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Host Analytics has introduced AirliftXL, a new feature of its cloud-based financial performance management (FPM) suite that enables its software to translate users’ spreadsheets into the Host Analytics format. I find it significant in three respects. First, it can substantially reduce the time and resources it takes for a company to go live in adopting the Host Analytics suite, lowering the cost of implementation and accelerating time to value. Second, it enables Host Analytics users who have the appropriate permissions to create and modify models and templates that they use in planning, budgeting, consolidation and reporting. This can enhance the value of the system by making it easier to maintain. Third, it can make it far easier to routinely collect and connect planning and analytical models used by all departments and business users as it has outlined in its planning cloud offering. Although it has limitations in its initial release, AirliftXL gives corporations a workable alternative to stand-alone spreadsheets and has the potential to substantially increase productivity and effectiveness of an organization in the full range of budgeting, planning, consolidation and reporting functions.
AirliftXL addresses a fundamental issue that diminishes the productivity of companies, especially finance departments. I’ve noted in the past that desktop spreadsheets are indispensable tools for individual tasks and ad-hoc analysis and reporting, but they are poorly suited to repetitive collaborative enterprise-wide functions such as planning, budgeting, consolidation and reporting. Spreadsheets are seductive because so many people are well trained in using them that they can translate their ideas into even complex models, do analysis and create reports. However, the productivity that spreadsheets afford in authoring is more than offset when they are used over time. Desktop spreadsheets have fundamental technological shortcomings that make them unwieldy for any repetitive, collaborative task. After more than a few people become involved and a file is used and reused, cracks begin to appear. Very quickly, a large percentage of the time spent with the file is devoted to maintaining and updating them, as our spreadsheet research has shown with up to 18.1 hours per month in maintenance that I have analyzed. Spreadsheets are notoriously error-prone. In addition to monetary losses, some of which have been spectactular, there is a drag on productivity as users try to locate the source of errors and discrepancies that routinely occur in spreadsheets and then fix those mistakes.
AirliftXL enables ordinary users to create spreadsheet models and reports in Microsoft Excel and then quickly convert these to the Host Analytics enterprise system. Those that “own” a model, analysis, report or process can control these in Host Analytics. This speeds the process of setting up Host Analytics because there’s no need for someone to “translate” the company’s current set of spreadsheet models, analyses and reports. This cuts the time (and therefore the cost) of setting up the new system.
It also means that whenever changes need to be made, those responsible can make the changes themselves. Allocations and analytical models used in planning and consolidations can become part of a company’s system almost immediately. These alterations can be effected by “exporting” the Host Analytics object to a spreadsheet, modifying it and uploading back to the system. As well, users can create new models, analytics and reports in Excel and import them into the Host Analytics system. There’s no need for resident expertise or consulting time to make such changes. AirliftXL provides an organization with the best of both worlds: first, the up-front productivity that comes from enabling the author, a subject-matter expert, to quickly translate his or her ideas into a spreadsheet and, second, the ongoing productivity that is achieved when the plan, analytic model or report is kept in a centralized, easily accessible and controlled environment. These same authors can update and expand their analytical models or reports. Host Analytics can render models back into an Excel spreadsheet and the owner – not a consultant or trained IT person – can make the necessary changes and then upload them back into the system. This is an important capability because change is a constant in businesses and these changes must be reflected in financial performance management systems.
Organizations have hundreds, sometimes thousands, of spreadsheets circulating that support a multitude of processes and users in every department and business unit. AirliftXL can help incorporate them into a controlled enterprise software environment. Information that today is kept in one part of an organization can be viewed and used by others. Budgets and integrated business plans can quickly incorporate the most up-to-date information. Complex models now held in spreadsheets can be more controllable, consistent and safely accessible to a wider group of users. Creating links across individual spreadsheets (say, sales forecasts prepared by the sales organization and a company income statement forecast prepared by Finance) is straightforward, although linking to external data sources (say from a spreadsheet to a relational data store) is a little trickier. As well, Excel’s built-in financial, statistical and logical functions are all maintained in Host Analytics.
AirliftXL has the potential to be an important differentiator for the company. IBM Cognos has had something like this in Cognos Insight but from my analysis it is not as easy to use as the Host Analytics feature. Some corporations have finance IT professionals with deep subject-matter expertise as well as IT systems skills, but even their presence does not address the root cause of the misuse of spreadsheets. Most people who understand the needs of the business lack the IT skills necessary to use their company’s systems. They default to using spreadsheets because it is more expedient than trying to transfer their knowledge to someone who understands IT systems.
