Management Information Systems

• Information supports day-to-day operations.

Business Functions

• Human Resources
• Finance
• Accountancy
• Production
• Sales and Marketing
• Manufacturing
• Customer Service
• Communication
• Purchasing
• Distribution / Logistics
• Research

Management Information System

• Organizes data to produce meaningful and useful information.

Data

• Unprocessed facts and figures.

Categories of Data

• Numeric: Numbers
• Non-Numeric: Text

Information Hierarchy (DIKW Model)

• The DIKW model was created by PROF. Ray R. Larson
• Wisdom
• Knowledge
• Information
• Data

Information Continuum

• An information continuum was made by Scott Andrew
• Data: A fact or piece of information.
• Information: Knowledge derived from data.
• Business Intelligence: Information management pertaining to collecting information, giving the ability to make good decisions.

4 V's of Big Data

• Volume: Scale of data
• Velocity: Speed of data processing
• Variety: Different data forms
• Veracity: Trustworthiness and quality of data

Information Management

• Systematic process of collecting, storing, organizing, maintaining, and distributing information.

Key Aspects of Information Management

• Data Collection: Gathering relevant data from various sources, including internal systems, customer interactions, or external sources.
• Data Storage: Safeguarding data in databases, cloud storage, or physical archive, ensuring easy access and retrieval while maintaining security and privacy.
• Data Organization: Structuring information in a way that allows for easy retrieval and efficient use, such as through categorization, tagging, or indexing.
• Data Processing and Data Analysis: Turning raw data into meaningful insights by applying analytical tools and techniques to help organizations make informed decisions.
• Data Distribution: Sharing the right information with the right stakeholders at the right time, using appropriate channels.
• Data Governance: Establishing policies, processes, and controls to ensure data quality, security, and compliance with legal and regulatory requirements.

Importance of Information Technology

• Improved Decision Making:
  • Access to Accurate Information: Information management ensures that decision-makers have quick access to accurate, reliable, relevant data, leading to informed and effective decisions.
  • Real-Time Insights: By organizing and analyzing data, IM enables real-time reporting analysis, helping organizations respond swiftly to market changes or internal issues.
• Operational Efficiency:
  • Increased Productivity: Reduce redundancy, avoid errors, and streamline workflows by organizing data efficiently.
  • Automation: Automate routine tasks like data entry and reporting.
• Enhanced Communication and Collaboration:
  • Centralized Information Access: Promotes better communication within an organization by creating a single source of truth.
  • Knowledge Sharing: Facilitates knowledge management by capturing and storing valuable insights that can be shared across the organization.
• Data Security and Compliance:
  • Protection of Sensitive Information: Helps secure critical data, reducing the risk of data breaches, theft, or unauthorized access.
  • Regulatory Compliance: Ensures organizations comply with legal industries.
• Cost Saving:
  • Efficient Resource Utilization: Properly managing information can lead to cost savings by optimizing storage, reducing duplication, and minimizing wasteful activities.
• Competitive Advantage:
  • Better Market Responsiveness: Respond more quickly to market opportunities and threats.
  • Data-Driven Innovation: Companies can harness Big Data and Analytics for product development, improving customer experiences, and creating innovative solutions.
• Improved Customer Service:
  • Personalization: Businesses can provide personalized experiences, improving customer satisfaction and loyalty.
  • Faster Response Times: Properly managed information allows customer service teams to resolve issues quickly by providing them with the necessary details.
• Risk Management:
  • Disaster Recovery: Enables businesses to back up and recover critical data in case of a system failure or disaster.
  • Mitigating Data Loss: By employing effective data storage and backup strategies, organizations minimize the risk of data loss.
• Informed Strategic Planning:
  • Long Term Insights: IM allows organizations to store and analyze historical data, offering valuable insights for long-term strategic planning.
  • Predictive Analysis: By leveraging trends and patterns in the data, organizations can anticipate future challenges or opportunities, leading to proactive rather than reactive strategies.
• Sustainability:
  • Paperless Environment: IM promotes digital record-keeping, reducing reliance on physical documents, paper, and storage space, contributing to environmental sustainability.
  • Resource Optimization: Organizations can use data to optimize resource use, reducing waste and driving more sustainable business practices.

Data Model

• A set of principles for organizing data.
• Data Items: Arranged into a hierarchy comprising Data Elements and Data Structures.
  • Data Items: Considered to be atomic or the simplest element of Data Organization that cannot be divided any further.
• A data model for organizing customer records is not recommended to keep names of individuals as single data items.
• The hierarchical nature of a Data Model is based on the fact that Data Elements are grouped by Data Items and consequently Data Structures are a logical collection of Data Elements.
• Used to organize data.

