Business Analytics - Types of Data

Quiz

Data in Business Analytics

Data is the heart and soul of business analytics. It can be defined as information that is collected, processed, and analysed to obtain data insights and make informed decisions. This data can be collected from different sources, including social media platforms, web sources like websites and webpages, financial transactions, and other channels running on the internet.

Data Quality

Data quality is an important factor for business analytics. High-quality data is always fruitful to find accurate results and relevant to the questions being asked while low-quality data can lead to inaccurate conclusions and poor decision-making.

Majorly, Data in analytics can be divided into three types: structured data, unstructured data, and semi-structured data. The data which has a predefined structure is called structured data; structured data includes the data stored in a database and the data which is well-organized in a spreadsheet. Unstructured data does not have any predefined structure, such as content on social media or consumer reviews. It is more difficult to organize and analyse. Semi-structured data is partially structured such as emails or weblogs.

Types of Data

Some common categories of Data are as follows −

1. Structured Data

Structured data is a type of data that is highly organized and easily searchable using simple algorithms; it is easily stored and managed using traditional data management tools like spreadsheets, SQL databases, or tables. In other words, we can say that the structured data is organized into rows and columns and then can easily be searchable as and when required. Structured data is often quantitative and numeric which may include numbers, percentages, and related data. Structured data is comparatively simple to analyse using statistical techniques such as regression analysis, correlation analysis etc.

Examples of Structured Data

Some of the most relevant examples of Structured Data are as follows −

Spreadsheets − Data organized in Excel sheets in tabular form or the form of rows and columns.
Google Sheets − Google Sheets are organised data on the cloud.
Relational Databases − As its name suggests relational databases store data in tabular form. Some common examples of relational databases are MySQL, PostgreSQL, and Oracle.
Data Warehouses − Large-scale storage of structured data for analysis and reporting purposes, such as Amazon Redshift or Google BigQuery.
CSV (Comma-separated values) Files − In CSV files, each line represents a row and each field within the line is separated by a comma.

Characteristics of Structured Data

Some of the common characteristics of Structured Data are as follows −

Follows a predefined Schema − It follows a predefined schema or predesigned format that defines the data types, relationships, and limitations.
Organised in Tabular Form − Structured data is typically arranged in rows and columns. For example a spreadsheet, table or database. The data is well organized using data definition, format, and meaning of the data. The data lives in fixed fields inside a record or a file.
Consistent Data Types − In a tabular form of data, each column has a structured data table which includes specific data types, such as integer, string, or date.
Easily Searchable − Structured data may be efficiently searched, queried, and modified using SQL.
Grouped − Similar entities are grouped to form relations or classes. The data is simple to access and query. As a result, other programs can easily use it.

Advantages of Structured Data

Some of the key advantages of Structured Data are as follows −

Easy to store and access Structured data has a predefined structure which makes it easy to understand, store and access data.
Efficiency Structured data can easily be stored, retrieved, processed, and managed using traditional and advanced database management systems efficiently.
Accuracy Data constraints and validation are imposed on structured data to maintain its integrity and accuracy.
Scalability Well-suited for large-scale data storage and complex queries.
Interoperability It can be integrated and used with a variety of business intelligence and reporting solutions.
Requires less storage space: It requires less storage space to store data.

Disadvantages of structured data

A predefined structure of data is limited to use.
It has limited storage functions.
Difficult to change or update; this results in massive expenditure of resources and time.

Tools for working with structured data

Structured data is well-defined and ordered; it is suitable for different tools to analyse. Structured data has been used since a long time back; therefore, well-designed and tested tools are available to store, process and access structured data. These programs range from database management systems to analytics and business intelligence tools and assist teams in effectively utilizing data.

Some of the most common tools for managing structured data are −

MySQL − Embedding data in mass-deployed software.
OLAP (Online Analytical Processing) − Data analysis.
SQLite − Relational database.
PostgreSQL − Supports SQL and JSON for querying and programming languages like C/C+, Java, and Python.
Oracle database − Advanced database management system.

2. Unstructured Data

Unstructured data is data which doesn’t contain structure, predefined format or schema to store the data. Unstructured data is very challenging to store, and process using traditional relational databases or RDBMS. Unstructured data includes business documents, email messages, videos, images, webpages, and audio files.

It is often qualitative, i.e. descriptive and narrative. Customer credit reports, insurance claims, and airline ticket complaints are some of the key examples of unstructured textual data that have commercial implications.

Unstructured data can be analysed using advanced analytics techniques such as natural language processing (NLP) for sentiment analysis.

Examples of Unstructured Data

Unstructured data is qualitative rather than quantitative, which means that it is more based on its characteristics and categorical.

Emails
Social media posts
Audio and video files
Sensor data
Memos
Documents (PDFs, Word files)
Webpages
Images (JPEG, GIF, PNG, etc.)

