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Management Information System (MIS)

An MIS report is a tool that managers use to evaluate business processes and operations. Business managers at all levels of an organization, from assistant managers to executives, rely on reports generated from these systems to help them evaluate their business’ daily activities or problems that arise, make decisions, and track progress.

MIS system reporting is used by businesses of all sizes and in every industry.


Who Uses MIS Reports?

There can be as many types of MIS reports as there are divisions within a business. For example,

  • information about sales revenue and business expenses would be useful in MIS reports for finance and accounting managers.
  • Warehouse managers would benefit from MIS reports about product inventory and shipping information.
  • Total sales from the past year could go into an MIS report for marketing and sales managers.


Type of Information in an MIS Report

To make this information most useful, you also need to ensure that it meets the following criteria:

  • Relevant - MIS reports need to be specific to the business area they address. This is important because a report that includes unnecessary information might be ignored.
  • Timely - Managers need to know what’s happening now or in the recent past to make decisions about the future. Be careful not to include information that is old.
  • Accurate - It’s critical that numbers add up and that dates and times are correct. Managers and others who rely on MIS reports can’t make sound decisions with information that is wrong.
  • Structured - Information in an MIS report can be complicated. Making that information easy to follow helps management understand what the report is saying.


Data Analytics

Data Analytics predominantly refers to an assortment of applications, from basic Business Intelligence (BI), reporting and Online Analytical Processing (OLAP) to various forms of advanced analytics.

Types of Data Analytics Applications

  • Exploratory Data Analysis (EDA), which aims to find patterns and relationships in data,


  • Confirmatory Data Analysis (CDA), which applies to determine whether hypotheses about a data set are True or False.
  • Data Analytics can also be separated into Quantitative Data Analysis and Qualitative Data Analysis.


  1. Quantitative Data Analysis: This involves analysis of numerical data.
  2. Qualitative Data Analysis: The qualitative approach is more interpretive - it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view.


More advanced types of Data Analytics include–


  • Data Mining, which involves sorting through large data sets to identify trends, patterns and relationships;
  • Predictive Analytics, which seeks to predict customer behaviour, equipment failures and other future events; and
  • Machine Learning, an artificial intelligence technique that uses automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modelling.

Some Application areas of Data Analytics are as follows:

  • Data Analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyse withdrawal and spending patterns to prevent fraud and identity theft.
  • E-commerce companies and marketing services providers do clickstream analysisto identify website visitors who are more likely to buy a product or servicebased on navigation and page-viewing patterns.
  • Mobile network operators examine customer data to forecast so they can take steps to prevent defections to business rivals.
  • Healthcare organizations mine patient data to evaluate the effectiveness of treatments for cancer and other diseases.

Inside the Data Analytics Process


  • Data from different source systems may need to be combinedvia data integration routines transformed into a common format and loaded into an analytics system
  • Once the data that’s needed is in place, the next step is to find and fix data quality problemsthat could affect the accuracy of analytics applications. That includes data cleansing jobs to make sure that the information in a data set is consistent and that errors and duplicate entries are eliminated.
  • At that point, the data analytics work begins. A data scientist builds an analytical model, using predictive modelling tools or other analytics software and programming languages.
  • The last step in the data analytics process is communicating the results generated by analytical models to business executives and other end users to aid in their decision- making.

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