Why Data Analytics is Important: Why You Should Learn It

In today’s scenario, a glimpse of the importance of Data Analytics can be unraveled from the fact that in 2023 the market share of Data Analytics estimated at $38.67 billion, is predicted to grow at a CAGR of 30.4% to value approximately $ 393.35 billion by 2032. Data Analytics has made managing and processing big and complex datasets help the business world and the scientific arena. Enhancing the capacity of data-driven decisions and a catalyst for innovations are a few of the benefits along with other credible advantages, that we will have a look at in this article. So, without further ado, let’s delve into why data analytics is important and why you should learn it. 

Brief History of Data Analytics

Before we embark on the endeavor of explaining the  Importance of data analytics, and the need to learn it, let’s take a look at the milestones in the journey of the development of Data Analytics:

• Ancient Times: The root of Data Analytics is statistics, developed in ancient Egypt to keep a census of the Pyramids.
• 1700s-1800s: Bayer’s Theorem ( proposed in 1700) and Regression Analysis formulated in the 1800s, later became the base for the development of Data Mining.
• 1880- The US conducted a census, which took 8 years to analyze and generate the findings.
• 1890- Herman Hollerith invented the “Tabulating Machine” which helped the next US census to be completed in 18 months.
• 1959: Artur Samuel the pioneer of Artificial Intelligence coined the term “Machine Learning”. At this time he was working as a well-known programmer with IBM.
• 1962-John Turkey first coined and described the term” Data Analysis”.
• 1969: Advanced Research Projects Agency Network(ARPANET) created the first TCI/IP protocol. They were the world’s first wide area network.
• 1970- Edgar F. Codd invented the relational database.
• 1980: William H. Inmon proposed the term “ data Warehouse”, where the data was stored with time stamps.
• 1985: C.F Jeff Wu from the Chinese Academy of Science first used the term “ data Science”
• 1989: Gregory Piatetsky Shapiro introduced in his paper the term “ known discovery in database”, which became known as Data Mining” in the business circle.
• 1990: Tim Berner Lee and Robert Cailleau founded the “World Wide Web”, and introduced the computing world to HTML, URLs, and HTTP
• 1997: Google.com was registered.
• 1999: Salesforce company was the first to offer services for “cloud computing”.
• 2005: Roger Magoulas coined the term “ Big Data” for the first time. In the same year, Doug Cutting and Mike Cafarelle developed Hadoop and Apache. 
• 2006: Amazon Web Services introduces cloud computing infrastructure services.

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Reasons Why Data Analytics is so popular today

Post the 1990s with the introduction of the internet and computing technologies, the whole world has transposed to a whole new modus operandi with a surge in almost every field. The development of advanced technology and tools is emerging at lightning speed in all sectors. These developments have acted as supplements to increase the speed of innovations in areas that were not even thought about earlier. 

With the development of Data Analytics, the way businesses operate has taken a 360-degree turn. Gone are the days of slow trails and lengthy result delivery periods. 

In this section let’s have a look at how data can help businesses reach new capacities in particular areas.

Informed Decision making: 

Data Analytics has made a huge impact on the way businesses make decisions now. Gone are the days when intuition, guesswork, and relying on only expert experience were the norms for making decisions. Why Data Analytics is important in decision-making can be gathered from the following points:

• Data Analytics helps businesses make data-driven decisions based on assessable insights rather than intuitions.
• Prescriptive Analysis suggests to the company how to react to changes and events forecasted 
• Predictive Analytics forecasts the outcome of changes made, thus, informing the company whether the change will be favorable or not.
• With the help of data analytics modeling a company can visualize the results and determine the factors responsible for negative or positive impact. These changes can be in pricing, product offerings, and changes in sales patterns.
• Data Analytics feeds data-backed information to your decision-making process and validates the course of action to be favorable or unfavorable before one puts that decision into practice.
• Informed decision-making with the help of Data Analytics goes through 6 steps before finally delivering the result, these are:

• Goal orientation
• Extensive Market Research
• Data Collection
• Data Analysis
• Developing models and algorithms to get insight
• Final Decision Making

Example 

• Starbucks: In 2008 Starbucks had to close several locations due to losses. Due to this ex-CEO Howard Schultz changed his decision to incorporate location analytics, local team opinions, and data analytics to decide on profitable locations.