Characteristics of Unstructured Data

Some of the key characteristics of unstructured data are as follows −

No specific Data model − Unstructured data doesn't have any specific data model; which means it doesn't have a specific format and structure to store data.
Volume − Volume refers to the size of data; modern datasets come in bigger sizes which means they have a large volume of data.
Variety − Unstructured data includes different forms of data like text, multimedia, etc.
Doesn't have semantics − Unstructured data doesn’t have specific rules and regulations.
Complexity − Hard to manage and analyse with traditional data tools.
Storage − Typically stored in data lakes or NoSQL databases.

Advantages of Unstructured Data

Some of the key advantages of Unstructured Data are as follows −

Rich Source of Information − Unstructured data I a rich source of information. It contains in-depth information, capturing nuances and context that structured data misses.
Variety of information − Unstructured data contains a variety of information.
Provides comprehensive insights − Unstructured data provides comprehensive insights into customer sentiments, behaviours, and preferences.
Flexible Diverse Sources − The flexibility of unstructured data allows it to include a wide range of data formats, such as text, images, and videos.
More Detailed Information − Unstructured data can contain more precise and granular information, including nuances, feelings, and specific details that may be lost in structured data.
Real-time Data − Can be generated and analyzed in real-time
Deeper Analysis using AI/ML − AI/ML is used to analyse unstructured data.

Disadvantages of Unstructured Data

Some of the key disadvantages of Unstructured Data are as follows −

No standard structure − Unstructured Data doesn’t have a predefined structure to store, process and access the data.
Inconsistent in format and content − Data from different sources may be inconsistent in format and content, complicating analysis efforts.
Complexity in Analysis − due to the lack of structure; it has complexity to analyse data. It uses complex algorithms to process the data.
Performance Issues − Querying and retrieving specific information can be slower.
Noise and irrelevant information − It may contain noise and irrelevant information which may increase challenges to ensure data quality and consistency.

Tools for working with Unstructured Data

NoSQL Databases − MongoDB, Cassandra.
Data Lakes − Amazon S3, Azure Data Lake.
Big Data Platforms − Hadoop, Spark.
Machine Learning and AI − TensorFlow, and PyTorch for processing and analyzing data.
Text Mining Tools − Apache Lucene, NLTK.

3. Semi-Structured Data

Semi-structured data combines features of both structured and unstructured data. This sort of data includes information that is partially ordered but not enough to be categorized as structured data. Semi-structured data includes XML and JSON files, which are organized and also contain unstructured data elements. Semi-structured data is often analysed using a combination of traditional data management tools and sophisticated analytics techniques.

Semi-structured data applies to a variety of applications where some degree of organization is desirable but stringent schema requirements are not necessary. Hence, it falls between structured and unstructured data.

Examples of Semi-structured Data

Some common examples of Semi-structured Data are as follows −

XML (eXtensible Markup Language) Files
JSON (JavaScript Object Notation) Files
Email
HTML (Hypertext Markup Language) Documents
Log Files
NoSQL Databases
Sensor Data

Characteristics of Semi-structured Data

Some common characteristics of Semi-structured Data are as follows −

Partially structured − Semi-structured data is partially structured; it means it’s a combination of structured and unstructured data.
Flexible Schema − Semi-structured data doesn’t have any specific structure. Hence, it doesn’t conform to a data model.
Self-describing Nature − Data often contains metadata or tags that describe its structure and significance. XML and JSON are some examples.
Easier Data Integration − The flexible schema makes it easy to combine semi-structured data from different sources.
Supports Complex Data Types − It supports complex data types like arrays and objects.

Advantages of Semi-structured Data

Some of the common advantages of Semi-structured Data are as follows −

Flexibility − Semi-structured data may contain different data types and formats.
Flexible schema for Data Integration − The flexible schema of semi-structured data allows its users to integrate data collected from different sources.
Scalability − Semi-structured data has features to store data in a scalable manner.
Interoperability − It includes files like JSON, XML, and YAML.
Complex Data Types − Semi-structured data can handle arrays, objects, and other complex data types, enabling the representation of rich, multi-dimensional data.
Storage Efficient − Semi-structured data can be more storage-efficient.

Disadvantages of Semi-structured data

Some of the common disadvantages of Semi-structured Data are as follows −

Partial structured − It contains partial structure data which can be a bit difficult to store and process.
Inconsistencies in the data − The lack of a strict schema can result in inconsistencies in the data.
Complexity in Data Management − Managing semi-structured data can be complex due to the lack of a fixed schema.
Performance Issues − Querying and processing semi-structured data may be less efficient than structured data.
Limited Tool Support − There are limited tools available for managing and analysing semi-structured data,

Tools for working with Semi-structured data

Working with semi-structured data requires some specialized tools and techniques. Some of the most commonly used tools to work on Semi-structured data are as follows −

NoSQL Databases − NoSQL Databases like MongoDB, Couchbase, and Cassandra.
Data Lakes − Data lakes are capable of working with large volumes of data. Amazon S3, Azure Data Lake, and Google Cloud Storage
Apache Spark − It is an open-source application that works as a unified analytics engine for large-scale data for semi-structured types.
Altova XMLSpy − It is a tool for modelling, editing, transforming, and debugging XML-related technologies.
Natural Language Processing (NLP) − The Natural Language Toolkit is a library to process human language data.

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