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An SME Approach to Data Analytics

By Jennifer Riley, Ph.D., CPA, CIA; Pamela J. Schmidt, Ph.D.; and Kimberly Swanson Church, Ph.D.
May 1, 2022

Many small and medium-sized businesses have yet to comprehensively employ data analytics within their organizations, but they can begin with small steps by adapting what’s already in place.

 

When approaching data analytics, the theory goes you can either dive headfirst into the data revolution or fall into the dustheap of history. Accountants and small businesses are two groups most often assumed to be in the latter category. After all, we’re told technology can do everything an accountant can but better, cheaper, and more reliably. A consistent mantra is that the accounting profession is antiquated and slow-moving. Similarly, small to medium-sized businesses (SMEs) are sometimes portrayed as “slow followers,” failing to take advantage of innovative technology. There seems to be a constant drumbeat of ever-increasing volume, and the only way to stay in the race is to master the growing list of software, apps, web scrapers, text analyzers, visualization suites, and so on. If accountants won’t do it, that’s okay because data scientists will. If SMEs can’t do it, don’t worry because the big companies already have. Right?

 

Curious about the state of SMEs’ approach to data analytics, we asked representatives from SMEs about their use of analytics tools and their associated personnel needs (see Table 1 for more information about the respondents). Their responses confirm that, despite awareness of the importance of data analytics and of the variety of tools available, many remain at the starting blocks of the analytics race. When SMEs do undertake common analytics tasks, Excel spreadsheets remain the preferred choice, the one-tool-fits-all-tasks tool.

 

Excel is a hard habit to break for companies of all sizes. We propose, however, that this doesn’t mean SMEs are destined for the scrap heap, nor are they doomed for obsolescence. Rather, we detect many positive indicators that SMEs have the capability to build on their advanced Excel expertise as a pathway into the data analysis revolution. As one would expect, the SME path differs from that of large companies.

 

DATA ANALYTICS FOR SMEs

 

Our research study was supported by an IMA® (Institute of Management Accountants) Research Foundation Incubator Grant, and the results of the full study are forthcoming in the Journal of Emerging Technologies (“Has Excel Become a ‘Golden Hammer’: The Paradox of Data Analytics in SME Clusters”). The survey was administered to highly experienced, professional representatives of SMEs attending several accounting conferences. We asked four primary research questions: which data analysis tasks are being performed and how, the reasons for any data analysis tasks not being performed, the approaches for developing data analysis expertise, and the skills sought when hiring entry-level accountants.

 

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The first major takeaway from their responses is that Excel is the primary, and often the sole, tool utilized by the majority of SMEs for common analytics tasks. Specifically, SMEs reported using Excel to perform, for example, data formatting (79%), data cleansing (55%), trend analysis (72%), and visualization (76%). This is reflected in the consistent demand for Excel skills, as cited by the SME respondents.

 

A second result from the full study is that if the SME doesn’t use Excel, more often than not the task simply isn’t performed. Some of these tasks, such as the use of adaptive technology learning tools (69%), aren’t surprising. But it’s concerning to learn that 55% don’t collect website, social media, or search engine data, and almost half (48%) don’t mine data for patterns, correlations, or anomalies. Nearly one-third (31%) of respondents indicate they don’t integrate nonfinancial data with their financial data, and about one-fifth (21%) don’t perform a fundamental task like financial statement industry comparisons. On the flip side, finding that some SMEs do use Excel to perform these very same tasks demonstrates the potential for other SMEs to follow suit even while continuing their reliance on Excel.

 

WHAT’S INVOLVED IN DATA ANALYSIS?

 

Data analysis offers a wealth of options for improving business decisions of all kinds, strategy, production, operations, and marketing. Unfortunately, it’s the very companies that are slow to adopt technology that may benefit the most. It requires an understanding of analytical methods and the adoption of new data analysis technology skills, and the sooner the better. The prevailing trend of digital transformation has revealed the types of companies most susceptible to disruption are those clinging to outdated business practices and lagging in technology adoption. If SMEs fall into this category, their resource disadvantage could make it difficult if not impossible to catch up.

 

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Yet there’s much to be optimistic about, given the agility of an SME. The clear progression from Excel use to embracing Excel’s enhancements to adopting its close cousin Power BI is a course the average SME can chart. The proposal here isn’t that Excel should be discarded by accountants. This leading spreadsheet application continues to offer flexibility and has been enhanced with many analytics capabilities sufficient for a number of more advanced business analysis objectives. But SMEs and accountants can build on their existing strengths with Excel, expand their current tool kit to handle larger data sets, utilize new kinds of data, and embrace a few targeted analytics methods to produce more valuable insights in core business processes.

