Management Information Systems

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A management information system (MIS) is a set of systems and procedures that gather information from a range of sources, compile it and present it in a readable format. Managers use an (MIS) to create reports that provide them with a comprehensive overview of all the information they need to make decisions ranging from daily minutiae to top-level strategy. Today’s management information systems rely largely on technology to compile and present data, but the concept is older than modern computing technologies. The main purpose of a management information system is to make managers’ decision-making more efficient and productive. By pooling information from a range of sources into a single database and presenting the information in a logical format, an (MIS) can provide managers with everything they need to make highly informed decisions and perform in-depth analysis of operational issues. An (MIS) can collect nearly any type of information managers require. They can view financial data such as daily revenues and expenses at a glance and attribute them to specific departments or groups. Performance indicators such as the timeliness of projects or the quality of products coming off an assembly line can help managers pinpoint areas of needed improvement. Staff can manage schedules for work shifts, incoming deliveries and outgoing shipments from any place linked to the (MIS). A management information system can facilitate collaboration and communication as well. Employees can edit and share documents and communicate relevant information on anticipated developments and warnings across the organization. The ability to create reports is one of a management information system’s most valuable features. Internal reports present information in a way that managers can understand, by including all relevant data and grouping data in a logical manner. For example, a report viewed by a corporate manager for a restaurant chain may show revenue, expenses, labour-hours and volume of each outlet, allowing him to see which store makes the most money per employee on the floor and which stores have higher expenses compared to revenue and volume–an indicator of waste or theft. According to the U.S. Department of Labour, non-profit organizations can use an (MIS) to automatically generate reports required by the federal government. This allows employees and volunteers to focus their time on more productive activities and can reduce errors and the costs associated with resubmitting federal reports. Front-line employees can use an (MIS) to perform their jobs more effectively as well. For example, employees at all levels can consult an (MIS) to check on the status of inventory items, view statistics related to their specific department or group and request internal transfers of materials. A management information system can be a costly investment. In addition to purchasing an (MIS) software package and hiring extra IT personnel to oversee and maintain the system, a company must train all employees to use the system. Front-line employees often perform the first two steps in an (MIS), data collection and input, leaving them with less time to focus on productive activities; this can increase overall salary expenses. Weigh the costs of an (MIS) against the potential benefits before implementing this tool in your small business.

Data Management


A Definition of Data Management

Data management is an administrative process that includes acquiring, validating, storing, protecting, and processing required data to ensure the accessibility, reliability, and timeliness of the data for its users. Organizations and enterprises are making use of Big Data more than ever before to inform business decisions and gain deep insights into customer behavior, trends, and opportunities for creating extraordinary customer experiences.

To make sense of the vast quantities of data that enterprises are gathering, analyzing, and storing today, companies turn to data management solutions and platforms. Data management solutions make processing, validation, and other essential functions simpler and less time-intensive.

Leading data management platforms allow enterprises to leverage Big Data from all data sources, in real-time, to allow for more effective engagement with customers, and for increased customer lifetime value (CLV). Data management software is essential, as we are creating and consuming data at unprecedented rates. Top data management platforms give enterprises and organizations a 360-degree view of their customers and the complete visibility needed to gain deep, critical insights into consumer behavior that give brands a competitive edge.

Data Management Challenges

While some companies are good at collecting data, they are not managing it well enough to make sense of it. Simply collecting data is not enough; enterprises and organizations need to understand from the start that data management and data analytics only will be successful when they first put some thought into how they will gain value from their raw data. They can then move beyond raw data collection with efficient systems for processing, storing, and validating data, as well as effective analysis strategies.

Another challenge of data management occurs when companies categorize data and organize it without first considering the answers they hope to glean from the data. Each step of data collection and management must lead toward acquiring the right data and analyzing it in order to get the actionable intelligence necessary for making truly data-driven business decisions.

Data Management Best Practices

The best way to manage data, and eventually get the insights needed to make data-driven decisions, is to begin with a business question and acquire the data that is needed to answer that question. Companies must collect vast amounts of information from various sources and then utilize best practices while going through the process of storing and managing the data, cleaning and mining the data, and then analyzing and visualizing the data in order to inform their business decisions.

