In one corner are exasperated information technology professionals, who are perplexed by continued devotion to the beloved application and insist it be eliminated from human and computer memories. From CIO magazine’s primer “How to Say Goodbye to Spreadsheets” in 2007 to the hundreds of articles, blogs, and new technology sales pitches since, Excel isn’t only “ancient,” it’s downright dangerous in its susceptibility to errors and manipulation.

In the opposing corner are the masses of accounting and finance professionals, whose backlash to The Wall Street Journal’s 2017 column “Stop Using Excel, Finance Chiefs Tell Staff” was so fervent and incensed that author Tatyana Shumsky wrote a second article, “Finance Pros Say You’ll Have to Pry Excel Out of Their Cold, Dead Hands,” just to describe it. Why, in the face of an ever-growing chorus of naysayers, do accounting and finance professionals continue to passionately defend Excel?

We suggest the answer may lie in a bias toward the status quo. Spurred by the firestorm created by The Wall Street Journal, we sought to understand prevailing attitudes about the replacement of Excel with newer, more advanced data analytics applications. With this knowledge, organizations can craft strategies and develop interventions for reducing resistance.

Supported by the IMA® (Institute of Management Accountants) Research Foundation, we conducted a survey of 92 accounting and finance professionals. Our study identifies their perceptions of the value of new data analytics technology, the costs and benefits of a switch, and how these factors influence resistance to change. Unfortunately, the perceived costs to switch, or expand from sole use of the familiar Excel tool, overwhelm the benefits. (The full study is published in the Winter 2020 issue of Management Accounting Quarterly.)

A positive takeaway of our research is that accounting and finance professionals are fully aware of the benefits and overall value of data analytics software over Excel. We recommend organizations focus on reducing the costs of switching rather than demonstrating the benefits and value of the more advanced technology. The biggest roadblock to address? Too much work, too little time.


In this article, we propose that a bias toward the status quo use of Excel has hampered widespread adoption of more advanced data analytics technology tools. We conducted a survey of accounting and finance professionals and found that while users recognize the benefits and value of newer tools, the perceived costs of switching create user resistance.

We recommend that organizations focus on reducing the costs of switching rather than demonstrating the benefits and value of the more advanced technology.

The complete study is presented in our article, “Will We Ever Give Up Our Beloved Excel?” found in the Winter 2020 edition of Management Accounting Quarterly. We refer interested readers there to find a more detailed discussion of our motivation, status quo bias theory, and the statistical procedures performed.


Excel has earned its place in the work of financial professionals for good reason. For those of us who remember the dark days of Lotus 1-2-3, or dare we say it, green column paper and a 10-key calculator, Excel was a revelation of speed and efficiency. To its credit, Excel has evolved to address the complexities of today’s world, including a number of analytics capabilities. Pivot tables, Solver, and the Analysis Toolpack offer a variety of functions for working with data, and the roughly one-million-row capacity is sufficient for many data sets. Excel offered a blank canvas of sorts, a seemingly endless sea of rows and columns, formats and functions, to be bent and shaped to the will of the artist.

Unfortunately, this blank canvas design is also one of Excel’s main weaknesses. Because it requires significant manual entry, it allows ample opportunity for errors and mistakes. Even when a workbook is free of problems upon initial construction, any subsequent entries, format changes, column additions, and so on present challenges for security and accuracy. This becomes even more dangerous when workbooks are shared.

Excel was originally intended for individual users, not the collaborative, mobile work environment of today. Sharing workbooks too often results in multiple versions at differing stages of currency. Finally, despite offering more than one million rows of capacity, the actual usable space is much lower. Performance, response, and functionality suffer significantly as data sets grow. With volume representing one of the foundational building blocks of Big Data, this alone disqualifies Excel from holding a reliable seat at the analytics table.

The good news is that a plethora of more advanced analytics tools stand ready to assume the throne. From plug-and-play, point-and-click options to highly sophisticated, heavy coding languages, thousands of choices are available for financial professionals to get their analytics game on. Yet change hasn’t happened.


Information technology studies find that people resist change for a number of reasons. Of particular interest to us is status quo bias, first documented by famed economists William Samuelson and Richard Zeckhauser. Simply put, this is a bias toward doing nothing, therefore maintaining one’s current position. Individuals exhibiting a status quo bias conduct a cost-benefit analysis, assess the perceived value of the new alternative, and, as a result, resist change. Costs include learning time and effort, transition, and sunk costs. Benefits include increased productivity and decreased errors. This analysis may be formal or informal, quantitative or qualitative.

In our study, we asked financial professionals to respond to a series of questions to measure their perceptions of the costs, benefits, value, and resistance to change from Excel to a new, more advanced tool. We then analyzed their responses to assess the strength of this model. The results of our study suggest financial professionals recognize the benefits of switching and perceive the positive value of newer, more advanced data analytics tools. This perceived value isn’t significantly decreased by switching costs. The net positive of the cost-benefit analysis increases the perceived value of the tool and decreases professionals’ resistance to the change. But switching costs directly influence resistance to change, indicating a preference to maintain the status quo.

