-step revenue recognition framework laid out in the standards as easily and consistently as possible.
Invoicing and billing are a part of the order-to-cash process that benefits from automation. This process is relatively straightforward for companies that exclusively or mainly have contracts for stand-alone transactions where the performance obligations to the customer are met over a relatively short period of time (no more than a month or two). Generally, ERP or corporate financial management systems will be able to automate the process and related accounting.
However, companies that engage in subscription or recurring revenue relationships with customers, or those that have contracts covering longer-lived transactions (such as projects), will find it useful to have dedicated software to automate their invoicing and billing and serve as an authoritative source system that drives revenue recognition. Subscription and recurring revenue relationships often involve frequent changes to deliverables, which complicate invoicing and billing as well as the revenue recognition process under the new standards. In our research more than twice as many (86%) companies that use a third-party dedicated billing system said they are satisfied or somewhat satisfied with the software they use for invoicing as those that use spreadsheets (40%).
Finance executives in companies that will be subject to the new revenue recognition standards should not overlook the impact that the structure of their sales contracts and contracting process can have on their accounting department. I recommend that they scrutinize their contracts and contracting processes to determine how their design can be used to minimize finance department workloads under the new standards. They should examine how well their contracting processes are integrated with invoicing and billing and any other elements of their order-to-cash cycle, especially as these relate to revenue recognition.
Robert Kugel – SVP Research