It’s important to keep in mind that data management best practices result in better analytics. By correctly managing and preparing the data for analytics, companies optimize their Big Data. A few data management best practicesorganizations and enterprises should strive to achieve include:

  • Simplify access to traditional and emerging data
  • Scrub data to infuse quality into existing business processes
  • Shape data using flexible manipulation techniques

It is with the help of data management platforms that organizations have the ability to gather, sort, and house their information and then repackage it in visualized ways that are useful to marketers. Top performing data management platforms are capable of managing all of the data from all data sources in a central location, giving marketers and executives the most accurate business and customer information available.

Benefits of Data Management and Data Management Platforms

Managing your data is the first step toward handling the large volume of data, both structured and unstructured, that floods businesses daily. It is only through data management best practices that organizations are able to harness the power of their data and gain the insights they need to make the data useful.

In fact, data management via leading data management platforms enables organizations and enterprises to use data analytics inbeneficial ways, such as:

  • Personalizing the customer experience
  • Adding value to customer interactions
  • Identifying the root causes of marketing failures and business issues in real- time
  • Reaping the revenues associated with data-driven marketing
  • Improving customer engagement
  • Increasing customer loyalty

Specifically, Big Data Analytics enables enterprises to narrow their Big Data to the most relevant information and analyze it to inform critical business decisions. This proactive approach to business is transformative because it gives analysts and decision makers the power to move ahead with the best knowledge and insights available, often in real time. This means that companies can improve their customer retention, develop better products, and gain a competitive advantage by taking rapid action to respond to market changes, indications of critical customer shifts, and other metrics that impact business. Enterprises utilizing Big Data Analytics with fidelity also have the ability to boost sales and marketing results, discover new revenue opportunities, improve customer service, optimize operational efficiency, reduce risk, and drive other business results.

Trends in Big Data Analytics

Big Data Analytics is changing the way the world does business, which also means that it is changing technology and business practices. Robert L. Mitchell, Computerworld contributor and chief editor of, explains that Big Data technologies and practices move quickly and states that “top emerging technologies and trends should be on your watch list.” Mitchell compiled a list of the hottest trends in Big Data Analytics, based on input from IT leaders, consultants, and industry analysts:

  • Big Data Analytics in the cloud
  • Hadoop: The new enterprise data operating system
  • Big Data lakes
  • More predictive analytics
  • SQL on Hadoop: Faster, better
  • More, better NoSQL
  • Deep learning
  • In-memory analytics

Mitchell explains that these trends are emerging so quickly that IT organizations must “create conditions that will allow analysts and data scientists to experiment.” Collaboration between IT and analysts should occur in order to meet the needs of organizations and deliver the resources necessary for Big Data Analytics.

The increasingly widespread use of Big Data Analysis solutions is a clear indication that Big Data is not just a fad: it’s a business practice that is here to stay because of the insights it delivers to enterprises that want to gain a competitive edge, improve sales and marketing team performance, increase revenue, and make proactive data-driven business decisions.

Business Intelligence


Business intelligence (BI) is a technology-driven process for analysing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. BI encompasses a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.

Continue reading Business Intelligence

R Blog

R is a statistical programming tool used for analysing data sets and creating visualisations of data. While it can seem tricky at first given that the programme is entirely code based, but it can be handy tool. Download R from You can learn how to use R on different sites such as stack overflow, but these can be a bit over complicated. It’s best to start of simple like with ‘Try R code school’, which is a pirate themed – not sure why – step by step guide for learning the basics in R. There are 8 badges that can be ‘earned’ when using Code school and when this is completed you achieve your 8 badges like I just did – Yaaargh.

My R badges

Now to put my new learned skills to use I will demonstrate how I created linear regression analysis in R. I also will demonstrate Visual diagnostics of simple linear regression with ggvis function. To accomplish that we will need to install several packages.

