Big Data Analytics – Analysis Vs Reporting

We all heard these two terms in different fields. If we take a project as a simple example, it contains these two steps: analysis and reporting. Without these steps, we can’t complete a project. While we use these terms under the same collected data, reporting and analysis are very different in terms of their purpose, tasks, outputs, delivery, and value. These two are the most critical and challenging steps in the overall data-driven decision-making process in an organization. Before dealing with these terms, we have to know the difference as well.

Let’s see a simple definition for analysis and reporting.

Analysis: The process of exploring data and reports in order to extract meaningful insights, which can be used to better understand and improve business performance.

Reporting: The process of organizing data into informational summaries in order to monitor how different areas of a business are performing.

Now, let’s discuss the key differences between analysis and reporting.

  • Reporting translates raw data into information. Analysis transforms data and information into insights.
  • Reporting helps companies to monitor their online business and be alerted to when data falls outside of expected ranges. Good reporting should raise questions about the business from its end users. The goal of the analysis is to answer questions by interpreting the data at a deeper level and providing actionable recommendations.
  • Through the process of performing analysis you may raise additional questions, but the goal is to identify answers or at least potential answers that can be tested. In summary, reporting shows you what is happening while analysis focuses on explaining why it is happening and what you can do about it.

Scope

The ultimate goal for reporting and analysis is to increase sales and reduce costs (i.e., add value). Both reporting and analysis play roles in influencing and driving the actions which lead to greater value in organizations.

Let’s explain for more clarity, by- Purpose, Tasks, Outputs, Delivery, and Value.

Purpose

Good reporting should raise questions about the business from its end users. The goal of the analysis is to answer questions by interpreting the data at a deeper level and providing actionable recommendations. In summary, reporting shows you what is happening while analysis focuses on explaining why it is happening and what you can do about it.

Tasks

If most of the team’s time is spent on activities such as building, configuring, consolidating, organizing, formatting and summarizing – that’s reporting. The analysis focuses on different tasks such as questioning, examining, interpreting, comparing and confirming.

Outputs

Reporting follows a push approach, where reports are pushed to users who are then expected to extract meaningful insights and take appropriate actions for themselves (i.e., self-serve). Three main types of reporting are canned reports, dashboards, and alerts.

In contrast, analysis follows a pull approach, where particular data is pulled by an analyst in order to answer specific business questions. There are two main types of analysis: ad hoc responses and analysis presentations.

Delivery

As mentioned, reporting is more of a push model, where people can access reports through an analytics tool, Excel spreadsheet, widget, or have them scheduled for delivery into their mailbox, mobile device, FTP site, etc.

While analysis is all about human beings using their superior reasoning and analytical skills to extract key insights from the data and form actionable recommendations for their organizations. Although analysis can be “submitted” to decision-makers, it is more effectively presented person-to-person.

Value

It’s important to understand the relationship between reporting and analysis in driving value. The “Path to Value” diagram is given below:

The important thing is that, if you remove any of these stages, it can be more difficult or impossible to achieve the desired value.

Reporting will rarely initiate action on its own as the analysis is required to help bridge the gap between data and action.

Next, we will discuss Data Analytic Tools.

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