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Ventana Research coined the term “enterprise spreadsheet” in 2004 to describe a variety of software applications that add a desktop spreadsheet’s user interface (usually that of Microsoft Excel) to components that address the issues that arise when desktop spreadsheets are used in repetitive, collaborative enterprise processes. Enterprise spreadsheets are designed to provide the best of both worlds in that they offer the ease of use and flexibility of desktop spreadsheets while overcoming their defects – chiefly inability to maintain data integrity, lack of referential integrity and dimensionality, absence of workflow and process controls, limited security and access controls as well as poor auditability. All of these issues can cause serious problems for business use, which I’ll discuss below.
Companies should investigate enterprise spreadsheet applications that can address desktop spreadsheet issues because they can provide better results, save an organization substantial amounts of time and provide greater accuracy and security. Most products are designed to be a cost-effective replacement for the desktop variety. Enterprise spreadsheets fill various roles to suit specific business needs. Some take the form of a simple data collection program that maps the spreadsheet’s two-dimensional grid to a relational or multidimensional data store. Others offer business intelligence software capabilities that enable self-service automated reporting from enterprise data sources. Still others may be relatively elaborate applications that incorporate programmed workflows, access controls, audit trails and more sophisticated visualization methods than are available in Excel and utilize a relational or multidimensional data store. Dozens of applications that incorporate enterprise spreadsheets are available today.
Enterprise spreadsheets fill an important role in corporate computing environments. For example, the spreadsheet interface has become common in business planning applications because companies find it easier to train people to use this software when it has a familiar look and feel. It might serve as an alternative user interface to an ERP system for specific tasks, where the object is to simplify and shorten data input or facilitate some analytical interaction (such as comparing two columns of numbers to spot matches or data inconsistencies). An enterprise spreadsheet also may serve as an automated conduit for moving data from one enterprise system to another, using macros to automate actions on the data as it moves between systems. Using macros makes it easier for business people, not IT specialists, to program these actions because far more people understand how to use them than are able to program applications or data connectors. These spreadsheets may serve as a data on-ramp or off-ramp to some process handled in an enterprise system such as ERP or CRM that requires human intervention. In this role, an enterprise spreadsheet provides the programmed workflows, security and referential integrity to fill a process gap that an enterprise system cannot address or is too expensive to implement and maintain in that core system. In addition to collecting data, enterprise spreadsheets may enrich data from an enterprise source in a controlled environment, federate data from multiple systems, perform checks or reconciliations before data enters an enterprise system or perform some analysis for decision support.
I regard the electronic spreadsheet as among the top five most important advances in business management to come along in the last 100 years. It revolutionized almost all aspects of running an organization. It was the original “killer app” that made it necessary for people to go out and buy a personal computer. Yet it has inherent technological defects when used in repetitive, collaborative enterprise processes. One is a lack of data integrity, which maintains the accuracy and consistency of data – spreadsheets are notoriously error-prone. More than one-third (35%) of participants in our benchmark research on spreadsheets said that data errors are common in the most important spreadsheet they use in their job, and another 26 percent said errors in formulas are common.
A related drawback is that desktop spreadsheets lack referential integrity; that is, the meaning and context of an individual cell is defined by row and column headers rather than being defined within the individual cell. This creates the familiar problem when a group of spreadsheets supposedly containing the same data are combined, but someone has added or deleted a row or column: The result is inaccurate. In our spreadsheet research more than half (56%) of spreadsheet users – even those who have been using them for more than a decade – said that they find it usually or always time-consuming to combine data from multiple spreadsheets. Another problem is that desktop spreadsheets, being two-dimensional grids, have limited ability to manipulate and report data having three or more dimensions. While accountants can work around this limit, it’s a problem for most business users because businesses work in multiple dimensions such as organizational structures (regions or divisions, for example), products (from families down to individual stock keeping units), customers (national accounts down to drop-ship locations), dimensionality and time.
They also lack programmed workflow: People attach spreadsheets to email messages, making it difficult to keep track of the latest versions. More than one-fourth (28%) of research participants said that processes that run on desktop spreadsheets frequently break down because people using them don’t know what to do next or forget to pass them along. Likewise, they lack process controls to ensure that they are reviewed properly and that any deficiencies found in the spreadsheet are noted and automatically returned to the preparer for correction. Finally, desktop spreadsheets have limited security, access controls and audit functions as well as poor auditability. To address these deficiencies, people – especially those in finance organizations – have to spend a great deal of time reviewing and correcting spreadsheets. So while our Office of Finance research finds that the accuracy of information gleaned in desktop spreadsheet processes is acceptable, fewer than one-third of organizations said that the information the finance department provides is timely.
Because enterprise spreadsheets address these issues, they can reduce the incidence of errors and malfeasance that make using spreadsheets in repetitive, collaborative enterprise processes problematic. In many cases, enterprise spreadsheets support what we call “continuous accounting” in that they ensure data quality from end to end in financial processes. Of course, not every enterprise spreadsheet product addresses all the issues I’ve mentioned. But often this doesn’t matter if, for example, the offering is designed to perform a limited set of functions such as reporting or acting as a data conduit connecting two systems in an otherwise controlled environment.
Businesses should recognize that they no longer have to put up with the shortcomings of desktop spreadsheets. They have options to have the best of both worlds, allowing people to continue working in a familiar environment but without the drawbacks that spreadsheets impose when they are used improperly. There is no good reason not to consider adopting such an application. After all, people routinely spend time exploring application options for their smartphones but rarely spend any time getting to know business software that can increase their productivity. There are many forms of enterprise spreadsheet applications to solve a range of business issues. I recommend that businesses investigate options that give users the ease, convenience and familiarity of spreadsheets without the hassle and risk that often goes with them.
