The terms “reporting” and “analysis” are often used synonymously within companies and their management accounting processes. Of course, there are some similarities. For example, the ultimate goals of both reporting and analysis are to increase sales revenues, to reduce excessive costs or to maintain transparency within the company structure—not to underestimate their roles in influencing behavior, shaping opinions as well as initiating actions, ultimately leading to highlighted organizational value.
Nevertheless, there is no similarity between reporting and analysis if we take a closer look into their initial purpose, ordinary assignment of tasks, and ways of delivering and extracting information. Only in some kind of equitable co-existence can both reporting and analysis boost their potential and value. Therefore, no company representative should disregard any one of these two processes and be aware of even nuanced differences in the meaning of these terms.
To go into this in detail, we will divide the process of data-driven decision-making into three successive stages: purpose (or rather tasks), methods of information delivery, and creation of value.
#1 Purpose / Tasks
Seeking in-depth definitions of both reporting and analysis in the literature on Economics, you can find one of the following expressions.
Reporting: Defines a process of making collected data more “tangible” and clustered. Reports serve as information transmitters for management levels and operating departments. For example, they monitor the specific performing levels of different sales regions. In the end, the ideal conception is based on a regular provision of information to the organization’s decision-makers, highly supporting them in their work.
Analysis: Generally speaking, analysis means the procedure of breaking a complex topic into several smaller parts. In relation to management accounting, it defines a process of screening an existing level of data and information (based on the already existing reporting schemes) plus extracting specific insights—a procedure which can be used afterwards to gain a greater understanding of business matters, predicting future developments and enhancing overall performance.
To put it in other words, reporting is capable of translating raw data into some form of valuable information while acting as a benchmarking tool (“What is happening or has happened?”). Analysis instead is driven by its main goal of providing recommendations (often transformable into immediately initialized measures), explanations or rather potential answers by interpreting data on a much deeper level (“Why is something happening and which actions can we undertake to strengthen or stop desired/undesired incidents?”).
As already stated, ideally both reporting and analysis co-exist equally within the organizational structure. If you want to know whether this is true for your enterprise, you could focus on the primary tasks that are being fulfilled by your management accounting staff. The majority of working time is used for tasks like questioning, comparing, researching, doing evaluations or confirming—analysis has a bigger impact on daily working routine. On the other hand, reporting is overvalued against analysis if management accountants’ performance can be best described by terms such as composing, configuring, organizing, editing, consolidating or summarizing.
#2 Information Delivery
In describing the ways of information delivery (the outputs), we have to distinguish between push and pull approaches.
Usually, reporting follows the push approach. Mainly static, out-of-the-box and customized reports are delivered to their specific recipients on a regular schedule (daily, weekly, monthly, etc.) via their mailboxes, mobile devices, in print form, and so on—whether curiously awaited or simply ignored.
Therefore, automation plays a role. Once a new report has been prepared, how can it be refreshed regularly by using automated routines instead of wasting too much manpower resources in updating charts, graphs, tables, statistics, and texts?
In most cases, the recipient of each report is expected to draw the correct conclusions from the reports’ information and data, as well as initiate effective measures by him/herself (self-service approach). So reports often only state that something is going wrong for the company (maybe not even exactly defined), but without providing appropriate solutions to end this tumbling trend.
Analysis, on the contrary, is based on the pull approach. The analyst pulls specific information and data out of different sources and “digs” into it—with the ultimate goal of answering questions and (at least contributing to) solving problems as well as providing guidance on necessary actions.
Eventually, there are two main types of triggers for some kind of analysis—resulting either in analytical presentations (which are focused on more complex business questions compared with common reports, therefore needing more time to be published and highlighting key findings and recommendations) or in ad hoc responses (ad hoc requests often come up from too vague report outputs; there is only a small timeframe to provide answers).
In contrast to reporting, it is not possible to perform analysis on a progressively automated routine. Compared with human beings, robots are not yet capable of providing the superior rationale and analytical skills needed to gain outstanding insights from the data, and of recommending implementable measures to the organization’s decision-makers.
In general, compared with reporting, analysis places value on context, explanations and recommendations because it wants to properly answer the question: “Why is it happening and what can we do about it?” If data analysis is predestined to initiate effective decisions, measures and actions, the persons acting need to understand the situation’s backgrounds and periphery to interpret data results correctly.
In this context, analysis also gives a much deeper explanation of specific figures. While reporting only (if at all) highlights significant numerical values or key variances, analysis additionally provides causal commentaries concerning their (un)importance to the organization.
Finally, in the standardized accounts of reporting, you normally will not find recommendations for actions to be taken. In contrast, analysis provides guidance on prospective measures based on its key insights found in the data, and examines the impact and effectiveness of actions that were already taken in earlier periods or stages.
#3 Creation of Value
We have closely looked at the different characteristics of reporting and analysis. But how do both contribute to strengthen the organization’s value? There are diverse stages that precede the final creation of valuable outcome:
Data > Reporting > Analysis > Decisions > Measures > Value
To create some kind of valuation, everything stands and falls with the quality of the present data. Only reliable, complete and thoroughly examined data will lead to useful reporting schemes, which are needed to produce good analytics. Since reporting and analysis are intertwined, reports’ presentation and findings will raise specific questions that can only be answered after some in-depth analysis has been conducted on the subject.
The analysis part usually provides several recommendations and will call for decisions to be made (dos, don’ts, open issues)—finally followed by initiated measures as well as potential value that can be realized if the right actions have been implemented.
In brief: Reporting vs. Analysis
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