What is Data Analytics and Why it Matters?

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History and Evolution of Data Analytics: The concept of ‘Big Data’ has been around for decades. Many organizations now understand that, if they capture all the data sets that streams into their businesses, they could apply analytics to get significant insights and value from the data. Even in the 1950s, decades before anyone even uttered the term “Big Data,” the businesses were using analytics – especially numbers in an excel sheet which were analyzed manually to gain insights and trends. The companies used this information for future decisions. Whereas, today, the business can identify insights for immediate action as the new benefits which the big data analytics brings are efficiency and speed. The ability to work faster and be agile gives the organizations a competitive edge in the market, which they did not have previously.

Big Data is everywhere and is not a fad; in fact, it is growing rapidly at a phenomenal pace. We are just at the beginning of a revolution which will touch every business and life on this planet. The fact is that, if the companies don’t take it seriously and think they can choose to ignore the concept of big data, then they are sure to be run over by the steamroller called the ‘Big Data.’ Let us understand why, according to Forbes market research?

Data is the next oil and water. Data volumes are exploding. By 2020, 1.7 megabytes of new information will be created every sec for each human on the planet. Therefore, our accumulated digital universe of data will grow from 4.4 zettabytes to 44 trillion gigabytes or 44 zettabytes. Today, Facebook users send an average of 31.25 million messages and view 2.77 million videos every minute. We are witnessing a massive growth in the photo and video data where up to 300 hours of video per minute are uploaded to YouTube alone.

By 2020, there will be over 6.1 billion smartphone users globally, and within by 2025, there will be over 50 billion smart connected devices in the world, all built to collect, analyze, and share data. A third of data will pass through the cloud infrastructure connected over the internet. Distributed computing where computing tasks will be performed using the networks of computers in the cloud is going to be real. Healthcare industry could save as much as $300 billion a year by better integrating the big data. However, at the moment, less than 0.5% of all the data is ever being used or analyzed; an enormous potential and opportunity for the businesses to take advantage for becoming the game-changers in the world of digital transformation and innovation.

What is the difference between a Data Science vs Big Data vs Data Analytics?

a. Data Science: Data Science is a field which comprises everything relating to data cleansing, preparation, and analyzing of both structured and unstructured data. It is a combination of mathematics, statistics, programming, problem-solving, and capturing data in ingenious ways. Data Science is the ability to look at things differently, and the activity of aligning the data using algorithms to make recommendations for capturing whitespace market and retention of the existing customer base. It is used in industries such as – Internet searches, Digital advertisements, and Search recommendations.

The application of Data Science is widely used in the Internet search engines for making use of the data science algorithms to deliver the best results for the search queries within seconds.

b. Big Data: Big Data refers to enormous velocity and volumes of data which cannot be processed effectively with the traditional applications. The processing of Big Data starts with the raw data, both structured and unstructured that is not aggregated and is most often impossible to store in the memory of a single computer. Big Data inundates a business due to its volume and size and can be used to analyze insights that will lead to better decision-making and for strategic business moves. Big data is used in industries such as – FSI, Retail and Communication.

An example of big data application in the Financial Services – Retail banks, Credit card companies, insurance firms, venture funds, private wealth management advisories, and institutional investment banks use big data for their financial services. The common problem among them all is the massive amounts of multi-structured data based in multiple disparate systems which can be solved by big data. Therefore, big data is used in several ways, such as compliance, customer, fraud, and operational analytics.

c. Data Analytics: The word analytics came into existence from Greece towards the end of the 16th century in the form of ‘Anlytikos’ which means involving Analysis. Let us then understand the difference between ‘Analysis and Analytics’ – Analytics refers to always having an element of data associated with it and Analysis has to do with the procedures and more to do with the adherence of the processes. Therefore, Analytics is the science of using data to build models that lead to better decision-making that in turn, add value to individuals, organizations, and institutions.

It involves the application of algorithms to derive insights into a specific dataset, e.g., running through several data sets looking for meaningful correlations between each other. Data analytics is used in industries such as Healthcare, Travel, Gaming, Energy Management, etc. enabling organizations to make better decisions as well as verify and disprove existing theories or business models. The focus of data analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

Application of Data Analytics in Healthcare – The key challenge for hospitals with cost pressure is to treat as many patients as efficiently possible, keeping in mind the improvements in the quality of care. Medical equipment and machine data are increasingly being used to track as well as optimize patient flow, treatment, and equipment usage in hospitals. It is estimated that there will be 1% efficiency gain which could yield more than $60 billion approx savings in the global healthcare industry.

According to Gartner, – “Analytics will drive major innovation and disrupt the established business models in the coming years. Technical professionals need to adapt their data and analytics architecture from end to end, to meet the demand for analytics everywhere. Also, there will be a shortage of talent required for organizations to take advantage of Big Data.” It is clearly highlighted in Harvard Business Review that the sexiest job of the 21st century is ‘Data Analytics.’

