Category Archives for "Data Analysis"

Mar 06

MIS vs Business Intelligence (BI)

By ganpati | Data Analysis , Getting Started

A management information system (MIS) in simple terms is a system where all the organizational data and system data is collected on a historical basis. It can be present as a data warehouse that gathers data from different sources on a continuous basis.

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The objective is to obtain customized reports from the system based on requirements of managers. The main purpose of the MIS is to give managers feedback about their own performance; so that top management can monitor the company as a whole. Information displayed by the MIS typically shows “actual” data over against “planned” results and results from a year before; thus it measures progress against goals.

Also read What is Business Analytics

Business Intelligence (BI) covers all the processes from the source data to the final reporting. From that perspective, MIS is just a part of the whole BI framework. It encompasses data staging, data warehouse, reporting tools etc.

  • Source data: These are different datasources and transactional system where data is captured across the organization. It can be CRM, ERP or other operational systems to capture day to day transactions.
  • Storing data in operational data store: Day to day data from transactions that are carried out by various departments are stored in local databases which serve the purpose of a department or a part of the whole organization. These are also known as transactional databases.
  • Data Warehouse: Consolidating data from different departments in a data warehouse that can serve as a central data repository for the organization. It also checks the completeness of data and consistency between sources
  • Reporting: Consolidated data is presented to the management in the form of reports so that they can gain understanding of the current state of business.
  • In terms of reporting the data to the management BI is an advanced process of reporting which is applies more analytics and visualization techniques over the data for easy consumption of the users.

    Jan 29

    A Simple Step by Step Guide to QlikView Expertise

    By ganpati | Data Analysis

    Visualization should be the basis of any analytics job- be it BI or Data Science. The simple goal of visualization is to find out what your data is telling you and what it will take to make any decision based on the data. Deriving insights is one thing but communicating it in the form of reports, dashboards, or interactive visualizations (also known as Business Intelligence) requires a completely different skill.

    QlikView is one of the most intuitive Business Intelligence (BI) tool which is also very fast to deploy and easy to learn. So, if you want to communicate the insights that you just derived from your data to a large audience, Qlikview can be a good choice.

    In this post I’ll discuss how to master Qlikview in a few steps. I’ve mentioned the steps to be covered and provided the links from where it can be learnt.

    1. Getting started

    2. Getting into details

    3. Advanced Visualization techniques

    So far you would have gained a decent grasp over the tool. There are more topics that you may explore like data transformation, advanced expressions, security etc.

    Dec 10

    Pricing Analytics with Sales Data

    By ganpati | Data Analysis

    There are many service based industries that are adopting dynamic pricing or other pricing strategies based on analytics to beat competition and maximize profit. There are already some interesting cases available in Insurance, Air Travel and Hotel booking industry. In this post I’ll discuss two other interesting cases from the manufacturing industry where analytics based pricing can be applied for manufactured items and in auctions.

    In this post we will discuss two interesting posts from Kaggle to explain how pricing analytics problems can be addressed. There can be many more ideas on which can work on. Please feel free to post in the comment section if you have ideas about other pricing strategies that can be executed with analytics.