This is both good and bad news. The good news is that professionals recognize that more advanced data analytics tools have value. The bad news is that despite this, they still resist the change. In sum, we don’t need to sell them on the value of the analytics tool, nor do we need to continue sounding dire warnings about the growing threat to the profession. Rather, time is better spent addressing the switching costs that bias us toward maintaining the status quo.

The most common complaint we heard from study participants was the time needed to learn a new tool. Representative of this feeling, one professional stated, “Need more opportunity to learn, too much work as it is.” Another added, “I know I need to use analytics but I have zero time for this, trying to run a business.” These comments perfectly capture status quo bias: When faced with constraints, the only reasonable choice we see is to stick with what we’re doing. In an even more straightforward recognition of a resistance to change, one respondent said, “I never thought I would become the fossil that complains about changes, but I would probably hate switching from Excel.”


Perhaps most revealing, one professional told us, “Bosses keep pushing DA [data analytics] but don’t give me the resources (time) to learn it.” “Too much work, too little time” is nothing new for financial professionals. The responses in our study make it quite obvious that professionals know what they should do. They simply don’t believe they have the time and support to do it. If we truly want the financial profession to move beyond Excel, beyond the 20th Century, we must be willing to invest the time and effort. Establishing a fundamental commitment to supporting change is the first—and most fundamental—factor to consider. (See “Lay the Groundwork!”)

You should also consider the reasons why your organization wants to reduce dependence on Excel and embrace a more advanced tool. Several recent articles in Strategic Finance (see “Old Technology as a Strategic Advantage,” September 2019, or “Accountants and Tech: A Game Changer?” March 2017, ) warn against rushing into new technologies without consideration of organizational objectives and strategies. Explicitly identify your goals and the most appropriate tools to meet those goals. This is important not only to improve the likelihood of success, but also to reduce status quo bias.

Samuelson and Zeckhauser found status quo bias is stronger when the number of options grows. Also referred to as the paradox of choice, our ability to make a decision is compromised, and we become almost paralyzed when faced with too many alternatives. It’s critical that only the most suitable tools are introduced to employees. If the strategy is disjointed or if too many tools are explored, we may become overwhelmed and find it easier to stick with the status quo.


To paraphrase a recent TV commercial, quitting a habit cold turkey (meaning a complete and abrupt cessation) is hard; try quitting what we call “slow turkey” instead. Fortunately, breaking the Excel habit slow turkey is easier than you might think. In seeming recognition of Excel’s iron grip on our profession’s collective psyche, several analytics tools not only easily import to and from Excel, but also evoke its look and feel. Microsoft’s entry into the data analytics market, Power BI, represents the most direct and approachable extension of Excel. The simple fact that the application is a Microsoft product creates an aura of accessibility. It’s familiar, and users know Microsoft—what to expect and how it works.

Power BI offers the opportunity to bridge the gap from Excel dependence to an analytics mind-set. When one of the authors of this article advertised a data analytics training session as “a hands-on learning opportunity featuring Power BI, an emerging data analytics application,” three individuals registered for the session. She rebranded the communication to describe Power BI as “an extension of Excel with much the same functionality” and immediately saw a jump to 25 participants. We have spoken with other data analytics educators who noted the same thing: By calling Power BI “Excel on steroids,” we then find professionals are much more open to the change.

These are anecdotal experiences, but they aren’t isolated. The CIO article on saying goodbye to spreadsheets recounts how one company’s decision to replace Excel with Oracle’s Hyperion Planning was spurred by the new application’s familiar, Excel-like spreadsheet user interface. Another company noted that its transition to iDashboards was eased by the ability to directly copy queries and other programming out of existing Excel spreadsheets into the new application.

Utilizing an Excel-based or Excel-like application is one of the least disruptive methods for phasing into a more advanced analytics mind-set. A number of tools can be explored, depending on your organization’s size and strategy. Examples of Excel-based products include Vena Solutions, Airtable, Spreadsheet Server, Atlas for Microsoft Dynamics, and BI360. These products don’t need to replace Excel, but rather allow for integration of data stored in Excel. The analytics tools are used for cleansing, manipulation, collaboration, analysis, and reporting—areas where Excel is prone to errors, lack of currency, and other limitations. (See “Data Analytics Tools Beyond Excel," below.)

Easing Excel devotees into new software helps to reduce the perceived switching costs, namely the time constraints, frustration, and anxiety we face when asked to undertake a new way of thinking or being. Consider New Year’s resolutions. Experts recommend breaking down your goals into small, doable steps that add up to the larger goal. Don’t resolve to eat better or exercise more or spend less. Rather, set a goal to replace your afternoon bag of chips with an apple, take a 10-minute walk after dinner, or make your morning latte at home.

This slow turkey, specific, and practical approach has been conclusively shown by researchers to be more successful than placing someone at the bottom of a mountain and shouting “Climb!” Once you become aware of the pull of the status quo, you might identify other areas where it influences decisions and apply these strategies to combat it. (See “Status Quo Bias: It Isn’t Just Tech,” below.) We suggest the same is true of adopting new analytics tools.