• Google: Google uses people analytics and employee reviews to make decisions regarding employee welfare and the enhancement of low-performing employees. It is counted as one of the best places to work.

• DBS Singapore Bank: DBS Bank holds the no. 1 position in the Top ASEAN Banks, because of the exclusive personalized customer services they provide. They invested heavily in Artificial intelligence and data analytics to offer intelligent banking and financial solutions to their customers based on their investment portfolios and patterns.

Competitive Advantage

A business gains a competitive advantage in the industry depending on its product quality, pricing, and services. The elements that directly affect competitive advantage are the cost structure, product pricing, product quality, marketing impact, product availability and logistics, and customer service. The stream of analytics that concentrates on competitive advantage is known as Business Analytics. Business Analytics analyzes how to optimize the workflow, expansion analysis, and quality control.

In the current industry scenario, all the leading companies have gained considerably after the integration of Business Analytics, here are a few examples of how these giants turned the tables:

• Coco Cola: Cocoa Cola the beverage giant has 35 million Twitter followers and on Facebook, around 105 million fans follow it. Coco-Cola uses AI-based picture technology to identify pictures that have the beverage featured, despite the user not being a follower. Through this analysis, the company identifies the users, their demographics, preferences, and other useful details to customize and personalize target advertisements.

• Tesla: Tesla which is the world’s most valuable automotive brand uses Business Analytics to connect their cars to the owners wirelessly. The analysis carried out gives timely updates to the owners regarding upgrade requirements, any problems to occur soon, traffic situations while driving, and any imminent road risks. This has ensured high levels of customer satisfaction levels.  

• Amazon: Amazon, a trillion-dollar world leader in the online retail sector uses Business Intelligence to track and analyze their customer behaviour through their purchase habits, abandoned cart products, most purchased products, and most browsed sections. Based on this analysis, they keep sending timely offers and suggestions customized as per the customer’s preferences. The company’s annual revenue share of 35% constitutes impulse buyers.

Enhanced Efficiency and Productivity

Companies discovered the impact it had on their operation efficacies. Data Analysis is used in the manufacturing division to identify the processes and workflows that are productive from those that are wasting resources and bearing unnecessary costs to the company. Manufacturing Analytics help to keep a check on the accuracy of any work process and in real-time identify any stop gaps or compromises in quality. It helps in setting up KPIs and process metrics to increase productivity efficiency. It helps in the identification of the complexity of process flows and helps in taking timely actions.

Data analytics is important because it helps more collaboration, and transparency in the processes between departments. Such collaborations lead to reducing redundancy and duplication of processes. The positive aspect of analytics and departmental collaboration even leads to new ideas, better and faster innovations, and better risk management.

To highlight the advantage of employing Data Analytics in the manufacturing processes, here are some real-life cases:

• General Electrics: General Electric uses its assembly line Asset Performance Management(APM) analytics system for predictive maintenance, reduction in assembly line downtime, and forecasting of any unplanned emergency. SmartSignal another manufacturing analytics program uses data to forecast, diagnose, and prevent equipment failure suddenly. The company uses Proficy C Serve for Grid Analytics and Visual Intelligence to increase the proficiency of the grid and reduce emergency risks.

• Ford Motor Company: Ford Motors formed a Global Data Insights and Analytics in-house in 2015. It takes care of the manufacturing Analysis of the manufacturing and supply chain units. For example, it manufactures the outer panels of the F-150 pickup trucks and Transit Vehicles. The predictive analytics and visualization tools, the engineers keep track of the distribution of resources, and variations in the production workflow to ensure a strict check on the quality of the products. These predictive analytics called Miniterms 4.0 after its introduction in 2019 has managed to save the company € 1 million since its introduction in 2019.

