Pennsylvania CPA Journal
Companies now see problems and opportunities earlier than they once did due to advances in data collection. But the speed of data distribution does not guarantee the signals will be interpreted correctly or acted upon effectively.
For years, finance leaders invested heavily in reducing the delay between business activity and managerial awareness. Those efforts were largely successful, and information now moves through organizations faster than ever. Yet, decision quality has not always improved at the same pace. This column explores why better visibility does not automatically produce better decisions and argues that the remaining constraint is a decision architecture: the assumptions, review processes, and accountability mechanisms that convert information into judgment.
Over the past decade, reporting systems have become more integrated. Dashboards that once refreshed weekly are updating daily, sometimes hourly. The assumption that better information naturally produces better decisions is deeply embedded in modern finance operations. It appears in technology roadmaps, reporting initiatives, and business cases supporting new systems. Yet experience suggests a more complicated reality.
Yes, organizations now see problems earlier than they once did. Also true is that they may see opportunities earlier. However, neither guarantees those signals will be interpreted correctly or acted upon effectively.
I have seen executive teams spend considerable time debating decisions despite having access to detailed operating data, while other organizations moved quickly with less information but clearer decision frameworks.
Velocity vs. Capacity
Organizations have reduced the delay between when an event occurs and when management becomes aware of it. That is an advantage, but decision capacity has not evolved at the same pace. Most organizations still rely on management routines developed in a much slower information environment. Capital requests move through familiar approval processes, and forecast discussions often occur on monthly or quarterly cycles, even when operating metrics change daily.
The result has become increasingly common: information reaches decision-makers faster than organizations can consistently interpret, challenge, and act on it. In some cases, the volume of available information creates its own complications. Dashboards expand. Metrics multiply. Exceptions generate alerts. Leaders become exposed to more signals than they can realistically evaluate. The problem is determining which information deserves attention and which information merely reflects short-term noise.
Data and Decision-Making
In forecasting discussions, I have occasionally seen teams spend more time debating whether a metric was moving than whether the movement was meaningful. The information was available almost immediately; agreement on its significance often proved far more difficult.
Consider a common example. A company approves additional hiring because demand appears to be strengthening. Months later, management can identify the financial outcome but may struggle to reconstruct the assumptions that supported the decision. Was demand expected to remain elevated for two quarters or six? Was the decision intended to support growth, improve service levels, or address capacity constraints?
Finance functions routinely review results against budgets, forecasts, and prior periods. Variances are analyzed and forecasts revised. Financial outcomes receive considerable scrutiny. The original decision logic receives much less.
I have been in post-period reviews where teams could explain precisely why financial results differed from plan, yet no one could confidently explain whether the underlying decision had been sound based on the information available at the time. The distinction matters. A good decision can produce an unfavorable outcome; a poor decision can occasionally produce a favorable one. But without preserving assumptions and reviewing decision quality separately from financial results, organizations may learn the wrong lessons.
Incentives can reinforce the issue. Leaders are often rewarded for responsiveness, decisiveness, and execution speed. Those traits are valuable. They can also create subtle pressure to act before assumptions have been challenged adequately. Over time, organizations may begin equating action with judgment. The two are not always the same.
None of these issues are caused by real-time information. The difficulty arises when organizations assume that faster visibility compensates for weaknesses in how decisions are evaluated, documented, and reviewed. More information may improve decisions, but it does not guarantee better decisions.
Where the Problem Shows Up
Weak decision discipline rarely appears as a dramatic failure. More often, it emerges through reasonable decisions that appear justified individually. An organization experiences several months of strong sales growth and begins discussing expansion. Hiring follows. Inventory commitments increase. Marketing spending rises. Each decision may be supported by current operating data, while the underlying assumptions remain less certain.
I have seen situations where management teams were monitoring revenue trends almost daily, yet they invested relatively little effort in understanding whether those trends were sustainable. Growth was visible. The drivers of growth were less certain. By the time questions emerged regarding customer retention, pricing durability, or margin quality, resources had already been committed.
