Purpose of Data Analytics – A Comprehensive Guide With Examples

The power of data has been understood by businesses and organizations, and the purpose of leveraging data to yield significant results has already been put to use. With tons of raw data available, the Purpose of Data Analytics to unlock its potential has been judiciously used by all sectors. In this article, we highlight the core advantages of data, and we bring you a list of different fields where Data Analytics has helped enhance and rationalize different business processes.

Introduction to Data Analytic

To understand the Purpose of Data Analytics, let’s first understand the core, of Data Analytics.

What is Data Analytics?

The process of acquiring, storing, organizing, and analyzing the data to identify patterns, prepare and analyze trends, present solutions, and monitor the results embody Data Analytics. The acquired data can be real-time, historical, structured, and raw data. It goes much further beyond the traditional method of manually monitoring the key performing indicators.

How Did Data Analytics Come Into Existence?

Data Analysis has been in existence since ancient times. The first recorded historical evidence was the record-keeping methods by the Egyptians. Late in the 1800’s the foundation of the new age Data Analytics was set by Fredrick Winslow Taylor, who initiated the first time management exercises.

Herman Hollerith invented the first “Tabulating Machine” for the US Government in the 1890s to shorten the time consumed for the census. In 1970, Edgar Codd invented a relational database that was the foundation for SQL, the programming language. Soon in the 1980s data warehousing was developed. Data Mining was introduced in the year 1990. The year 1991  was iconic and revolutionary, as Tim Burner Lee introduced the world to the internet. In 1994, Yahoo was created by  David  and

FiloJerry Yang. Another iconic giant, Google Search was created in 1998 by Sergey Brin and Larry Page. 

The year 2000 was when the golden period of the internet and data started. The technology of cloud-based applications was developed. In 2005 Hadoop was developed by Doug Cutting and Mike Caferella.

Since the 1990s the world of data has progressed by leaps and bounds.

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How Has Data Become Significant in the Digital World?

Data is cogent on its own. Data allows businesses an insight into their strategies and processes. Data is the fuel essential for information metamorphosis leading to optimal processes and innovations.

The size of the Data that was generated in 1986 was 2.6 Exabytes, and in 2000 it was 54.5 Exabytes. The digital world is changing at lightning speed with the advent of Internet of Things(IoT) devices. In 2020 64.5 zettabytes of data was generated, which is predicted to grow to 180 zettabytes per minute. The COVID-19 pandemic has changed the digital world by increasing data generation to approximately 5000%. 

Decision Making and Business Intelligence

The Purpose of Data Analytics integration in a company is essentially focused on effective decision-making. Business Intelligence aids businesses in growth in all sectors. How is it possible?

How Does Data Analytics Facilitate Effective Decision-Making?

In the past, the decision-making was based on human expertise and experience. This was like a gamble, that worked in some cases, and in some took ages to incorporate and caused massive losses to the business. The Data source in the present scenario is rich with information and diverse. The technology of Data Analytics facilitates acquiring this data in real time. The analytics insights give a clearer picture and help the board members make decisions in real time which can be acted upon immediately. This has transformed the entire business culture by being more forward-thinking in decision-making and taking multiple steps towards innovations. It has also made it easier to collaborate with clients and local members and solve issues in real-time.

How has Data Analytics Enhanced Business Intelligence?

Data Analytics was earlier adopted to analyze customer data, but now it has become an indispensable part of the decision-making process. Business Intelligence thus, is the collection of the historical data and the current data of the company and analysis of it with the help of different Data Analytics tools to give it a meaningful structure. This real-time analysis helps in gaining appropriate insight into all the processes. It helps in increasing the workflow and helps the business achieve its benchmarks. 

How Can Data Analytics Leverage Strategic Business Growth?

A company that amalgamates Data Analytics in their processes, builds a strong foundation for accurate analysis. This analysis in turn leads to an efficient organisation and strong management. Data integration of different departments and systems in the company helps in better interdepartmental collaborations and effectual sharing of information. 

