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Business computing has undergone a quiet revolution over the past two decades. As a result of having added, one-by-one, applications that automate all sorts of business processes, organizations now collect data from a wider and deeper array of sources than ever before. Advances in the tools for analyzing and reporting the data from such systems have made it possible to assess financial performance, process quality, operational status, risk and even governance and compliance in every aspect of a business. Against this background, however, our recently released benchmark research finds that finance organizations are slow to make use of the broader range of data and apply advanced analytics to it.

Analytics has long been a tool used by Finance. Yet because analytical techniques for assessing balance sheets, income statements and cash-flow statements are well developed and widely accepted, vr_NG_Finance_Analytics_01_finance_analytics_users_dissatisfiedfinance professionals have had little incentive to do more even as the opportunities available to them have proliferated. Taking a narrow of finance analytics they have largely failed to take advantage of advanced analytics to address the full needs of today’s enterprise and thus to increase their own value to it.

It’s not that finance departments aren’t aware of their shortcomings. For instance, more than half (58%) of participants in this research said that significant or major changes to their process for creating finance analytics are necessary; only 7 percent said no improvements are needed. We found four main reasons for dissatisfaction with their process: it’s too slow; it isn’t adaptable to change; there aren’t enough skilled people to do this work; and data used in it is inaccessible or too difficult to integrate.

Usually, addressing some business issue requires dealing with a combination of the underlying people, processes, information and technology. Companies often fail to address the issue successfully because they focus on just one of these elements. We think it’s important to use the people, process, information and technology framework to isolate the root causes behind the issues. Let’s look at the role of technology – mainly software – in finance analytics.

Our research finds many companies have trouble with the technology aspects. Only 12 percent of organizations are satisfied with vr_NG_Finance_Analytics_02_spreadsheets_arent_right_for_finance_analyticsthe software they use to create and apply analytics; more than twice as many (27%) are not satisfied. That’s probably because 71 percent of them use spreadsheets for analytics, a higher percentage than for any other tool. Two-thirds of these users said that reliance on spreadsheets makes it difficult to produce accurate and timely analytics. In contrast, fewer than half use innovative techniques such as predictive analytics (44%) to assist planning and forecasting, and just 20 percent are employing big data to process the flood of data into today’s businesses.

The research demonstrates a correlation between the technology a company uses and how well its finance analytics processes work. Two-thirds of participants who said their software works well or very well also said their finance analytics process needs little or no improvement. By comparison, just one in four of those that said significant changes must be made to the software they use have a process that needs little or no improvement.

Here again we find that the inappropriate use of spreadsheets is an issue. When asked whether spreadsheets cause problems in their use of analytics, 67 percent said yes. This is because desktop spreadsheets have inherent shortcomings that make them poorly suited for any sort of advanced analytics. In particular, they cannot readily manage analyses involving more than a handful of dimensions. (A dimension is some aspect of business data such as time, business divisions, product families, sales territories and currency.) Many of these dimensions are constructed in hierarchies: Branches roll up into territories which roll up into divisions of companies, for example. Analyzing data usually requires viewing the data from different perspectives (which translates into dimensions) to isolate an issue or opportunity. One such would be looking at sales by product family and region and then drilling down into specific branches or stock-keeping units (SKUs). In doing analysis, it’s difficult enough to manage the dimensions of the purely financial aspects of a business. Spreadsheets are especially ill-suited to analyzing operational and financial data together, such as the delivery method or product configuration details.

Our research data shows that not having the right technology is impedes finance departments’ ability to create and use more advanced analytics. We found several reasons why companies decide not to make these technology investments. The top three are a lack of resources, no budget and a business case that’s not strong enough. The first two may be valid reasons, but not wanting to commit resources and budget to advanced analytics could be a symptom of a poorly constructed business case, as I noted earlier. A lack of leadership and vision on the part of senior finance executives also plays a role. Many may say they want their department to play a more strategic role in running their company yet fail to follow through to adopt new methods and the necessary supporting technology.

But now a new generation of finance department leaders is emerging. These are people young enough to have grown up with technology and to be more demanding in their use of software and systems to produce results. The time is ripe for change, and it’s up them to drive finance departments to be more strategic in their use of analytics.

Regards,

Robert Kugel – SVP Research

Technology for the Office of Finance can have transformative power. Although progress has been slow at times, today’s finance organizations are fundamentally different from those of 50 years ago. For one thing, they require far fewer resources (chiefly people) to perform basic accounting, treasury and corporate finance tasks. In addition, public corporations report results sooner – sometimes weeks sooner – than they could in the mid-20th century. And finance departments are able to harness substantially more data and a wider array of analytics to promote insight and support more agile decision-making.

Even in this context, in many corporations the tax function remains a backwater in its use of technology. Most of their tax professionals are awash in desktop spreadsheets, tools that they initially thought would promote efficiency and accuracy. But the inherent problems with desktop spreadsheets make tax processes not only needlessly time-consuming (even using macros and other spreadsheet automation techniques) but also prone to errors and inconsistencies. This is especially true if people must enter the same information multiple times, which increases the chances of mistakes. In addition, assumptions made and rationales behind formulas used in data transformations may not be documented or readily accessible to others in the organization. This legacy can be problematic years later in an audit, especially if the individual who prepared the spreadsheet is no longer employed at the company. And with spreadsheet-driven processes, auditing taxes and the underlying data and calculations is difficult and time-consuming. On top of all this, the effects of the desktop spreadsheet’s inherent shortcomings multiply with the size of a corporation. By their nature, these spreadsheets hinder a corporation’s ability to understand tax issues and optimize  related decisions.