While AirliftXL is an important first step in taming the spreadsheet problem, it has limitations. For one, it’s not possible at this point to create a dynamic model such as an integrated income statement, balance sheet and statement of cash flows. This is a snap in a two-dimensional spreadsheet grid but much harder when working with a relational or multidimensional database. Moreover, there’s no guarantee that the spreadsheets imported into Host Analytics will be free of formulaic errors or even if it is well constructed. Thus, companies will need to put quality control processes in place, especially if a spreadsheet can have a material impact on the accuracy of financial statements or could defeat controls for fraud. It also would be handy if some vendor would create a product that could automate the digestion of masses of spreadsheets floating around companies as described in this patent for extracting semantics from data.
Despite these reservations Host Analytics’ AirliftXL provides an important capability that can cut costs of deploying and maintaining its software and increase its value to a company. This advancement builds on top of its recent rating as a Hot Vendor in the 2013 Value Index on Financial Performance Management. I recommend that corporations looking to change or upgrade all or some of their financial performance management suite consider Host Analytics and how AirliftXL helps transition the use of spreadsheets to a dedicated application approach.
Robert Kugel – SVP Research
All the hubbub around big data and analytics has many senior finance executives wondering what the big deal is and what they should do about it. It can be especially confusing because much of what’s covered and discussed on this topic is geared toward technologists and others working outside of Finance, in areas such as sales, marketing and risk management. But finance executives need to position their organization to harness this technology to support the strategic goals of their company. To do so, they must have clarity as to what big data can do, what they want it to do, and what skills and tools they need to meet their objectives.
Big data has always been with us, just on smaller scales: The term refers to data sets so large and complex that organizations have difficulty processing them using on-hand database management systems and applications. It has become a popular buzzword because technology for handling big data has crossed a threshold, making it at the same time more capable and cost-effective. Companies now can tap into huge amounts of structured and unstructured data using advanced data processing technologies, analytics and visualization tools to achieve insights not previously available using more conventional techniques. In a recent research analysis, I covered some of the potential benefits (and potential pitfalls) of big data as it relates to company management. Increasingly, the ability to analyze large quantities of business-related data rapidly holds the promise of fundamental changes in how executives and managers run their businesses. Properly deployed, big data analytics enables a more forward-looking and agile management style, even in very large enterprises. Because it allows more flexible forms of business organization, it can give finance organizations greater scope to play a more strategic role in corporate management.
Big data and analytics are a natural combination. By itself, a mass of data is not especially useful, and there are significant challenges to teasing out insight from such large data sets. However, information technology has evolved to make assembling and working with extremely large amounts of data far more practical. As well, routines involving advanced analytics that were once the domain of people with Ph.D.s in statistics are increasingly usable by business analysts, as new analytical software packages are designed to hide the complexity of the underlying statistical work. My colleague Tony Cosentino recently summarized the progress to date in adoption of big data analytics, covering some of the existing uses (already numerous) and emerging trends.
Keep in mind that it’s not just a matter of learning how to master new software and munge data. Finance departments must sharpen their skills in determining how to best utilize big data analytics. And it’s even more important that finance executives understand how to make practical use of big data analytics: In some cases users may want to consume only the output of the analytics created by other parts of the organization (such as demand forecasts by product families), while in others the organization may want to purchase applications that use or embed big data analytics (such as continuous monitoring for ERP systems governance) or enable price and profit optimization.
Our benchmark research on operational intelligence, a technology-driven discipline that has been using big data operating across networks and systems has been using analytics for years, shows that the most common reasons for using such applications (cited by almost three in five companies) are to manage performance, detect fraud, comply with regulations and manage risk. These areas are broadly applicable for finance organizations, but I assert that as well as governance and control, initially the three main applications of big data analytics are planning, reviews and alerts. Here’s how.
Companies do a lot of planning, so it’s useful to segment the activity. One way is by time. There are three main planning time frames in which big data analytics plays a role.
- Short-term tactical planning is used, for example, to project demand for specific products or create offers that might spur incremental demand. Especially in consumer products and business-to-consumer marketing, these models are statistically and computationally challenging, as they must be continually updated and adjusted. However, this is not an area of business where Finance has taken a role.
- Long-term and strategic planning can help determine the impact of a confluence of factors on markets and costs. Decades ago, the largest companies maintained strategic planning staffs to generate long-term forecasts to inform senior executives of important market trends. Except at companies that have very long cycles with specific demand and supply requirements, those staffs have disappeared or have been substantially reduced as corporations switched to third-party sources.
- In the time horizon between short- and long-term planning there are techniques for improving the accuracy of forecasts of revenue and costs using large sets of historical data, which enable organizations to better understand the various factors that influence demand. This sort of advanced modeling using predictive analytics can be useful in improving the accuracy of corporate business planning and budgeting, which is at the core of financial planning and analysis. Predictive analytics uses techniques from statistics, modeling and data mining that weigh multiple current and historical facts and their interactions to predict outcomes. Good predictive models can identify the most important factors driving outcomes, and because of this, they often can be more accurate than simple extrapolation. For example, by examining large sets of historical data, a fast-food chain can predict with reasonable accuracy demand for certain menu items at specific locations at a given hour of a given day by taking into account factors such as the day of the week, time of the year, sales patterns over the past three weeks, advertising spend and special offers.