Data Analytics is a strategic asset for the company as it helps in customizing marketing campaigns based on customer segmentation analysis. Supply chain analytics help in identifying cost-saving opportunities, and predictive analysis steers the company to indulge in innovations in a bid to stay on top in the competitive market.

Improving Operational Adequacy

The Purpose of Data Analytics is to offer robust tools to improve the operational efficiency of a company.  With the help of Data Analytics, a company can carry out efficient resource allocation. A company can eliminate issues of overstaffing and reduce unnecessary expenditure and underutilization of manpower, and also avoid understaffing which directly impacts the quality of customer service. Once the inefficiencies in processes are identified, a company is in a better position to streamline processes, like supply chain, and procurement. Accurate analysis through Data Analytics helps in improved forecasting and optimization of processes in real time. The end goal is to improve service delivery to customers. 

How Has P&G employed Data Analytics for Operational Adequacy?

P&G, the leader in the FMCG market, has started an initiative known as “Smart Basket”. In this, they have integrated predictive analytics, advanced Machine Learning, and Artificial Intelligence to collect the data of each store’s product preference, and the store’s customers’ product preference. This helps in stocking the fast-moving goods only, and reduction of non-moving stock. In this manner, the company optimizes its stock inventory and reduces unnecessary expenditure on non-moving stocks. They have introduced geospatial analytics for higher and faster distribution at optimal costs. The Purpose of Data Analytics in the company is to enhance the customer experience and optimize operational and distribution costs.

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Enhancing Customer Experience

A loyal customer boosts the company sales by 60-70%, and the Purpose of Data Analytics integrated with the customer service department aims for the same. Customer experience, customer journey, customer retention, and customer engagement analytics give insight into the preferences, needs, and behaviour of the customer. This in turn helps in finding out the inefficiencies in the entire customer delight journey. A company based on the analysis can train their customer service teams to be more proactive, increase the response time, and be well prepared for forecasted challenges. 

Social media analytics help the company to identify effective channels for advertising and report in real-time the effectiveness of the marketing campaigns. The marketing campaigns can be made more personalized, and relevant and resonate better with the targeted customers. This entire effort not only aims at acquiring new customers but also increases customer retention and engagement. 

How Does Walmart Stay Ahead in the World as a Global Leader?

Walmart is the largest retailer and the largest company in terms of revenue generation globally. It collects 2.5 petabytes of unstructured customer data every hour. The analysis covers millions of products and millions of different sources. This entire activity has helped boost online sales by 10-15% with a $ 1 billion net increment in revenue. Data Analytics has enabled the company to provide the best e-commerce technology logistic efficiency, thus, providing a world-class shopping experience to the customers in-store and online. 

With the Purpose of Data Analytics integration for enhancing customer retention, they acquired Inkiru Incorporation, a data analytics-based company. Inkiru helps Walmart in creating target marketing campaigns, effective merchandising analytics, and personalization of shopping experiences for customers.

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Risk Management and Predictive Analysis

Companies and government bodies have integrated predictive analysis to help conduct risk management. The Purpose of Data Analytics is to help in reactive risk management, proactive risk management, and predictive risk management. Predictive analytics for risk management uses data mining, artificial intelligence, machine learning, deep learning algorithms, and data modeling techniques to identify risks over a vast number of areas. 

Financial risk analytics extends its services to banks, retail, software development firms, supply chain, manufacturing, and insurance. The risk analytics predicts risk mitigating measures against employee blunders, fire, earthquakes, and cyber attacks. Risk Analytics helps in identifying irregularities and deviations from compliance standards in a company, that can cause heavy losses and legal lawsuits. Operation analytics helps in analyzing the overall impact of risks on the business and steps to ensure a cushion against the forecasted risks.

Government agencies use risk analytics to analyze future risks and devise measures to protect the population by taking timely actions.

