Data, Information, and Knowledge

Data: Raw facts, such as employee number, total hours worked, inventory part number, or number of units produced on a production line.

Information: Organized and processed data that provides additional value. For example, sales data summarized to show total sales for the month, sales by salesperson, or sales by product line. Providing information to customers can lead to increased revenues and profits, as seen with Kaboodle, a social shopping website that facilitates information sharing among shoppers and sellers.

Knowledge: Awareness and understanding of information and its potential uses. Information becomes more useful when it is applied to specific tasks or decisions. For instance, effective shopping requires understanding the needs and desires of the intended recipients of the items being purchased.

Data Processing: A simple three-step process consisting of:

Collecting data
Organizing data
Analyzing data

The Value of Information: The value of information is directly linked to its ability to help decision-makers achieve their organization's goals. Valuable information enhances efficiency and effectiveness. Many businesses mistakenly assume reports are based on accurate data; however, studies show a significant portion of customer contact data is often incorrect, which can lead to substantial financial losses.

For example, organizations with 100,000 customers and a 22% error rate might lose approximately $2.2 million due to inaccurate data. Studies indicate that 84% of CEOs are concerned about the quality of the data used in their decision-making processes.

Benefits Gained Through Use of High-Quality Data: High-quality data is essential for sound decision-making and is a valuable asset for any organization. Poor data quality can result in erroneous decisions. Key benefits include:

Improve Decision-Making: Data drives major decisions across various sectors, removing guesswork and minimizing risks.
Increase Customer Satisfaction: High-quality data allows for personalized customer experiences, while poor data can lead to dissatisfaction through errors.
Increase Sales: Accurate consumer targeting, effective communication, and informed sales strategies rely on high-quality data.
Improve Innovation: Quality data supports process improvement initiatives aimed at enhancing efficiency and customer satisfaction.
Raise Productivity: Employees can focus on core tasks rather than data errors, leading to greater efficiency.
Ensure Compliance: Adherence to regulations such as GDPR and HIPAA necessitates accurate data to avoid legal repercussions.

Characteristics of Quality Information: Quality information must be:

Accessible: Easily reachable by authorized users in the appropriate format and timing.
Accurate: Free from errors; inaccuracies arise from erroneous data input (garbage in, garbage out).
Complete: Contains all necessary facts for informed decision-making.
Economical: Cost-effective to produce, balancing value and production costs.
Relevant: Pertains directly to the decision-maker's needs.
Reliable: Trustworthy based on data collection methods and sources.
Secure: Protected from unauthorized access.
Timely: Delivered when necessary.
Verifiable: Can be validated through multiple sources.

Data Hierarchy:

Entity: A person, place, or thing for which data is collected (e.g., employees, products, customers).
File: A collection of similar entities.
Attribute: Characteristics of an entity (e.g., employee number, last name, first name, hire date, department number).
Domain: The range of allowable values for a data attribute (e.g., employee type limited to F - full-time, P - part-time, or C - contractor). Defining domains can enhance data accuracy.

Note