 

IMPLEMENTATION TRENDS

 

Our survey shows SMEs are looking toward the future. Figure 1 presents the respondents’ planned future data analysis activities. Forty-three percent of SMEs report being somewhat likely to expand their use of Excel, while 30% of respondents are somewhat likely to increase their use of Excel add-ins. From a positive view, this represents a significant opportunity for many SMEs to take advantage of their currently available tech tools. One-third of SMEs are at least somewhat likely to expand their data analysis technology tools (33%) and data volume (40%). It’s problematic that the majority of SMEs are neutral, somewhat unlikely, or highly unlikely to expand tools, volume, or data types. One of the greatest advantages of data analysis is the ability to capitalize on unstructured data (much of which is public and free), which many larger companies have already captured.

 

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Figure 2 shows more advanced data analysis technology the SME respondents expect their organization to utilize in the next three to five years. Cloud computing is the overwhelming winner, cited by 67% of respondents. Cloud computing is a data analysis enabler because it allows SMEs access to advanced applications and data sources at a low cost. The next technology rankings are Internet of Things (33%), automation (27%), machine learning (17%), financial technologies (17%), and AI (14%).

 

While it’s encouraging that SMEs in our survey are somewhat likely to engage in future analytics technology activities and tool use, the percentages reported remain disturbingly low. Further, anyone who has experienced any kind of technology change at an organization knows the best-laid plans often go awry. These are reasons why expanding the use of Excel analytics tools may be an optimal strategic path for an SME. Exploiting Excel’s capabilities in concert with the characteristics of an analytics mindset is a path for SMEs to engage in meaningful data analysis.

 

“Skills SMEs Require for a New Hire” shows that SMEs are looking for characteristics including the ability to identify and define problems, a problem-solving orientation, and a commitment to being an independent learner. These analytics mindset skills can be combined with technology skills (Excel), statistical tools, and communication skills to springboard an SME into data analysis activities. For example, Excel has long offered technologies for automation, leaving no barrier for the more than 70% of respondents that fail to cite automation as a planned activity. If SMEs take initial steps into automation using Excel macros or VBA (Visual Basic for Applications) programming tools, it may enhance acceptance of future data analysis methods.

 

The continued dependence of businesses of all sizes on Excel originates in a resistance to adopt new technologies, and SMEs fall firmly into this camp. We provide a brief summary of Excel’s data analysis capabilities in “Excel Already Supports Analytics.”

 

THE FUTURE OF DATA ANALYSIS IN SMEs

 

The best news is that SME attributes can promote a rapid advancement into data analysis innovation. Our survey results offer insights specifically applicable to SMEs. The following SME attributes are a strong basis for data analysis innovation:

 

Entrepreneurial environment. Many SMEs operate entrepreneurially and so are uniquely positioned for agility. By necessity, SME employees are already “wearing many hats,” working across functional lines, and operating within a fluid hierarchy. Free of the confines of a large, centralized bureaucracy, SMEs can exploit their size for the dynamic response required in the data analysis race.

 

Small is agile. SMEs are inherently structured for rapidly infusing analytics across the organization. A 2019 Deloitte survey found companies with a culture of broad responsibility for analytics across all employees overwhelmingly report exceeding business goals (88%). By their smaller size alone, SMEs can more easily diffuse new responsibilities among employees and across all functional areas.

 

Culture and data governance. Technology innovations usually require culture change, exacerbated by data analysis’s demands for data governance as a company-wide initiative. With little separation between layers of an SME organization, the necessary data-oriented culture change established by top management (in some cases, the owners) can effectively be instilled throughout the organization.

 

Data accessibility. Accessibility to useful, curated data is critical to glean insights from analysis. According to Bernard Marr, technology futurist and international best-selling author, data democratization means everyone in a company has access to data, without bottlenecks or gatekeepers, without silos or fiefdoms. Cloud storage facilitates democratic data access, which even the smallest businesses can implement inexpensively.

 

Harness unstructured data. The Deloitte survey revealed only 18% of respondents are using unstructured data of any kind. By 2025, unstructured data is expected to compose as much as 80% of actionable data for decision making. Unstructured data is vast and pervasive, including text on web writings, social media posts, images, video, audio, satellite image files, geographic locations, and so forth. Traditional Excel spreadsheet skills aren’t sufficient for effectively using this unstructured data. Unstructured data enhances the ability to reach customers by predicting their needs and wants and then influencing their behavior. Harnessing unstructured data offers enormous opportunity for SMEs, enabling insight into personalized opportunities, habits, and patterns.

