What is big data analytics?
What is Big Data Why is it important
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, fin-tech, urban informatics, and business informatics.
John Mashey used the word Big data for the first time in the year 1990. 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 a set 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.
There is a big misconception between Big data and Business Intelligence (BI). Big data uses mathematical optimisation, statistics and concept of non-linear system identification to conclude laws (correlations, regressions and non-linear relationship effects) from large sets of data which contain low information to reveal relationship or to predict the behaviour and outcomes. Business Intelligence (BI) uses mathematical tools and descriptive statistics with data of high information to measure and calculate outcomes, detect trends and forecast the future of the organisation.
Big data is characterised by the following entities:
- Volume- The quantity of generated and stored data. It is the size of data that tells whether the data is fit to be classified as big data or not.
- Variety- The type and nature of data. Variety helps to analyse easily whether it can provide some useful information or not. Big data observe the text, images, audio, video, and also it completes the missing or unrevealed data.
- Velocity- The speed at which data is generated and processed to meet the challenges and demands that fall under the growth and development of any organisation. Big data is often available in real-time. Two main types of velocity which concerns Big data is the frequency of generation and frequency of handling, recording and publishing.
- Veracity- It refers to data quality and data value. The data quality of captured data can fluctuate rapidly and affect the accuracy of the accurate analysis. data must be processed with advanced tools using analytics and algorithms to reveal useful information. Information generation algorithms must detect and address invisible issues such as system failure, machine up-gradation etc.
- Relational- If the collected data contains common fields that would be able to make data analysis and conjoining of different data sets.
- Scalability- If the size of the data can be expanded and processed easily.
- Value- The information that can be extracted from the data.
- Variability- It refers to data whose value or other characteristics are shifting in relation to the context they are being generated.
Why is Big Data important
The importance of Big Data relies on how much data do you have and what do you do with it. You can access data from any source and analyse it to find answers which help you to
- Cost reduction
- Time reduction
- Smart decision making
- New product development
When you combine Big Data with analytics, you can gain much useful application at ground level such as:
- Determine the root cause of failures, issues and defects in real-time.
- Generate powerful marketing strategies for customers buying habits.
- Recalculating and re-evaluating the entire portfolio risk in a few minutes.
- Detecting the frauds before it affects your company.
How Big Data works:
Before using Big Data to work in any organisation, let us understand how it flows among different locations, sources, systems, owners and users. There are five important keys of Big data which includes traditional data, structured data, unstructured data and semi-structured data.
- Set a big data strategy.
- Identify the big data sources.
- Access, manage and store the data.
- Analyse the data.
- Make data-driven decisions.
Set a big data strategy
At large business organisation, big data strategy is the plan to help you oversee and improve the way you acquire, store, manage and use data for any purpose within the organisation. A big data strategy sets a stage for business success with that abundant data. when developing a strategy, its necessary to consider the existing and future goal and target of the organisation. This makes a wonderful strategy for using big data like any other valuable business asset rather than just a pile of applications.
Identify the big data sources
Today, everything is available online. Similarly, streaming data comes from the Internet of connection (IoT) and other connected electronic devices such as a smartwatch, medical devices, smart cars etc. you can analyse the big data when it arrives at you and decides whether to keep it or to discard it before making further analysis.
Social media such as Facebook, YouTube, Twitter, Instagram etc includes a bulk of big data in the form of images, audio, video, texts etc. These data are very useful for marketing, sales and operational purposes. These data are available in the semi-structured form or unstructured form.
Publicly available data comes from open sources like government data or from various online portals. Other big data includes cloud data sources, data lakes and customers.
Access, Manage and Store big data
Modern computers provide speed and power needed to quickly access the huge amount of data. Along with reliable access, companies also need methods for collecting and integrating the data, data governance and storage and preparing data for analytics purpose. Some data must be stored in traditional warehouse-like cloud solutions and Hadoop.
Analyse big data
High power computing and high-performance computer can easily access and choose which data is useful for analyses. However, big data analytics in today’s world means how companies are utilising the information for better use from the data.
Make data-driven decisions
Well, managed and trusted data leads to trusted decisions. To stay in the competition in today’s world, businesses need to utilise and gain the full value of big data and operate in a better way based on the evidence presented by big data. The benefits of utilising this data are crystal clear, which helps business to perform better and operate in profitable and in a more predictable way.
Application of Big data
Big data has increased the demands of information in the last few years. It was predicted that in the year 2010, the industry worth more than $100 billion and was growing at an unexpected rate; about twice as fast as a software business as a whole.
- Government– The use and adaptation of big data within government agencies processes allows efficiencies in terms of productivity and research innovation. Data analytics often requires several parts of the government department to work together and develop new methods to deliver faster and innovative results.
- Manufacturing– According to recent global studies conducted by TCS, improvement in supply planning and product quality provides the real benefits of big data in the manufacturing sector. Big data provides a platform for transparency in the manufacturing industry which shows the availability and growth in performances. Predictive manufacturing uses a vast amount of data and advanced tools and technologies for processing the systematic process of data into useful information. This concept of predictive manufacturing begins with data acquisition where different types of data are available in the unstructured format of data such as sound, pressure, vibrations, current etc. Thus, this generated big data acts as an input of predictive tools and preventive strategies in Health Management.
- International development– Various researches on Information and Communication technologies suggests that big data can make an important contribution to international development. Recent advancements of big data offer different opportunities in the field of health care, cyber, security, crimes, economic productivity, natural disaster etc. Additionally, user-generated data offers new discoveries which are lacking behind due to some reasons. However, there are still some problems which are yet to be covered by big data such as privacy issues, imperfect methodology and interoperability issues.
- Health Care- Big data has served healthcare at its best by providing standard medicinal terms and prescriptive analytics, clinical risk intervention and predictive analytics, waste reductions, internal and external reporting of patients, standard medical terms and point to point medical solutions. The level of data generated in the health care system is not enough. The adoption of mHealth, eHealth and wearable technologies, the volume of data is expected to increase. These data include electronic health care record data, patient-generated data, sensor data and other different forms of data which are difficult to process. Big data in health research is continuously striving to increase its results in the field of biomedical research, as data-driven analytics is moving forward than hypothesis-driven research. This trend generated by big data can be tested in clinical researches and traditional follow-up biological researches.
- Media- According to Nick Couldry and Joseph Turow, Media and Advertising companies approach big data as an important and actionable point of information about millions and millions of individuals. The media industry is growing at an unexpected rate from the traditional approach such as newspaper, magazines, or television into the customer with technology which targets people location to location. The main aim of media is to display a strong message or strong content that can be clearly visible to the customers. Using data mining, strong messages (advertisements) and content (articles) can be displayed to the customers. Big data captured data, target the customer (by advertising in the market) and provide data journalism which provides unique and innovative insights and infographics.
- Information Technology– Big data has help business operations as a tool to help the business employee to wok more rapidly and efficiently, collecting and distributing the resources in IT. By applying the principles of big data and machine learning and deep learning, IT departments can easily trace potential issues and move to provide a more accurate solution before the problem arise. At this time, Information Technology Operation Analytics has begun to play a major role in system management by providing data silos and insights from the whole of the system rather than isolated packets of data.
- Insurance– Health insurance providers are collecting data on different useful topics on social Determinants of health such as food and TV consumption, clothing size, purchasing habits where they can predict the cost and revenue, in order to spot health issues of their clients. It is difficult to say whether this information is being used for pricing purpose or not!
- Education– Various programs, online and offline, are being conducted to train the people in order to meet the shortage and demands of Data analyst and data scientist. Private boot camps have already begun their free programs like The Data Incubators or paid programs like General Assembly.