Senior Vice President Research
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Today’s proponents of artificial intelligence (AI) tend to focus on its spectacular uses such as self-driving cars and uplifting ones such as medical treatment. AI also has the potential to aid humanity in more modest ways such as eliminating the need for individuals to do tedious repetitive work in white-collar areas. Along these lines, at its recent Vision users conference, IBM displayed an application of its Watson cognitive computing technology designed to automate important aspects of regulatory and legal compliance. Should it prove workable, the application of cognitive computing to compliance could be the first step in achieving what various “Paperwork Reduction Act” legislation has failed to do: substantially cutting the time needed to comply with rules imposed by government entities.
Regulatory compliance requires plenty of effort, especially in heavily regulated industries and especially during periods of rapid change in rules. Regulatory burdens on business in the United States have been increasing and growing more complex. For example, the number of pages added to the U.S. Federal Register, a rough measure of rule-making, grew 38 percent, from 529,223 pages in the 1980s to 730,176 in the 2000s, and that growth is on pace to reach 800,000 for the decade ending in 2019. Not all of these additions apply to a specific company’s business, and not all changes are relevant. But poring through pages of laws, rules and judicial rulings to identify relevant new requirements or changes to existing ones requires expertise and often considerable effort. Determining how to address regulatory changes and ensuring that these requirements are being met also entails knowledge and experience and consumes time. While necessary virtually none of all this work adds to the bottom line (except to the extent that it avoids fines or penalties) or improves a company’s competitiveness.
In concept, cognitive computing is well suited to help manage compliance because it has the ability to continuously scan all sources of rule-making, identify those that may be relevant to an organization, and provide suggestions on how best to comply with rules and oversee the compliance program. It can improve the effectiveness of the compliance process by reducing the risk that a company will overlook regulations that apply to it or will implement a compliance program that does not adequately address requirements. In short, by using automation, cognitive computing can increase the efficiency with which a company addresses its compliance requirements. Our benchmark research on governance, risk and compliance (GRC) finds that this is important: Companies most often focus on GRC to contain overall risk and the risk of failure to comply with regulations (77% and 74%, respectively) and much less often to cut costs (31%).
The primary steps any company faces in addressing regulatory compliance are identifying and understanding regulations that apply to it; determining how to address each of them; creating the appropriate measures and governance to achieve compliance; ensuring that the necessary documentation is created to confirm conformance; and guaranteeing that issues that arise are handled properly. Companies face challenges in doing this correctly and in a timely fashion. The process of interpreting the regulations and linking them to the appropriate controls is difficult and costly. Expertise is necessary, on the part of internal staff, external consultants or legal counsel. Historically companies have devolved responsibility for regulatory compliance to the individual business units most closely affected because it was the practical approach. However, decentralized approaches make it difficult to gauge overall compliance, and as the scope of regulation increases over time they lead to duplicate controls and increased costs of compliance.
IBM Watson is potentially a good fit for managing regulatory compliance because it pools knowledge of a topic. As in the case of medicine, the collective efforts of all companies using Watson to assist in managing regulation help all of the participants. Because their combined learning processes are cumulative, Watson can build a knowledge base fast and absorb new facts and conditions quickly. It’s to all participants’ advantage to expand the capabilities of the system cooperatively. In both disciplines, learning involves mastering a technical language and syntax and being able to link their meaning to specific recommended actions.
Watson’s approach to cognitive compliance starts by parsing the body of regulations in a fashion similar to the work it has done in consuming the scientific literature in the field of medicine. It then would identify all compliance requirements that may be relevant to a specific financial institution. The company would vet the list it produces to arrive at a list of validated compliance requirements. The cognitive compliance system would then use Watson to generate a recommended set of controls and procedures based on accepted practices (which may be rooted in anything from black-letter law to actions taken by similar companies). The user company would select those that it deems appropriate. These decisions would be made by trained individuals – for example, those with compliance responsibilities in a particular area, internal counsel or attorneys specializing in a relevant practice area. Once established, a cognitive compliance system could automate the process of monitoring regulatory actions and rule-making that is relevant to the company and flagging anything that requires review.
IBM intends to focus Watson’s cognitive compliance efforts initially on the financial services sector. In part this is because the company already has a significant presence in this market segment, but the main reason is because in the United States the complexity of the rules governing this industry has mushroomed since the financial crisis of the past decade. For example, the so-called Volcker Rule, intended to prevent banks from engaging in speculations that put government deposit insurance and the financial system at risk, was spelled out in just 165 words in the 2010 Dodd-Frank Act. However, translating that concept into practice required the collaboration of five regulatory agencies: The Federal Reserve, the Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC), the Federal Deposit Insurance Corporation (FDIC) and the Office of the Comptroller of the Currency (OCC). It took about five years for this group to assemble a 71-page rule (not written in plain English) that has an 891-page preamble. As to cost of dealing with this complexity, in 2015, the OCC estimated that the cost of complying with Dodd-Frank for the seven largest U.S. banks in 2014 was US$400 million. In another example, 13 Europe-based banks spent between $100 million and $500 million each to achieve compliance with a rule requiring them to create umbrella legal structures for their local operations and take part in the Fed’s annual stress tests. To be sure, the current regulatory conditions affecting banks is an extreme example. However, for that reason it’s an attractive potential market.
If applying cognitive computing to regulatory compliance works for financial services, there are likely to be many other industries in which the regulatory requirements are demanding enough to track and implement to make its use worthwhile. One intriguing possibility for the longer term is Watson’s potential to identify duplicate or conflicting regulations and laws and enable legislators and regulatory bodies to streamline or rationalize them. We recommend that financial services organizations and perhaps others look into this intriguing possibility.
Senior Vice President Research
Follow Me on Twitter @rdkugelVR and
Connect with me on LinkedIn.