Similar patterns appear in workforce planning. A manufacturer experiences three months of 18% sales growth and approves 25 additional production employees based on the assumption that demand will remain elevated through the following year. Six months later, demand normalizes. During the review process, finance discovers that the hiring decision relied heavily on recent order activity but included little analysis of customer concentration, backlog composition, or order durability. The outcome was not necessarily caused by poor data. The underlying assumptions were simply never documented or revisited. The organization responded quickly; whether it responded prudently is a different question.
Decision Architecture
Organizations often respond to decision quality concerns by requesting additional reporting, new metrics, or more detailed dashboards. Those efforts may improve visibility, but they do not necessarily improve judgment.
What is often missing is a structured approach for converting information into decisions. Decision architecture is the term I use for the assumptions, review processes, accountability mechanisms, and analytical disciplines that shape how important decisions are made.
The first component is information quality. Real-time data creates little value if management lacks confidence in its accuracy or consistency. Before an organization can improve decisions, it must establish confidence in the information supporting those decisions.
The second component involves interpretation. Data rarely explains itself. A decline in margin may reflect pricing pressure, changes in customer mix, labor costs, timing differences, or some combination of factors. Two experienced executives can review the same report and reach different conclusions. Without a disciplined approach to interpretation, additional information often increases debate rather than clarity.
A third component is accountability. Significant decisions are often supported by forecasts or analysis, yet the assumptions behind those decisions are rarely preserved. Without them, organizations may struggle to distinguish between a flawed decision and a genuinely reasonable decision that was affected by changing circumstances.
The final component is outcome review. Most finance functions are highly skilled at reviewing results. Fewer organizations consistently review the quality of the decision process itself. Some of the most valuable lessons emerge from examining what management believed, what ultimately occurred, and why the two differed. Those conversations often reveal weaknesses in assumptions, blind spots in analysis, and occasionally decisions that were better than the outcomes suggest.
Individually, none of these elements is particularly novel. Together, they help organizations derive greater value from real-time information. Information quality establishes trust, interpretation provides context, accountability preserves decision logic, and outcome review creates learning.
Without those elements working together, information velocity may simply accelerate inconsistent judgment. Real-time data can shorten the distance between events and awareness. The harder task is converting that awareness into sound decisions.
Implications
The role of finance may be evolving in ways that are easy to overlook. Historically, the finance function earned influence by producing reliable information. That responsibility remains essential, but it is increasingly supported by technology. So, as information becomes more accessible, the source of value is shifting.
Finance leaders are often positioned to provide something many organizations need more urgently than additional reporting: structure around how decisions are evaluated. This does not mean finance should own every significant decision. It does suggest that finance is uniquely positioned to challenge assumptions, evaluate trade-offs, assess risk, and create discipline around decisions that might otherwise remain informal.
Forecasting becomes less about predicting a single outcome and more about testing assumptions. Variance analysis becomes an opportunity to evaluate decision logic rather than simply explain results. Capital allocation discussions become stronger when underlying assumptions are documented clearly enough to revisit later.
The distinction will create opportunities for firms willing to move beyond discussions of reporting efficiency. Questions surrounding assumption development, decision accountability, governance, and post-decision review often receive less attention than technology selection, yet they may have a greater influence on long-term business performance.
As compliance work becomes more automated and reporting becomes more immediate, the advisory role of the profession may continue to expand because finance professionals are trained to evaluate evidence, challenge assumptions, and connect individual decisions to broader economic outcomes.
The next stage of finance maturity will likely be defined by a disciplined approach to deciding what that information means.
Conclusion
The challenge has shifted from obtaining information to interpreting it. Real-time reporting solved a visibility problem. It did not solve the more difficult question of how organizations evaluate assumptions, weigh competing risks, or determine which actions deserve confidence.
In many respects, the future of finance may depend less on producing information and more on creating discipline around how information is used. Those who derive the greatest value from real-time data are more likely to be those who develop clear decision frameworks, preserve assumptions, review outcomes thoughtfully, and learn from experience.
That may sound less exciting than new technology, but it is where much of the value resides. Organizations have solved much of the problem of knowing what is happening. The next challenge is knowing what to do about it.
Anthony J. Borrelli is a staff accountant at Maillie LLP in West Chester. He holds a bachelor’s degree in accounting from the University of Pittsburgh and is a member of the Pennsylvania CPA Journal Editorial Board. He can be reached at aborrelli@maillie.com.