Big Data Analytics
Big data analytics is the complex process of examining and processing large amount and varied amount of data sets or big data to reveal the truth by examining hidden patterns, unknown correlations, fluctuations in market trends, customer choices and preferences. This information can help business organizations to plan according to the customers choice and make important business decisions. You can continue to read What is the difference between big data and big data analytics?
Big data analysis continuously processes huge data and extract information that traditional applications lack to do. Big data analysis consist of all three types of data- structured, unstructured and semi-structured data. This analysis highlights the current market scenario, customer preferences and other patterns necessary for business growth and management.
Big data analysis helps to analyse the data and draw an important conclusion using an application of data science and algorithms. Big data analytics in the form of advanced analytics which uses advance algorithms of predictive analytics, statistical algorithms and what-if-analysis.
Big data Analytics serves as analytics professionals such as data scientist and data analysts the ability to analyse Big data from multiple and varied sources. These sources include structured, unstructured and transactional data sources.
Big data analytics provides an efficient path for the success and growth of the business organisation. Big data analytics have several benefits such as effective marketing campaigns, the discovery of new product, make effective operation, and operating management.
Big data is a field that deals with systematically extracting the information from the large and complex, structured and unstructured data which are difficult to solve using normal data processing software.
Big data include capturing data, data storage, data analysis, search, sharing, transfer, visualisation, querying, updating, information privacy and data sources. Big data is generally associated with three main V’s- volume, variety and velocity.
When there are large structured and unstructured data, we are primarily concerned with observing and tracking those data. Thus, Big data includes data with sizes which are greater to be handled by using normal software.
In the current scenario, Big data uses predictive analytics, user behaviour analytics and other typical analytics methods to extract information from data. This analysis of data can find a new development in medicine, science, technology and so on.
Scientists, business executives, analyst, practitioners of medicine, advertising companies, and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics.
Big data usually includes data sets whose are beyond the ability of the normal software to manage, capture and process the data. Big data processes structured, unstructured and semi-structured data but however, its primary focus is related to unstructured data.
Big data requires an of technology and techniques with a new form of unity to disclose data -sets that are complex and massive in size. Some people describe Big data is present everywhere where parallel computing tools are needed to handle data. Big data uses volume, variety and velocity to analyse the data.
Difference between Big Data and Big Data Analytics:
Big data is the collection of unstructured and semi-structured data which require lots of advanced technology to gather important information. Big data analytics has the collection of data which are sorted in a proper manner and hence this reduces the complexity and reduces the time for processing data.
Data analytics has a proper goal in mind while sorting these data in such a way that it can collect lots of information. On the other hand, big data is the collection of huge piles of data that requires advanced algorithms and faster processing to conclude some useful information.
Big data employs advance technology such s Machine learning, Artificial Intelligence and Data mining to handle big data whereas, Big data analytics requires simple tools such as statistical modelling and mathematical algorithms to simplify the problems.
You can suppose big data as a library while Big data analytics is like a particular book that chooses and focuses on the right thing.
Big Data Analytics:
- Meaning– Huge volume of data which cannot be handle by using traditional programming and characterised by 3V.
- Concept– Diverse data types generated from multiple sources and it include all types and format of data.
- Basis of formation– Internet users, Electronic devices, sensors, Audio/Video streaming, data generated in various organisations.
- Application– Financial services, Telecommunication, Research and development, Security and law, government organisation, Health and sports.
- Approach– To develop business ideas, gain competitions.
To mark sustainability and understand new demands of customers
- Meaning– Collection of huge data that is used to gain useful outcomes for various purpose.
- Concept– A scientific and specialised area which focus on Artificial Intelligence, Machine Learning, Data Mining etc to extract information and support business organisation.
- Basis of formation– Capture complex pattern from various data sources, applies scientific method to extract knowledge from data sources.
- Applications– Internet searches, Digital advertisement, search optimisation, Fraud and risk assessment, web development.
- Approach– Extensive use of mathematics, statistical and other advance technology to extract information.
Programming skills like NoSQL, SQL etc for data acquisition.