Data Items

• Constructs of fields.

Data Elements

• Records

Data Structures

• Files

Data Model

• Individual data items is characterized by some sort of a format, typically referred to as its Data Type.
  • Data Type: Indicates not only the acceptable form of a data item but also its format and possible range; Declares the appropriate operations that are possible on a data item.
    • Commonly used by BUSINESS INFORMATION SYSTEMS (BIS)(numbers, text, date, and time, and others)
    • Text: A series of characters composed of characters and other symbols.
    • Numbers: Integer, decimal, float, and other types of numbers.
    • Time and Dates: Including seconds, minutes, and hours.
• Information can be in a variety of forms and is stored in various channels.
• Almost any business is now operating a DATABASE.
  • Database: An organized collection of structured information of data typically stored in a computer system via software or app.
  • Some or all of the business areas can draw on the information stored in the CENTRAL DATABASE.
    • Sales Department: Stores data about customer orders.
    • Finance and Accounting: Uses sales data to generate invoices and process payments.
    • Marketing Department: Draws on customer data and sales information for effective marketing campaigns.
    • Human Resources: Stores information about the company, employees, their skills, and professional development skills.
    • Production Department: Facilitates keeping track of stock and production levels for manufacturing and production areas of business.

Problems That Arise Without Organization

• Data Redundancy: Data becomes duplicated and stored at several locations in more than one file.
• Poor Data Availability: Data becomes isolated and available only to the owner of a particular file in a file system; sharing of data and its visibility to employees becomes reduced.
• Poor Data Security: Data spread across business in various forms and locations reduces the ability of a business to set proper security controls and ensures authorized access to information.
• Error-Prone Data: When the same data exists in multiple locations it becomes more vulnerable to human errors introduced by different employees, and mistakes tend to go unnoticed for longer.

MIS Roles and Responsibilities

• Chief Information Officer (CIO): Responsible for overseeing all uses of MIS and ensuring strategic alignment of MIS with business goals and objectives.
  • Possesses a solid and detailed understanding of every aspect of an organization, coupled with tremendous insights into the capability of MIS.
  • Manager: Ensure the delivery of all MIS projects on time and within budget.
  • Leader: Ensure the strategic vision of MIS is in line with the strategic vision of the organization.
  • Communicator: Advocate and communicate the MIS strategy by building and maintaining strong executive relationships.
• Chief Technology Officer (CTO): Responsible for determining the throughput, speed, accuracy, availability, and reliability of an organization's information.
  • Ensures the efficiency of the MIS system throughout the organization
  • Handles hardware, software, and telecommunications.
• Chief Data Officer (CDO): Responsible for determining the types of information the enterprise will capture, retain, analyze, and share. CIO is responsible for the IS through which data is stored and processed, while CDO is responsible for the data, regardless of the IS.
• Chief Security Officer (CSO): Responsible for ensuring the security of MIS systems and developing strategies and MIS safeguards against attacks from hackers and viruses.
• Chief Privacy Officer (CPO): Responsible for ensuring the ethical and legal use of information within an organization.
  • The newest senior executive position in MIS.
  • Lawyers by training, enabling them to understand the often complex legal issues surrounding the use of information.
• Chief Knowledge Officer (CKO): Responsible for collecting, maintaining, and distributing the organization's knowledge.
  • Designs programs and systems that make it easy for people to reuse knowledge.
  • Must continuously encourage employee contributions to keep the system up-to-date.
• Chief Automation Officer (CAO): Determine if a person or business process can be replaced by a robot or software.
• Chief Intellectual Property Officer (CIPO): Manage and defend intellectual property, copyrights, and patents.
• Chief Sustainability Officer (CSO): Oversee the corporations “environmental" programs.
• Chief User Experience Officer (CUEO): Create the optimal relationship between user and technology.

Data Collection Techniques

• Refers to a method used to collect and analyze different forms of data.
• Standard Data Collection Techniques: Include going through documents related to a topic, conducting interviews with people who know about the subject, and listing observations that can help solidify the information gathered.
• Data Collection: Fundamental to gaining insights and making informed decisions.

Kinds of Data Collection

• Qualitative Data: Refers to data that describes characteristics, qualities, and other non-quantifiable traits of a certain object (personal opinions, descriptions of places, events, behavior, or quality).
• Quantitative Data: Refers to quantities or countable data, such as statistics, number of respondents, test subjects, and those under certain standards of measurement, such as temperature.