How Did American Express Amalgamate Data Analytics to Prevent Fraud?

American Express uses Enhanced Authorization (EA) which is a fraud detection analytical tool. Every time a credit card is swiped, the information about the credit card no, purchase amount, type of merchandise, IP address, email ID, and shipping address is captured and analyzed for future possibilities of fraud. 

American Express at present employs more than 800 Data Scientists.

How Does the Government Use Risk Analytics?

In Korea, the government has a Bid Rigging Analysis System. This system analyzes the pattern of the bidding done for government projects. In this manner, they catch any rigging being done.

The OECD Risk Analytics has been put into service with the whole Airport Group of the City of Mexico. They conduct reactive risk analytics after any disaster, proactive risk analytics to avoid any current risks, and predictive analysis for preventive measures like fires, earthquakes, floods, and any other natural disasters.  

Healthcare and Medical Research

The Healthcare Analytics industry was 2020 valued at $23.51 billion and is expected to grow to $96.90 billion by 2030. The Purpose of Data Analytics in healthcare is multifold. Data Analytics has enabled doctors and hospitals to store the medical history, age, and the possible implications of treatment with particular types of medications. This helps tailor the treatment as per the patient and reduces the element of error to a very high extent. In hospitals, data analytics has helped manage resources efficiently. It has helped in forecasting operating theatre demand, making it easier for critical patients to find one at a crucial time. It helps in managing the staff, especially during predicted rush hours. It helps manage the supply chain of medicines and resources in the hospital. Healthcare analytics for a patient helps in preventing the 30-day readmission period, thus helping in reducing hospital cost and insurance costs. 

A Few Examples of Healthcare Data Analytics Help in Hospitals

In 2021 96% of US nonfederal acute care hospitals implemented the Electronic Health Record (EHR) where the data of more than 145 million patients were recorded.

Blue Cross Blue Shield used data analytics to identify 742 risk factors that would lead to opioid drug abuse. This initiative was taken up by the US Government to reduce the number of accidental deaths and road accidents, which was a very high number.

John Hopkins, one of the leading hospitals in the US has 14 IT centres, that record an average of 500 messages per minute. Their data analytics system built by GE Healthcare Partners has helped in a faster rate of assignment of operating rooms to emergency patients, lower delays in transferring patients to other treatment centres, and timely discharge with minimum hassle for the patients.

The second Purpose of Data Analytics has found importance in the field of discovery in the field of science. Discovery Analytics has succeeded in aiding the discovery of new drugs, new disorders, and diseases, and in finding out new patterns of treatments without experimenting on another specimen. It has helped the government bodies predict the pattern of infection and better population health management.

Landmark Drug Discovery and Development With the Aid of Data Analytics

Hong Kong-based biotech company, InSilico, used artificial intelligence to develop the drug INS018-0556. This helps in the effective treatment of idiopathic pulmonary fibrosis, a form of cancer.

With the help of advanced data analytics and artificial intelligence, gene sequencing has been developed which helps medical researchers analyze the different components of proteins. This has also helped in understanding the impact of drug research.

Social Impact and Data Analytics

The Purpose of Data Analytics in the domain of social impact analysis is to understand the impact of any initiative, program, and implementation of policy on society and the economy. Social impact analytics is used in social programs like education programs, employment outcomes, new or reformed policy, crime rate analysis, and health improvement programs.

In the healthcare domain, analytics helps in finding out the impact of any healthcare initiatives implemented, predicting the incidence of any outbreak of diseases or infection, and the access to patient care. With the assistance of demographic data analysis, NGOs and government organizations analyze the needs, preferences, and challenges faced while rolling out the programs. This helps the agencies to measure the outreach of their program and devise more effective strategies. 

In the area of environmental conservation projects, it helps in the analysis of the impact of the projects on the environment and local communities. 