As useful as predictive analytics are for forecasting, they may be even more valuable when applied to reviews and alerts. Predictive analytics can provide a baseline against which to compare actuals. This, in turn, enables an organization to get an earlier warning when results diverge meaningfully from what was expected, so executives and managers can react immediately rather than in days or weeks. For example, in business-to-business relationships that involve many routine purchases (any sort of supplies, for example) a divergence from established trends could generate an alert to the sales organization. Embedded analytics in an order-entry system could highlight late or smaller-than-usual orders. These might indicate a competitive threat or some other issue that would benefit from a timely interaction with the customer. This is just one of the ways that data captured by the financial systems can be used to improve the effectiveness of other business units, enabling the department to play a more strategic role in supporting the company.
Another use is in accounts receivable, where predictive analytics can promote customer satisfaction. To illustrate, a company that does a routine analysis of payment patterns can have a good idea of when specific customers will pay. If one that routinely pays its invoice between the 16th and 19th day of the month has not paid by the 23rd day, the analytics system generates an alert. A call to the customer or an automated email notes the delayed payment, asking if there was an error in the billing or some other point in dispute. There are a couple of advantages to this approach. If nothing else, if there is an issue, it is likely to be resolved more quickly. Moreover, from a customer satisfaction perspective, it’s a far superior form of customer interaction than waiting several weeks and then sending out a dunning notice demanding payment. Resolving any issue sooner improves cash flow, and if the company did make a mistake, asking for payment will only annoy the customer. Another use of big data in receivables is to automatically identify customers that are routinely tardy in paying. This can kick off an internal company discussion about what ought to be done about the situation, such as limiting credit or finding ways to accelerate payments.
Governance is another area where big data analytics are already at work, with companies using it for fraud detection and alerting. For instance, software packages can monitor a company’s financial systems for evidence of suspicious activities such as payments to bogus vendors or top-level alterations to financial statements. Such systems are designed to be high-level controls that reduce the need for manual internal and external audit work. And even more is possible. As I noted earlier, in the not-too-distant future it may be possible to have an “auditor in a box” – a forensic system that continuously identifies and lists all suspicious activities, transactions and conditions and weighs their materiality. Such a system would permit more timely responses to the risk of material errors or fraud and facilitate examinations by external auditors. In addition to being far more efficient than periodic manual effort, the auditor-in-a-box concept is potentially more reliable because it examines everything rather than relying on sampling.
However, there are challenges. Staffing and training are significant issues for Finance in dealing with big data analytics. Our research into the challenges of utilizing big data shows that nearly four in five companies find staffing and training to be an obstacle in utilizing big data. Despite the fact that analytics is an inherent element of the finance function, it almost always involves the broad application of basic approaches employing simple math (ratio and margin analysis, for example). Few departments have applied advanced analytics: Our finance analytics research finds that only 13 percent of finance departments employ predictive analytics.
To be able to handle these staffing and training needs, finance executives must understand their department’s big data analytics competence requirements. A useful place to start is to become familiar with the five personas Tony Cosentino developed to describe the people working with business intelligence and analytics. These personas illustrate the various objectives, skills and interests that individuals bring to the discipline. Adapting his approach to big data analytics to this discussion, at the top of the list are highly skilled statisticians who do exploratory work and create purpose-built analytics and analytical models to address specific tasks. These people usually have advanced degrees in statistics and understand how to use sophisticated analytical software and data sets to their fullest. Few finance organizations need this level of capability. A second type of user includes business analysts who have in-depth knowledge of the business and finance issues, know how to access and apply available data relevant to the issue, and have the ability and commitment to master software that requires training but not an advanced degree in statistics. Depending on a company’s size, finance organizations will need a person or a group of people with this level of competence. A third type is the knowledge worker. This description includes executives, managers and directors who need to interact with – not just consume – advanced analytics. These types of users should not be expected to learn how to create or structure analytics, but they need to know how to employ analytics embedded in dashboards or applications as well as visual discovery tools, which are increasingly user-friendly. This level is where the need is broadest, so finance executives must focus most of their efforts in terms of developing these skills.
Big data analytics is an important development that will challenge finance organizations to use new capabilities to improve their effectiveness and enhance their company’s competitiveness. There are many ways organizations can begin to address the challenge. At least, CFOs and senior finance executives should create a steering committee to identify opportunities to apply big data analytics; identify gaps in skills, processes, data availability and software; and establish timelines and goals. Moreover, if CFOs are serious about exploiting the potential of big data analytics, they must communicate its importance to their department and demonstrate a commitment to a plan of action.
Robert Kugel – SVP Research