Data Collection Methods

• Primary Data Collection
  • Surveys: One of the most well-known methods of data collection. Done with questionnaires and can be conducted physically and digitally.
    • Two Types of questionnaire
      • Open-Ended Questionnaires: Consist of questions that can be easily and correctly interpreted; explore a problem and lead to a specific direction of answer.
      • Closed-Ended Questionnaires: Consist of questions that are used when the system analyst effectively lists all possible responses, which are manually exclusive.
  • Interviews: Data is collected from a rigid procedure, collecting the answers to a set of pre-conceived questions through personal interviews.
    • Two ways to conduct interviews:
      • Unstructured Interviews/Informal: The interviewer conducts question-answer sessions to acquire basic information.
      • Structured Interview/Formal: Has standard questions which interviewees need to respond to in either a closed (objective) or open (descriptive) format.
  • Secondary Data Sources or Archival Data: Data is collected through old records, magazines, company websites, etc.
    • Documents and records based data collection make use of existing data for gathering information.
  • Experimental Test: Conducted on the subject and the data is collected.
    • Data collection method where you as a researcher change some variables and observe their effect on other variables.
  • Observation: Way of gathering data by watching behavior, events, or noting physical characteristics in their natural setting.
    • Types of observation
      • Direct Observation: When you watch interactions, processes, or behavior as they occur.
      • Indirect Observation: Results of interactions, processes, or behavior.
    • Focus Group: This kind of research involves a group of individuals that provides feedback and answers to the open-ended questions asked to them.
      • Can be seen as a combination of interviews, surveys, and observations.
      • Gathers collective opinions rather than individual ones.
• Internet-Based Data Collection
  • Forms: Similar to surveys, try to gather data through a set of questions. Unlike surveys, forms can be more general.
    • Forms are used to gather qualitative data from a subject or group of subjects, particularly their demographic data or contact details.
  • Transactional Tracking: Data collection technique that relies on one's purchases to derive data, from which each purchase made by a customer, researchers, and sellers can access it from their websites using a THIRD-PARTY SERVICE PROVIDER or from their E-COMMERCE IN-STORE POINT OF SALE SYSTEM.
  • Social Media Monitoring: Similar to transactional tracking; instead of a customer's transaction history, this kind of technique focuses on tracking one's social media history and footprint.
    • Many platforms and businesses use this to track one's engagement with different posts online to better understand what products and services they would be interested in.
  • Online Tracking: Unlike the others, it is more general and can be done on sites through the use of cookies.
  • Online Marketing Analytics: You will be able to collect valuable information from the marketing campaigns run through web pages, social media, emails, etc.
    • The tool can tell you who clicked on your ad, how many times it was clicked, and from what device it was clicked.

Data Collection Tools

• Online Survey Platforms: (SurveyMonkey, Google Forms, Microsoft Forms, JotForms, PaperForm, FormSite, or Qualtrics) Provides user-friendly interfaces for creating and distributing surveys.
  • These platforms offer skip logic, response validation, and result analysis features.
• Mobile Data Collection Apps: (Fulcrum, iFormBuilder, or Open Data Kit (ODK)) Enables users to collect data using smartphones or tablets.
  • These apps allow offline data collection, GPS tagging, photo attachments, and real-time syncing with cloud-based storage.
• Data Management Software: Microsoft Excel, Google Sheets, or Statistical Package for the Social Sciences (SPSS) assist in organizing, cleaning, analyzing, and visualizing collected data.
  • These provide functionalities for data entry, formula calculations, statistical analysis, and creating charts and graphs.
• Data Visualization Tools: (Tableau, Power BI, or Google Data Studio) Helps present data in interactive visual formats.
  • These tools facilitate the exploration of patterns and trends in the collected data through charts, maps, dashboards, and infographics.

Importance of Data Collection

• Gather relevant information for analysis.
• Ensuring accuracy in data collection helps generate reliable insights, conclusions, and recommendations.
• Strengthens the trust in the information collected and increases confidence in the results.
• Eliminates the need for rework or corrections, ensuring that resources are utilized effectively.

Steps in Data Collection Process

• Step 1: Define objectives and research questions
• Step 2: Design data collection instruments
• Step 3: Pilot testing
• Step 4: Data collection
• Step 5: Data entry and cleaning
• Step 6: Data analysis
• Step 7: Reporting and dissemination

Issues Related to Data Collection

• Bias: Occurs when there is a systematic deviation from the true value or representation of the data.
• Non-Response Bias: Occurs when individuals chosen for data collection do not respond or participate fully.
• Measurement Error: Refers to inaccuracies in collecting or recording data due to human error, equipment malfunction, or ambiguous measurement criteria.
• Data Tampering: Involves intentionally manipulating or altering data to achieve desired outcomes or mislead others.

Common Challenges in Data Collection

• Access to participants
• Sample size and representativeness
• Data quality control
• Time and resource constraints
• Inconsistent data
• Data downtime
• Duplicate data
• Hidden data