  • ggvis is the new standard tool for data visualization in R by RStudio. It lets you create static and interactive graphs to display distributions, relationships, model fits, and more. Similar to ggplot2, ggvis uses the grammar of graphics. The grammar provides an intuitive framework that lets you describe – and make – any plot that you can think of in your head. By learning the four components of the grammar, you empower yourself to make thousands of different types of ggvis data visualizations.
  • Best of all, ggvis plots are true web documents. You can save them as png’s for publication, but they come ready to be shared over the INTERNET. Each ggvis plot can be viewed in a web browser, which opens opportunities not available in R’s native graphics device. For example, with a one or two lines of code, you can turn a ggvis plot into an animation or an interactive data exploration tool. This enables you to do rich data visualizations for analytic s, communication and the web.
  • The format of the visual properties needs a little explanation. We use ~before the variable name to indicate that we don’t want to literally use the value of the mpg variable (which doesn’t exist), but instead  we want to use the mpg variable inside in the dataset. This is a common pattern in ggvis: we’ll always use formulas to refer to variables inside the dataset.
  • To make life easier ggvis uses the %>%(pronounced pipe) function from the magrittr package.
  • The first two arguments to ggvis()are usually the position, so by convention you can drop x and y:
  • You can add more variables to the plot by mapping them to other visual properties like fill, stroke, size and shape.
  • If you want to make the points a fixed colour or size, you need to use :=instead of =. The :=operator means to use a raw, unscaled value. This seems like something that ggvis() should be able to figure out by itself, but making it explicit allows you to create some useful plots that you couldn’t otherwise. See the properties and scales for more details.

Preparing the data: For my CA i choose to work with Auto data. I downloaded csv file from one of statistics  websites. I will attach Auto data to R using read.csv function , making sure to mark that this data set has headers. It’s important to note here that before you can use the read.csv function that you change the directory of the R workspace to the folder that the file your looking for is actually in(!)

Google Fusion Tables


This project is to create a heatmap outlining an Irish Population based on the 2011 census data. The map will show random population density of the counties in Ireland. The Data used for this project was downloaded from Irish Population Census 2011 from CSO. Irish KMZ Datafile from independent. Downloaded data was put into MS EXCEL for analysis and filtered. Original Data contained 4 columns. Columns were named province, males, females, and total persons. I had to clean this data and filter it to the remain of 2 columns Province and Total Persons. There was couple of discrepancies in data found during cleaning process  which were corrected accordingly. This Excel file was saved and uploaded to Google fusion maps. We also had Irish KMZ Datafile available to us on moodle, which was obtained from Irish Independent website. I merged this file to my Google fusion table to generate the Irish population density Heat Map. From the table below we can see that Dublin and Louth has the highest population density in the Republic of Ireland. Leitrim, Mayo and Roscommon on the other hand are the counties with the smallest population density.


Irish population

I than used more data, from the

filtered the Data for relevance to the Republic of Ireland only, and saved it in Excel file. I merged it then with KMZ Datafile, from the above to generate another visual presentation of  Population density per Republic of Ireland Counties Area. The link below is the link to view it in Google Fusion Tables.

Irish pop 2

Fusion Tables allow you to represent data visually and in quick, convenient way, saving you time to go through  enormous amount of data in your spreadsheets, and than trying to understand the information that you meant to extract from it. The easiest way to understand a heat map is to think of a table or spreadsheet which contains colours instead of numbers. The default colour gradient sets the lowest value in the heat map to dark blue, the highest value to a bright red, and mid-range values to light grey, with a corresponding transition (or gradient) between these extremes. Heat maps are well-suited for visualizing large amounts of multi-dimensional data and can be used to identify clusters of rows with similar values, as these are displayed as areas of similar colour.

The heat map is one of the most useful and powerful data-analysis tools available in business intelligence. What  BI can be gained from the heat map we have created, well for someone who is looking to start a new business  in Ireland, it will be easy to forecast the footfall of potential customers. For someone with existing business an advantage might be in advertisement campaign  which can be ran in certain counties with the highest populations to be more effective. Even the least populated areas, might be of an interest to government officials , for example with intention to populate then with an Asylum seekers to grow infrastructure e t c.