While the debate rages on between the Excel lovers and haters, the simple truth is that the increasing value of data, and the sophistication of technology to analyze it, waits for no one. Fortunately, you can pull users into an analytics mind-set without driving them into outright revolt. We need not rip Excel from anyone’s hands, but rather layer it with a tool such as Power BI. Forget about the New Year’s resolutions; you can have your cake and eat it, too.


It’s important to remember that Excel doesn’t have to be replaced overnight and can still function as a data storage application to import data into analytics tools. Here are a few of the most popular options to explore*:

Alteryx: A data preparation tool that focuses on the first and most time-consuming tasks in data analysis: to find, cleanse, and prep data, especially from multiple sources and in different formats. The tool supports a workflow beyond data extraction and loading, as well as doing analysis using a simple, visual drag-and-drop interface. CON: Many analytics tools already include data collection and transformation within the analysis tool, so it’s one more tool to acquire and learn.

iDashboards: Connects to nearly all data sources and offers template and customized dashboards through drag-and-drop design. It offers an extract-transform-load (ETL) tool with the ability to prepare data. Pricing is subscription-based and quoted. CON: Data limitations for size and structure. Some visualization components aren’t independent, which restricts customization of output (e.g., font style, size, and color).

Microsoft Power BI: A former Excel Add-In tool that’s now a standalone for interactive visualizations and business intelligence capabilities. With a familiar look and feel, it’s affordable with free or low-cost options to include with your current Microsoft license for more advanced features. Visualizations are interactive, using drag-and-drop functionality. Data sources import directly from Excel as well as Azure, Google Analytics, and other SQL Server databases.

CON: Difficulty processing very large data volumes, even when using the paid version. Similar to Excel, some formulas require more complex language and can be fairly rigid.

Sisense: Touts itself as a complete, end-to-end, out-of-the-box business intelligence platform. Reviews note the robust data integration and ETL capabilities, interactive dashboards and visualization tools, and capacity for processing very large data sets. Pricing is subscription-based and quoted per organization. CON: Difficult to share PDF reports outside of the system dashboard. May require technical expertise in SQL coding to set up.

Tableau: Stands out for visualization and dashboard creation, with virtually no programming required. This tool is intuitive, easy to use, and imports from a variety of data sources.

CON: Can be expensive, especially for small- to medium-size companies, and while it shines at visualization, it isn’t optimal for performing more sophisticated analysis.

Vena Solutions: A solution that uses Excel and specializes in financial planning and analysis. This tool enables more efficient and effective budgeting, reporting, financial close, and compliance by integrating existing Excel workbooks. It doesn’t replace Excel but instead uses Excel to address financial planning and analysis. Pricing is subscription-based and quoted. CON: Requires expert-level Excel skills.

* We do not endorse any individual product.


Status quo bias isn’t exclusive to technology. The psychological factors that lead us to inaction apply to a wide variety of situations. Cognitive effort at some level is required for nearly all situations, and the more difficult the decision, the more likely the bias appears. Here are a few examples where you might be affected:

Accounting information systems: Status quo bias likely will play a role in any attempts to move to blockchain and distributed ledger technology. The complexity and revolutionary nature of these tools create ideal conditions for inertia and decision avoidance.

Auditing: SALY (same as last year) is the epitome of status quo bias. Doing what we did in the past because it worked then can lead us to audit inefficiency at best and audit failure at worst.

Consumer preferences: Consumers are just as likely to be biased toward the status quo as the rest of us. While this can be a benefit to your company if it results in their continued patronage, it can be a detriment if it means they won’t accept any changes.

Insurance: Do you autopay your insurance policies? Studies have shown that individuals don’t shop for more optimal healthcare, home, or auto insurance plans even when the costs of switching are negligible.

Investments and retirement: How recently have you reviewed your 401(k) choices? Despite our individual risk preferences, we tend to leave our money in the default or initial allocations. We also tend to hold stocks too long, ignoring evidence that clearly indicates a mismatch between the individual’s risk preferences and the fundamental characteristics of the investment.

Partnerships: Relationships hold a number of benefits, but choosing to stick with a long-term vendor or customer simply because of time or tenure costs your business in a number of ways. If you’re reading this article, you understand the power of the status quo. Your partners do as well and take advantage of your tendency to stick with the status quo. They may push fees or other costs without increasing any benefits to you. But the power of competition with an innovative or disruptive entrant can bring new life, efficiencies, and opportunities for growth.

Strategy: CEOs and managers have been found to hold on to underperforming segments because the decision to take action is cognitively difficult. Decision makers may then display confirmation bias, searching for evidence to justify doing nothing.

Subscriptions: Another example of autopay’s contribution to status quo bias can be found in your smartphone. Nearly all apps you purchase require autorenewal, and finding the menu to cancel a subscription can seem like a wild-goose chase. This isn’t an accident. Developers know that any impediment on the road to changing the status quo is likely to result in the user giving up.

Some status quo bias choices may be optimal. For example, retirement planners strongly argue that a buy-and-hold strategy for investments is the best path toward long-term financial stability. The key is to determine when the status quo is the optimal decision on its merits, not on its ease.


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