• Procter & Gamble Pampers Diapers: Procter & Gamble is the world leader in FMCG products and it also incurred a considerable amount of losses due to defective production. The company used Microsoft IoT and edge analytics to innovate Hot Melt Optimization Analytics. This innovation earned it an award for Best IT Innovation in 2023.  This analysis uses data generated from the assembly line, Microsoft Predictive Analytics, and Azure Cloud to reduce the production of defective diapers. This has helped the company cut down 70% of its losses.  

Personalized Customer Experience

Customer experience can make or break a company. It is used by many leading industries, with small companies following suit. Data Analytics partnered with Artificial Intelligence provides insight into customer behavior to such depths it is not possible manually at all. Data is collected from various sources like web interactions, emails, mobile applications, Internet of Things (IoT) devices, customer service, and other sources. This data is analyzed for prevailing trends and patterns, and to forecast future events. 

The first step in offering personalized services to customers is customer segmentation. The data captured comprises their demographics, purchasing behavior, shopping history, and personal information along with the location. The groups are further segregated under micro level to facilitate ease of appropriate personalization. On this basis, the companies suggest products and services. They send promo codes and discount coupons depending on the occasion and purchase history. The marketing and advertisement campaigns are personalized to resonate well with the customer to impact directly the conversion rate. To emphasize more on the positive outcome of Customer Behavior Analytics here are a few examples of market leaders:

• Amazon: Amazon the world leader in online retail changed the mindset of people from shopping at brick and mortar stores to online shopping. Feedvisor’s 2021 report on Amazon’s Consumer Behavior carried out a survey and reported that 92% of consumers choose to be Amazon for online shopping. Amazon uses data analysis to capture a customer’s browsing pattern, shopping history, and product preference. Based on this information it suggests products resonating with the customer’s choices. In return, these suggestions help the customer save time on spending time browsing through different catalogs to search for their preferred products. As per reports, 56% of consumers visit Amazon daily, and 42% are impulse buyers.
• Starbucks: Starbucks sees 90 million transactions per week at its 25,000 outlets globally. The company started a Digital Flywheel program that offered rewards and promo codes to customers in return for their data. The program has 17 million registered users and 13 million active users to date. Based on the customer data, the customer is offered his/her preferred drink even when visiting a new outlet. The app also suggests products to try as per the customer’s preference. The app also assists in changing the menu as per the weather and season to enhance customer delight. Starbucks ventured into expansion by introducing its products in grocery stores based on the preferred choices of the customers around the grocery shops.
• Delta Airlines: Delta Airlines is one of the leading airlines globally due to its excellent customer satisfaction experience. Delta Sync which is a loyalty and reward program application is worth $1 billion. The data captured from the application helps the company send suggestions of deals and discounts on their preferred route and travel patterns. The customer data is provided to the in-flight attendees to help them offer personalized services to the flyers. The application also sends suggestions of the hotels and restaurants in the destination city, as per the customer’s preference. 

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Risk Mitigation and Fraud Detection

The biggest advantage due to which data analytics is considered extremely important is in its potential to prevent fraud and caution against acts of terrorism. Data Analytics paired with Artificial Intelligence can keep a check on money laundering, tax evasion, fraudulent insurance claims, terrorist financing, and cybersecurity threats. With the assistance of Data Analytics transactions can be monitored continuously, and the feedback received in real time. This helps in identifying crimes at the beginning threshold, enabling agencies to take preventive action faster. In the field of Income Tax predictive analysis helps with analysis of the reliability of the tax filed by an individual. Thus, saving the Government from paying money to ineligible candidates. In the medical arena, through data analytics, the Government can search for generic and cheaper products, in place of medicines with inflated prices. The following scandals came to light with the help of data analytics:

• Enron: Enron was a US-based company that dealt in energy, services, and commodities. In 2001 with the help of Data Analytics, the huge amount of bad debts that the company had omitted from the financial statement came to light.

• The Wells Fargo Scandal: Wells Fargo was founded in 1852 and is a US-based investment and services bank. In 2016, with the aid of Data Analytics, the bank was caught for creating millions of unauthorized duplicate accounts of its customers, due to unreasonably high sales pressure from the company. The employees had also issued loans and conducted other financial activities without the customer’s knowledge.

