What is Data? Types, How to Manage Data

What Is Data?

Data refers to statistics, individual facts, or any specific item of information, that can be numeric or could be collected through observations. From a technical point of view, data refers to a set of values that are of qualitative or quantitative variables. This can be about one or more persons or objects.

What is Information?

In a general sense, Information simply refers to data that is processed, organised, and structured. On the basis of the information, data gets a context that helps in decision making. For example, a sale made to a single customer at a restaurant is considered to be data. In the same context, when a business is able to identify the most popular or least popular dish, it becomes information.

What is Knowledge?

Knowledge is the proper assembly of meaningful information whose intent is to be valuable. It is a deterministic process. Knowledge can be of two types:

• Obtaining and memorising the facts
• Using the information to crack problems

Knowledge signifies a design that links and usually provides a high-level view and likelihood of what will happen next or what is described

For example, if the humidity levels are high and the temperature drips considerably, the atmosphere is pretty much unlikely to hold the moisture and the humidity, hence, it rains. The pattern is reached on the basis of comparing the valid points emanating from data and information resulting into knowledge or sometimes also referred to as wisdom.

What is Wisdom?

Wisdom is systematic and allows you to comprehend the interaction taking place among temperature gradients, raining, evaporation and air currents.

How Are Data, Information, and Knowledge Linked?

Data refers to information that is basic, unrefined, and generally unfiltered. Information on the other hand is information that comprises much more refined data. Information makes use of data that has evolved to the point of being useful for some form of analysis. Knowledge on the other hand refers to something that resides in us, this is brought to use only when human experience and insight is applied to data and information.

Imagine that you are running a business. You are into selling crockery items and you need to find out the item that is your best seller in each category. Find out the Data points associated to this and the information that you will use to reach to a conclusion.

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Types of Data

Data that comes from multiple sources—such as databases, Enterprise Resource Planning (ERP) systems, weblogs, chat history, and GPS maps—varies in its format. However, different formats of data need to be made consistent and clear to be used for analysis. Data acquired from various sources can be categorised primarily into the following types of sources:

• Internal sources, such as organizational or enterprise data
• External sources, such as social data

Structured Data

A set of data that complies with a data model that is pre-defined in nature and is simple and straightforward to analyse is known as structured data. Structured data will be in a tabular format and there will be a defined relationship between different rows and columns. Excel files or SQL databases are some of the common examples of structured data.

Unstructured Data

Unstructured data is a set of data that might or might not have any logical or repeating patterns. Unstructured data:

• Consists typically of metadata, i.e., the additional information related to data
  
  
• Comprises inconsistent data, such as data obtained from files, social media websites, satellites, etc.
  
  
• Consists of data in different formats such as e-mails, text, audio, video, or images Some sources of unstructured data include:
  
  
  • Text both internal and external to an organization: Documents, logs, survey results, feedbacks, and e-mails from both within and across the organization
    
    
  • Social media: Data obtained from social networking platforms, including YouTube, Facebook, Twitter, LinkedIn, and Flickr
    
    
  • Mobile data: Data such as text messages and location information

Unstructured systems typically have little or no predetermined form and provide users with a wide scope to structure data according to their choice. Unstructured data is generally deployed to:

• Gain considerable competitive advantage by organisations
• Gain a clear, complete, and big picture of future prospects by organisations

Semi-Structured Data

Semi-structured data, also known as schema-less or self-describing structure, refers to a form of structured data that contains tags or markup elements in order to separate semantic elements and generate hierarchies of records and fields in the given data.

To be organised, semi-structured data should be fed electronically from database systems, file systems, and through data exchange formats including scientific data and XML (eXtensible Markup Language). XML enables data to have an elaborate and intricate structure that is significantly richer and comparatively complex. Some sources for semi-structured data include:

• Database systems
• File systems like Web data and bibliographic data
• Data exchange formats like scientific data

Quantitative Data

The data that expresses a certain quantity, amount, or range is known as Quantitative data. In this kind of data type, there is usually measurement units that are associated with, e.g. meters, in the case of the height of a person.

Qualitative Data

Qualitative data refers to data that involves descriptive and conceptual findings that may be collected through questionnaires, interviews, or observation. By carefully analysing qualitative data, one will be able to explore ideas and will be able to further explain quantitative results.

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How to Manage Data?

An information system is based on the discovery of hidden patterns in data, which is a valuable resource, in order to explore information that is required for successful decision-making in an organisation. The organisation constantly upgrades itself with the aid of this data in order to stay competitive and plan for its future growth. Organisations use data to not only determine their objectives but also to guarantee that they are met.

One of the most important components of any information system is data. Data can be gathered from both internal and external sources. Users may make better decisions with the aid of data. As a result, data acquisition, gathering, and preservation are critical to an organisation’s success.

Data administration is keeping track of data resource requirements and procuring them. Because data is such an important asset to an organisation, data managers must keep track of its availability and storage. Data administration may also be used to check for data abuse. A data administrator’s responsibilities include:

• Data generation is being monitored.
• Keeping track on how data is being used
• In terms of data acquisition and usage, we adhere to industry norms.
• Data warehouse and storage maintenance
• Consistent data quality

Database

Database refers to an organised collection of data that is stored and accessed electronically from a computer system. Some of the more complicated databases are developed by making use of formal design and modeling techniques.

Database Management System (DBMS)

A software package that has been designed to define, manipulate, retrieve and manage data in a database is known as a Database Management System (DBMS). One of the main works of a DBMS is to manipulates the data itself. It can manipulate the format of the data, field names, structure of the record, and file structure. Such software also defines the rules that will help in validating and manipulating the data.

Challenges with DBMS in Managing Huge Data

Some disadvantages of DBMS in managing huge data are as follows:

• Complexity: DBMS is complex software and the end users must be aware of the complete functionality of the DBMS to use it properly. The problem of bad design decisions may be entertained when there is any flaw in the understanding of the DBMS.
  
  
• Size: DBMS is large software that occupies a large amount of disk space. To work effectively, it needs large amounts of memory.
  
  
• Slow performance: DBMS sustains less performance than a traditional file processing system as the traditional file processing system is written for a specific purpose, whereas DBMS is written for general purpose.
  
  
• Cost: The cost of DBMS is more than a traditional file processing system. The cost of DBMS usually depends on the functionality offered by the DBMS.