 

Expanded skills. Innovating with data analysis necessitates acquiring skills through targeted hiring or personnel training. Following an evolutionary path into data analysis, SMEs can capitalize on their personnel’s Excel expertise to leverage the data analysis capabilities already present.

 

CAUTIONS FOR DECISION MAKERS

 

The SME’s goal shouldn’t be to purchase technology for its own sake. We’ve reported on the state of data analytics in SMEs, and our results support previous articles that argue that rushing blindly into data analysis isn’t the answer. Being attracted by the regularly hyped shiny data analysis applications doesn’t guarantee effective data analysis utilization to advance SMEs’ business objectives. SMEs should be cautious in particular about ethical data management with matters of data privacy, liability, and security concerns.

 

 

Another problem lies with the risk of faulty outcomes from ineffective or erroneous use of data and analytics. A famous scene from the television sitcom The Office shows lead character Michael Scott driving into a lake because his GPS told him to turn. While this is a humorous example, it isn’t far from the reality that advanced analytics and technologies may lead down the wrong road, literally and figuratively.

 

Similarly, as we reported in the full study, outsourcing data analysis is a method only a small number of SMEs in our survey use for their analytics needs. SMEs can ill afford to cede their responsibility for data governance, or cede control of their opportunity to gain competitive advantage, to an external contractor or some unfamiliar technology. The tendency to blame the computer and wash one’s hands of accountability is tempting. Trusting data analytics security to a third party is not only risky, but also irresponsible. When considering the use of more analytics tools or technology, SMEs must address security issues as an integral part of the analysis.

 

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Some SMEs may write off these concerns because they don’t believe they need to or can use technology like AI and machine learning, viewing it as something too advanced for their current analytics needs. But they’re being targeted by more and more advanced solutions, such as Salesforce’s Einstein AI and Xero Mobile accounting app. And while they may not realize it, if SMEs use easily accessible Google Analytics or Weka, they’re already using unstructured web scraping and machine learning classification tools. Even as we suggest SMEs use Excel’s built-in data analytics capabilities, SMEs will likely need to grapple with these issues and these new tools, as technology change waits for no one.

 

THE TIME IS NOW

 

The race of data and analytics is on, and SMEs should seize the opportunities. True, it’s difficult to miss the headlines shouting about massive analytics investments by large enterprises. Commercials from analytics providers like Microsoft, SAP, and Google imply analytics is a simple plug and play that anyone can do. The reality isn’t so simple, even for some big companies that seemingly have resource advantages. For example, Business Insider recently reported that data on roughly $12 trillion poured into the U.S. consumer credit, lending, and mortgage market is accessible only in Excel spreadsheets.

 

Whether your SME is considering diving headfirst into data analysis or staying with the status quo, begin with honest answers to some basic questions: Is your business stuck in a rut, statically using a familiar tool and its same old features, unwilling to learn new ones? Is the desire to stay in your comfort zone driving your decisions? Is adherence to the old audit adage “same as last year” keeping you clinging to what you know? If the answer is “yes,” it’s time to reconsider the risk of not making advancements. Lay out a path to analysis by investing in achievable steps, making calculated investments, and taking measured risks into data analysis. In the current business climate, the real risk is to ignore the possible benefits from using data analysis to derive actionable business insights.

 

The path to mastery, whether you’re a carpenter or knowledge worker, is to continuously seek to expand your toolbox and skill set. Small steps into analytics will position both the individual and the organization in line with the more advanced tools already on the horizon. Accounting and finance professionals working at SMEs should capitalize on their current spreadsheet strengths and extend into the analytics features already available to them.

 

 

SMEs need not accept the all-or-nothing choice of either investing heavily in advanced analytics or facing certain doom. At its most fundamental, successful data analysis stems from a mindset, not a tool. Tune out the hype, the vendors, the consultants, and the doomsayers. If Excel is the answer for your SME, then begin to utilize its data analysis features. If not, then seek from among the many innovative new options. There is no one-size-fits-all solution, and bigger isn’t always better.

 

Jennifer Riley, Ph.D., CPA, CIA, is a professor of accounting at the University of Nebraska Omaha. Jennifer is a member of IMA’s Platte Valley Chapter. She can be reached at jenriley@unomaha.edu.
Pamela J. Schmidt, Ph.D., holds the William Lyman Dibble Professorship in Accounting at Washburn University and is a member of IMA’s Kansas City Chapter. She can be reached at pamela.schmidt@washburn.edu.
Kimberly Swanson Church, Ph.D., is the director of the School of Accountancy at Missouri State University. She’s a member of IMA’s Kansas City Chapter. She can be reached at kimberlychurch@missouristate.edu.
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