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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

Ventana Research recently released the results of our Next-Generation Business Planning benchmark research. Business planning encompasses all of the forward-looking activities in which companies routinely engage. The research examined 11 of the most common types of enterprise planning: capital, demand, marketing, project, sales and operations, strategic, supply chain and workforce planning, as well as sales forecasting and corporate and IT budgeting. We also aggregated the results to draw general conclusions.

Planning is the process of creating a detailed formulation of a program of action designed to achieve objectives. People and businesses plan to determine how to succeed in achieving those objectives. Planning also serves to structure the discussion about those objectives and the resources and tactics needed to achieve them. A well-managed planning process should be structured in that it sets measurable objectives and quantifies resources required to achieve them. Budgeting is a type of planning but somewhat different in that is financially focused and is done to impose controls that prevent a company from overspending and therefore failing financially. So while planning and budgeting are similar (and budgeting involves planning), they have different aims. Unlike budgeting, planning emphasizes the things that the various parts of the business focus on, such as units sold, sales calls made, the number and types of employees required or customers served.

Integrating the various business planning activities across a company benefits the senior leadership team, as I have written by enabling them to understand both the operational vr_NGBP_02_integrated_planning_works_betterand the financial consequences of their actions. There are multiple planning efforts under way at any time in a company. These plans typically are stand-alone efforts only indirectly linked to others. To be most effective, however, an individual business unit plan requires direct inputs from other planning efforts. A decade ago I coined the term “integrated business planning” to emphasize the need to use technology to better coordinate the multiple planning efforts of the individual parts of the company. There are good reasons to do this, one of which is accuracy. Our new research reveals that to be accurate, most (77%) planning processes depend to some degree on having access to accurate and timely data from other parts of the organization. For this reason, integrating the various planning processes produces business benefits: In our research two-thirds of companies in which plans are directly linked said that their planning process works well or very well. This compares favorably to 40 percent in those that copy planning data from individual plans to an integrated plan (such as the company budget) and just 25 percent of those that have little or no connection between plans.

Technology has been a major barrier preventing companies from integrating their planning efforts. Until relatively recently, joining the individual detailed plans of various departments and functions into an overall view was difficult because the available software, data and network capabilities were not sufficient to make it feasible and attractive to take this approach. To be sure, over the past decades there has been steady progress in making enterprise systems more accessible to ordinary users. But while dedicated planning software has become easier to use, evidently it’s still not easy enough. The research reveals that across the spectrum of corporate planning activities, three-fourths of organizations use spreadsheets to manage the process. We expect this to change over the next several years as the evolution in information technologies makes dedicated planning software a more compelling choice. One factor will be enhanced ease of use, which will be evident in at least two respects. Software vendors are recognizing that a better user experience can differentiate their product in a market where features and functions are a commodity. Ease of use also will extend to analytics and reporting, making it easier for business users to harness the power of advanced analytics and providing self-service reporting, including support for mobile devices. The other factor will be the ability to make the planning process far more interactive by utilizing in-memory processing to speed calculations. When even complex planning models with large data sets can be run in seconds or less, senior executives and managers will be able to quickly assess the impact of alternative courses of action in terms of their impact on key operating metrics, not just revenue and income. Having the means to engage in a structured conversation with direct reports will help executives be more effective in implementing strategy and managing their organization.

Technology is not the only barrier to better planning. The research demonstrates the importance of management in the process, correlating how well a planning process is managed with its accuracy. The large majority (80%) of companies that manage a planning process well or very well wind up with a plan that is accurate or very accurate. By contrast, just one-fourth of companies that do an adequate job achieve that degree of accuracy and almost none (5%) of those that do it poorly have accurate or very accurate results. Additionally, managing a planning process well requires clear communications. More than three-fourths (76%) of companies in which strategy and objectives related to plans are communicated very well have a process that works very well, while more than half (53%) with poor executive communication wind up with a planning process that performs poorly. And collaboration is essential to a well-functioning planning process. Most (85%) companies that collaborate effectively or very effectively said that their planning process is managed well, while just 11 percent of companies that collaborate only somewhat effectively expressed that opinion.

vr_ngbp_03_collaboration_is_important_for_planningCollaboration is essential because the process of planning in corporations ought to get everyone onto the same page to ensure that activities are coordinated. Companies have multiple objectives for their planning processes. Chief among these is accuracy. But since things don’t always go to plan, companies need to have agility in responding to changes in a timely and coordinated fashion. In a small business, planning can be informal because of the ease of communications between all members and the ease with which plans can be modified in response to changing conditions In larger organizations the planning process becomes increasingly difficult because communications become compartmentalized locally and diffused across the entire enterprise. Setting and to a greater degree changing the company’s course requires coordination to ensure that the actions of one part of the organization complement (or at least don’t impede) the actions of others. Coordination enables understanding of the impact of policies and actions in one part of the company on the rest. Yet only 14 percent of companies are able to accurately measure that impact, and fewer than half (47%) have even a general idea. Integrated business planning address that issue.

In most organizations budgeting and operational planning efforts are only loosely connected. In contrast, next-generation business planning closely integrates unit-level operational plans with financial planning. At the corporate level, it shifts the emphasis from financial budgeting to planning and to performance reviews that integrate operational and financial measures. It uses available information technology to help companies plan faster with less effort while achieving greater accuracy and agility.

For companies to improve competitiveness, their business planning must acquire four characteristics. First, planning must focus on performance, measuring results against both business and financial objectives. Second, it must help executives and managers quickly and intelligently assess all relevant contingencies and trade-offs to support their decisions. Third, it must enable each individual business planning group to work in one central system; this simplifies the integration of their plans into a single view of the company and makes it easy for planners in one part of the business to see what others are projecting. Fourth, it must be efficient in its use of people’s time. Success in business stems more from doing than planning. Efficient use of time enables agility, especially in larger organizations.

Today’s business planning doesn’t completely lack these features, but in practice it falls short – often considerably. Senior executives ought to demand more from the considerable amount of time their organization devotes to creating, reviewing and revising plans. They should have easy access to the full range of plans in their company. They must be able to engage in a structured dialog with direct reports about business plans, contingency plans and business unit performance. Information technology alone will not improve the effectiveness of business planning, but it can facilitate their efforts to realize more value from their planning.

Regards,

Robert Kugel – SVP Research

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