Organizations do not have to put up with these outdated, counterproductive practices. New tools can help streamline tax processes. One is Vertex Enterprise (which I reviewed earlier this year). VR_tech_award_winner_2013Vertex recently was awarded Ventana Research’s 2013 Innovation Award for the Office of Finance for this suite of application and integrated use of tax data and analytics. Vertex offers a single-platform approach to managing all types of taxes (direct and indirect) across the entire tax life cycle, from analysis through provisioning to audit defense, using a single data source. Direct (income) tax management is still a largely manual process involving a plethora of desktop spreadsheets. Calculating and accounting for direct taxes is complicated, largely because income tax laws can be quite convoluted, especially in certain industries. As well, the larger the number of tax jurisdictions it operates in and the more numerous its subsidiary legal entities, the more complex tax management becomes.

Vertex Enterprise includes tax provisioning software for both indirect and direct taxes. Indirect Tax is managed by the company’s O Series software, a single platform that handles sales-and-use-, VAT- and goods and services taxes on a global scale. Direct taxes is handled by Vertex Tax Accounting, global tax provisioning and reporting software that works with multiple general ledger systems in  multiple currencies and across multiple years within the context of multiple accounting regimes such as US- and other national generally accepted accounting standards (GAAP) and International Financial Reporting Standards (IFRS). The web-based software supports a distributed, collaborative, enterprise-wide tax decision cycle process that can achieve more optimal tax-related decision making. Direct taxes exist in a parallel universe, and one advantage in having a single system is that it facilitates the process of reconciling the business accounting performed in ERP systems with the tax accounting governed by law. Vertex automates tax account reconciliation and reporting and manages adjustments that span multiple time periods.

One notable advance for corporate tax departments offered by Vertex Enterprise is its tax data warehouse (TDW). Because a TDW can automate much of the painstaking work that occupies much of the tax practitioner’s time, it can free these trained individuals to focus on making the best tax decisions possible. Also, because a TDW can increase visibility into tax analyses and calculations, corporations can be more confident in their tax-related decisions. And with this greater visibility and confidence, tax departments can become a mainstream participant in the finance function. This is especially important in an era of increasing cooperation between tax authorities worldwide. More than ever, corporations that operate globally must be able to optimize their tax positions over time and across multiple jurisdictions. A TDW enhances that capability.

Conceptually, a TDW is simple: It’s a data store that makes all tax data readily available and can be used to plan and provision a company’s taxes. However, in larger companies (those with more than 1,000 employees) that operate in multiple tax jurisdictions and have even moderately complex legal entity structures, tax-related data structures and calculations become fiendishly complex, as I noted in an earlier perspective. For that reason, the first attempts to create TDWs proved unworkable because the sheer complexity of the direct (that is, income) tax domain overwhelmed the available information technology. These limitations forced companies to take shortcuts, which meant that each TDW had to be a largely custom effort and therefore expensive to build. And because these shortcuts rendered the systems brittle and difficult to change, they were expensive to maintain. Today, however, technology is available to make TDWs practical.

A TDW has several purposes: to ensure accuracy and consistency in tax analysis and calculations, improve visibility into tax provisioning, vr_Fast_Close_2012_ERP_01_multiple_erp_systems_are_commonand cut the time and effort required to execute tax processes. The technology is especially useful because data management is one of the biggest operational challenges facing tax departments today. That is, the information necessary for tax provisioning, planning, compliance and audit may not be readily available to the tax department because accounting and other information is kept in multiple systems from multiple vendors. Our benchmark research shows that 90 percent of companies with 1,000 or more employees use financial systems from multiple vendors, and 43 percent use four or more. In addition, not all of the data necessary for tax department purposes is captured by the ERP system. As well, data collected in a general finance department warehouse or pulled together in a financial consolidation system may not be sufficiently granular for tax department purposes.

Moreover, most companies’ ERP systems (the core technology for gathering transaction data) are not inherently “tax aware,” so tax departments repeatedly need to perform transformational steps to ensure that data is formatted and organized properly. Sometimes the data must undergo multiple transformations because, for example, the transaction information collected in an overseas subsidiary must be reported locally using the local currency and accounting standard but translated to the parent company’s tax books in a different currency using a different accounting standard. In some industries (such as financial services) there may be multiple local reporting standards, one for general statutory purposes and another reflecting specific rules for that industry demanded by some regulatory authority. In short, there are numerous data-driven headaches tax professionals have to address before they even get down to work. A TDW addresses this problem.

Ventana Research is dedicated to helping organizations enhance the effectiveness and strategic value of the Office of Finance. Tax departments, CFOs and controllers in larger, more complex corporations should examine how the tax organization spends its time and determine to what degree more enterprise automation and fewer desktop spreadsheets would enable the company to understand and manage its tax needs more intelligently. Vertex Enterprise can be a useful foundation for transforming the tax function, which is why it received our Technology Innovation Award for Office of Finance for 2013.

Regards,

Robert Kugel – SVP Research

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