The Data Analytics process has a few key components that are required for any initiative. By combining the components, a successful data analytics initiative will deliver a clear picture of current, past, and future trends in your business. This process generally, begins with descriptive analytics. This is the process of describing historic patterns in data. Descriptive analytics aims at answering questions such as “What happened?” Which involves measuring of traditional indicators such as ROI (Return on Investment). The indicators used will be industry-specific. Descriptive analytics doesn’t make predictions or directly inform decision. Its focus is on summarizing data in a meaningful and descriptive way.

The next important part of data analytics is ‘Advanced Analytics.’ This aspect of data science takes advantage of advanced tools in the extraction of data, discover trends, and make predictions. These tools include machine learning and classical statistics — machine learning technologies such as Natural language processing, Neural Networks, advanced analytics, and sentiment analysis. This information provides new insights from the data. Advanced analytics answers “What if?” questions.

The availability of machine learning techniques, cheap computing power, and massive data sets, has enabled the use of these techniques in many industries.

The collection of big data sets is instrumental in allowing these techniques of data analytics. Big data analytics has enabled the businesses to draw meaningful conclusions from varied and complex data sources, which has made possible by advances in cheap computational power and parallel processing.

What are the different types of Data Analytics?

Descriptive Analytics: Describes what has already occurred. It helps the business to understand how things are going and answer the question about what happened. This is past data and is reactive. These techniques summarize large datasets to describe the outcomes to the stakeholders. By building the KPIs (Key Performance Indicators), the strategies can help track and measure success or failure. Metrics like ROI are used in many industry verticals. Specific parameters are developed to monitor performance within particular industries. This process requires – a) collections of relevant datasets, b)processing of data, c) data analysis, and d) data visualization. It provides essential insights into past performances. Diagnostic Analytics: Analytics that helps in answering questions about why things happened? These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics by digging deeper to find the cause through root-cause Analysis. The performance indicators are further investigated to discover why they got better or worse. It is done in three steps: Identification of anomalies in the data. There may be unexpected changes in a metric or a particular market. Data that is related to these anomalies are collected. Statistical techniques are used to find relationships and trends that explain these anomalies. Predictive Analytics: Predictive Analytics will help us answer what will probably happen in the future as a result of something that has already happened. They help the business forecast, future behaviour and outcomes. It is proactive. They use historical data to identify and determine if an event is likely to recur. Predictive Analytical tools provide valuable insights into what might happen, and its techniques include a variety of machine learning and statistical methods such as – Decision trees, Regression, and Neural Networks. Prescriptive Analytics: Prescriptive Analytics helps the business prescribe the right course of action. They not only tell us what probably might happen but also what should be done if it happens by taking preventive action. It is proactive. By using insights from predictive analytics, the data-driven decision can be made. This enables the businesses to make informed decisions in the event of uncertainties. Prescriptive analytics techniques rely on strategies based on machine learning which can find patterns in large datasets. By analyzing past events and decisions, the likelihood of different outcomes could be estimated for taking preventive measures.

Why is Data Analytics Important to the Business?

One of the earliest adopters of Data Analytics technology is the Financial Services Sector. Data Analytics play a huge role in the Financial and Banking industries for accessing risk and predicting market trends. Credit ratings are one of the examples of data analytics which affects everyone. These ratings use many data points to determine the lending risk. It is also used to prevent and detect fraud, reduce risk and improve efficiency for Financial Institutions.

The use of Data Analytics goes beyond ROI and maximizing profits. The use of data analytics in healthcare (health informatics) is already widespread. Predicting patients results and taking preventive measures, improving diagnostic techniques, are a few examples of how data analytics is revolutionizing the healthcare industry. Besides, the pharmaceutical industry is also being transformed by machine learning. Drug discovery is a complex task with many variables. Machine learning can significantly improve drug discovery. Pharmaceutical companies also use data analytics to understand the market for drugs and predict their sales.

The Internet of Things (IoT) is another big field which is exploding alongside machine learning through connected devices. These devices enable a huge potential for data analytics. IoT devices contain sensors that collect meaningful data for their operation. Devices like the ‘Smart Light’ will help efficient traffic and energy management. Smart devices like this can use data to learn from the incidents and predict the traffic patterns. This will enable advanced automation and enable seamless traffic management and prevent accidents due to high-density traffic during peak hours.

To summarise, the Data Analytics provides insights which the businesses need for efficient and effective decision-making. Data Analytics used in combination, they provide a well-informed understanding of a company’s needs and opportunities. The application of data analytics is quite broad. Analysis of Big Data can optimize efficiency in different industry verticals. Performance improvement enables businesses to be successful in an ever-growing competitive world and has found success in many fields. Environmental protection and crime prevention are some of the applications of data analytics. The applications for Data Analytics now seem endless; more and more data is being collected every single day, which presents new opportunities for its application and the betterment of society and the world around.

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