• The Theranos Scandal: In 2014, Theranos was founded by Elizabeth Holmes as a revolutionary blood testing laboratory with advanced testing technology. However, in 2016 the investigating agencies with the help of data analytics uncovered the fraudulent activities, and the false reports which ran in millions, run by Elizabeth Holmes. Investigators with the assistance of data analytics compared all the test results from Theranos with standard test results and found lots of incorrect reports with incorrect life-threatening disease diagnoses.

Scientific Breakthroughs

In the field of science and healthcare, we have seen why data analytics has gained such importance. In the scientific domain, there are vast amounts of data which is generated, especially during experiments at great speed. The collection, handling, and processing of such data received at incredible velocity is only possible with data analysis. It is manually impossible for scientists to handle such complex datasets and make predictions. This is only possible with the help of advanced data analytics and machine learning. Even minute trends and variables that may turn significant are possible to be discovered with the assistance of data analytics

Hypothesis testing is easier with data analysis to arrive at the reports at a faster time than manual processes. It also eliminates the task of sifting through huge amounts of data to find potential matching candidates to expedite the process of testing. With data analysis, the necessity of researching animals and other species gets eliminated.

Data Analytics plays a significant role in the healthcare industry too. The vast amount of data generated by patient records, helps doctors detect ailments faster and accurately. The doctors also can prescribe medicines with higher efficiencies. Hospitals can share the availability and treatment data with other healthcare and rescue agencies, which leads to faster and better care, especially for critical patients.

To emphasize further the real-life application of data science in the scientific and healthcare domain, here are a few examples:

•  The Human Genome Project: The Human Genome Project which started in 1990 and the result was published in 2004, is counted among the major scientific breakthroughs of the century. The project was conducted with biologists, mathematicians, data analysts, computer scientists, and engineers. Vast amounts of complex data on the genome sequence were sourced to decipher the genome sequence. This helped in major scientific breakthroughs in the fields of microbiology, virology, cancer, and other infectious diseases. 
• Keppler Space Telescope: NASA’s Keppler Space Telescope and machine learning helped in the discovery of Kepler-90i. Kepler-90i is a sun-like star, that is 2545 light years away from the Earth. It was discovered with the help of machine learning from Google.
• Keytruda(Pembrolizumab): Keytruda increases the body’s immunity to detect and fight cancer cells in the lungs, melanoma, head and neck, and cervical cancer. It was developed by Merc, which has the healthcare industry’s largest immuno-oncology clinical research program.

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Conclusion

In conclusion, since the innovation of data analysis, the importance of Data Analytics has been observed in every field. It has revolutionized the entire world by leading the path to not only intelligent decision-making but many scientific and medical breakthroughs. The application of data analysis to convert raw data into meaningful and actionable insights has led to a reduction in the turnaround time and the adoption of succeeding steps at a faster rate. Not only has the industrial sector been able to operate efficiently, but groundbreaking discoveries in the field of science have been made possible at a faster rate. The biggest advantage if data analytics has been seen in the field of healthcare, where patient care can be provided in a tailored manner and at the right time, saving many lives. With the dawn of the data-driven era and the Internet of Things(IoT), the world is set to see revolutionary and unparalleled progress in all sectors. 

FAQs

Q. What is the importance of data analytics in the current scenario?

Data gathered from the Internet of Things(IoT) devices and other sources has been converted from raw complex sets to meaningful sets enabling a company to make actionable decisions. Data Analytics also helps the scientific industry in conducting complex research and discoveries, and in the healthcare industry saving lives by providing doctors and healthcare professionals with crucial information at the right time.

Q. How does Data Analytics help the industrial domain?

With The assistance of data analytics, companies can get an insight into their work processes, productivity and manufacturing trends, logistics data, customer behavior, manpower efficiency, compliance regulation, and detection of fraud and security threats.

Q. How is data analytics important in the scientific field?

Data Analytics paired with machine learning and artificial intelligence has made it possible for scientists to analyze big and complex data sets for the most insignificant trends and patterns, that have resulted in groundbreaking discoveries and innovations. The trial processes have shortened considerably leading to discoveries much faster.