What is Data Analytics? | Data Analytics Definition, use cases or applications
This basic guide covers data analytics definition, data analytics use cases or data analytics applications. It also mentions link to merits and demerits of data analytics.
Introduction: As we know huge amount of data are generated and stored in the large databases due to immense use of internet enabled devices and applications across the world. Moreover machines and sensors generate data faster than humans. It is difficult to analyse these complex and large data sets with the help of normal database management tools and traditional data processing applications and softwares. Data Analytics tools are developed to address these high volume, high velocity and/or high variety of information assets.
Data Analytics Definition
The process of analyzing data sets after performing extraction, profiling, cleansing, deduping operations in order to derive useful informations or conclusions with the help of hardware/software is known as data analytics. The following figure-1 depicts the data analytics block diagram.
The companies which use data analytics tools perform better. It helps to increase revenue, decrease costs and increase productivity of the companies. As shown data analytics consists of data sourcing, data analysis and data representations.
• The data are sourced from various sources which include both open and close data sets.
• It include both personal, official and public data from wide variety of sources.
• It uses scientific data from astronomy, Genomics, Biological research.
• It uses data from web such as log files, web indexing, text and documents.
• It uses official data from business transactions.
• It uses data from various sensors such as temperature, humidity, proximity for weather forecasting, surveillance etc.
• It uses personal data from social networks (facebook, twitter, google plus), call records from telecom service providers and medical records from hospitals.
• It uses photos or images and videos.
• Purchase transaction records from various online websites.
• GPS signals from various cell phone users.
Data analysis include following functional modules.
Data extraction: The process of extracting and storing the data from data sources mentioned above is known as data extraction.
Data Profiling: The process of examining and collecting informative summary in the form of smaller database from the larger one is known as data profiling.
Data Cleansing: The process of converting sourced data from errors, duplicates and inconsistencies into cleaned target data is known as data cleansing or data cleaning.
Data Deduping: The process of replacing multiple copies of data into single instance storage in order to save storage space/bandwidth is known as data deduping or data deduplication.
The data are represented in different textual and graphical forms for evaluation and use by the companies to examine various parameters.
Data Analytics Use Cases | Data Analytics Applications
Following are the use cases or applications of data analytics.
➨It is used by banking, finance and insurance firms to evaluate bahaviour of the people.
➨It is used for online gaming businesses to increase revenue by advertising based on online historical data with the help of machine learning.
➨It is used by security agencies and military for surveillance.
➨It is used by retail businesses to understand purchase behaviour and perform marketing accordingly.
➨It is used by telecom companies to analyze call records and customer databases.
➨It is used for scientific data analysis by many companies based on numerous sensors placed at different locations as per requirements.
➨It is used by social media companies such as facebook, google, twitter to display advertisements as per user behaviour and historic data.
Refer Advantages and Disadvantages of data analytics >> for more information on why data analytics is important and what are the challenges involved in adopting it.