Reporting and Analytics

Reporting and analytics are two separate words, and it is important to understand the difference between them.

Reporting is the process of assembling data into factual summaries to monitor how different areas of business are performing. There is no judgement or insights in reporting.

While Analytics is the process of extracting relevant insights by exploring data and reports. It is used to understand and improve business performance by injecting expertise and knowledge in analysis to deliver output.

What is the Difference between Reporting and Analytics?

Reporting and analytics are a wide term, and many people are not sure about the extensive differences between them.

The general difference is that the reporting turns data into information, while analysis turns information into insights.

Here are 4 differences between reporting and analytics

Purpose

Reporting is concerned with monitoring businesses, alerting them about the rise and fall of expected ranges, and raising questions. A good reporting practice is to ask questions from the user’s point of view.

The analysis is aimed to answer those questions by in-depth analysis of data and provide practical recommendations. You also provide answers or potential answers that can be tested in summary.

In short, we can describe reporting as WHAT IS HAPPENING? and analytics as WHY IT IS HAPPENING?

Tasks

It is very easy to mix up tasks of reporting and analytics because the analytics team are not evaluating the tasks and ends up spending most of the time in reporting tasks.

Hence to ensure balance, here is the list of tasks of reporting and analytics phases.

In reporting, we usually perform building, configuring, organizing, consolidating, formatting and summarizing data.

On the other hand analysis consists of questioning, examining, interpreting, comparing, conforming and predicting.

Outputs

Reporting and analytics may appear to be the same with a lot of graphs, tables, stats, charts etc. But they differ in outputs.

In reporting, we take the push approach, i.e. the data is pushed down to the user to extract output. There are three types of outputs in reporting.

• Canned Reports are unorthodox custom reports which you can access through different tools and can send to end-users. This type of report is static because it has set metrics and dimensions.
• Dashboards Reports are a combination of KPI (Key Performance Indicator) and a high-level view of business performance for users. Dashboards have data from various sources, and they are also static.
• Alerts are provoked with the fall in expected ranges, or the business hasn’t met the determined criteria. The objective of the alert is to notify the users and initiate appropriate actions.

On the contrary, the analysis uses the pull approach; in this, the analyst pulls out the data to answer the question. There are two types of analysis.

• Ad Hoc Response is an urgent and time-sensitive request and demands a quick answer. In this, the analysts are bombarded with questions, and the team has to address multiple questions simultaneously. Analysts are unable to perform deep research because of time shortage.
• Analysis Presentations are complex and time-consuming, and analysts perform a deep search. This type has two sections: Key Findings and Recommendations. Key Finding highlight the meaningful and actionable insights while recommendations provide a guide on the basis of analysis findings.

Delivery

Automation is the focus in reporting, building and delivering the report. After the reporting building, it’s time to deliver it, and some people ask for periodic delivery, and sometimes the report has to be delivered to a huge audience. So, analysts use automatic tools to send reports and build new reports like the previous ones.

The analysis focuses on manual work. The power of reasoning and analytical skill of humans is used to extract insights and recommendations from the information.

Best Reporting and Analytics Tools

These tools are the essential part of reporting and analyzing. Reporting and Analytic Tools are used to connect to data sources, gather information, provide insights. Moreover, they can create and execute the reports and analytics process.

There are numerous reporting and analytics tools; in this article, you will get to know about the best reporting tools and analytics tools.

Reporting Tools

1. Fine Report

Fine Report is a JAVA based reporting tool for enterprises. It handles every reporting process easily and intelligently. The large tool can deal with complex data needs and also provide insights into the business operation. Moreover, it has three report design patterns for IT and business departments to meet different report scenarios.

Fine Report has wide data sources connection and integration with multiple resources; you can also embed it with CCTVs and BIM. It can export reports in Excel sheet, PDF, PNG with 2D, 3D or HTML charts.

1. What a Graph

It is a cross-channel marketing performance reporting tool that allows them to track measures and analyze their marketing efforts.

What a Graph has integration for 30 data channels and custom APIs for high-level analytics. The tool creates beautiful visual reports and automates them in few clicks.

• Xplenty

It is a cloud-based data integration platform for marketing, sales, support and developer’s solution. Xplenty can build complete marketing and sales analytics solutions. Moreover, you can also create constructive and extensive campaigns and strategies.

Xplenty allows the user to integrate customer support and data from different sources like social media. You also have the choice to use the low-code or no-code option as well, which is handy for new users.

• Answer Rocket

Answer Rocket is another easy-to-use web-based tool; a person with no technical skill can use it very easily.

The charts are easy to customize, and reports get automatically saved to the dashboard. Moreover, you can also schedule email delivery.

Analytics Tools

1. Apache Spark

Spark is an open-source processing engine for analytics. It has gained popularity in the past 2 years due to its easy integration and Hadoop ecosystem. Spark can easily handle large unstructured data.

Apache Spark has its machine learning library, which is ideal for analytics.

• R

R is the most popular analytics tool; the developers are constantly working to make it more versatile. They have added over 1800 packages between April 2015 and April 2016 which takes the total to 8000. There are concerns with the big number of packages but huge number has definitely added to the abilities of R.

R is great for large sums of data and also integrates greatly with big data platforms.

• Microsoft Power BI

Microsoft Power BI is a business intelligence platform; it supports multiple data sources and also allows users to create reports and dashboards.

It also allows the user to post reports, and the user can use the Power BI app for quick delivery. Another good feature is the ability to implement automatic models through Azure Machine Learning.

• Apache Storm

Apache Storm is a big data tool for moving data. It works with static data and is perfect for real-time analytics and processing.

You can also hire professional companies like seodigitalmarketers.

Why do firms need to create separate data repositories for their reporting and analytics work?

It is an important question, and many people don’t have the answer to that.

So, here is how you can answer the question.

• Most firms store their data assets in separate data repositories to protect the data against the possibility of data damage through natural disasters.
• Maintaining huge databases in-house can be costly.
• Most organizations need to differentiate in-house data and data from aggregators.
• Running analytics against transactional data can slow down the system.
• Reporting and analytics are two separate functions; each requires its own database specifically formatted to the management team’s needs.