A Few Examples of Social Impact Analytics Application in Real Life

Barcelona government has rolled out an initiative known as the Barcelona Smart City Project. In this initiative with the application of data analytics the information regarding traffic and routes to avoid hold-ups and jams, the level of energy usage, the noise level, and the water consumption patterns are analyzed. 

NASA’s Earth Science Division uses advanced analytics to study climate patterns and track deforestation, forest fires, and any other natural disaster signs. The data is given in real time making it easy for the government and NDRF to plan effective strategies.

Election Prediction Analytics gained precedence since 2000. With the help of data analytics campaigners get a better insight into the voter’s preferences, opinions, and reactions to the offers made by the candidates. The marketing campaigns whether online or offline, the campaign goals, and the election plans are analyzed in real time and changed as per the requirement. Companies like the Pew Research Centre and 538 Agency are some well-known election prediction companies using data analytics.

Future of Data Analytics

Data Analytics has revolutionized the world. With the advent of machine learning, artificial intelligence, and advanced complex algorithms, data has opened avenues that were not even fathomed about earlier. With many developments on the horizon, the scope of Data Analytics is vast. In the future, the Purpose of Data Analytics will aim to reduce manual efforts with the help of automation and DataOps. Automating tasks will lead to lower error margins and higher efficiency in the data quality, leading to more accurate analysis.

At present, the use of data analytics is gaining clients, yet due to complex handling and cost, not many users jump on the bandwagon. The Purpose of Data Analytics developers is now develop no-code and low-code tools, to ease the application process and cost, so that more and more companies in the future adopt data analytics.

With the phenomenal increase in the Internet of Things(IoT) datasets are getting larger and more complex, and at the same time a treasure trove of vast rich information. Machine Learning and Artificial Intelligence will be able to discover such minute trends that can alter patterns and decisions in the future. 

Internet of Things(IoT), APIs, and the Internet are being developed further to assist easy integration and exchange of data between different systems, which is the core competency of a business. Data as a Solution (DAAS) is being developed to effectively analyze complex and large databases and easily share the information with the company stakeholders and clients. Not only are the tools cost-effective but they are being aimed at assisting the company in the easy sharing of information with the clients and stakeholders.

Data mesh is being developed to assist the teams in being accountable for their data, handling it appropriately, and avoiding bottlenecks. This will in turn increase data dexterity. The development of the data marketplace will be able to identify gaps in the data collected and fill in the gaps in such a manner that will augment the value of the data.

Conclusion

In conclusion, Data analytics has revolutionized the world by being the catalyst to promote better innovations, better strategies, and better services. With the help of data analytics companies have been able to improvise in many operational areas optimising their revenue, cutting unnecessary costs, and in turn boosting the economy. Many government agencies have been able to optimize the reach and good effect of their programs, which in turn promise better planning, structuring, and implementation of urban and rural developments. The environment protection agencies have been able to identify risks cropping up now which could catapult into major threats to civilization in the future. The most important Purpose of Data Analytics in the healthcare domain is facilitating the development and discovery of new products to treat patients along with better service in the healthcare centres.

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FAQs

Q. What is the Purpose of Data Analytics?

The purpose of analytics is to make intelligent insights from the data generated, to put into actionable use. In the industry domain, the purpose is to gain insights from data, to be able to make accurate decisions. In the healthcare division, it is to assist in the discovery of diseases and the development of new products to improve the lifespan of humans.

Q. How does data analytics impact the government?

Data Analytics helps the government in making important analyses and decisions regarding their healthcare programs. Based on analysis, they can find out about the reach and impact of the upliftment programs. They can find out about any catastrophes concerning natural disasters or epidemics caused by diseases and implement relief actions beforehand to reduce the risk of the impact.

Q. What is the scope of data analytics?

The scope of data analytics is quite vast. The development of advanced analysis, machine learning, and advanced artificial intelligence is helping shape the future of advanced technologies in all fields.