We all have heard this often – “Data is the new oil.” In today’s business world, data has become the most crucial aspect. Data is everywhere and it is also a part of our daily lives and it is growing exponentially. The creation and consumption of data are growing at an unprecedented pace, owing to smartphones, digital networks, and Wi-Fi.
Do we ever think about how much data we create per day? According to an estimate, 1.15 trillion MB of data is created every day by human beings. Humans created ~2.5 quintillion data bytes daily in 2020.
As a result, various technologies, processes, and systems have been developed to process, transform, analyze, and store data in this data-driven world. Big Data, Data Science, and Data Analytics are technologies widely used for managing such humongous data being produced globally.
Any huge and complicated collection of data is called “Big Data”. The process of obtaining useful information from data is known as “Data Analytics”. “Data Science” is an interdisciplinary field that seeks to uncover new information.
Each of these technologies complements the others but can also be utilized independently. Let us deep-dive into each of these technologies.
1️⃣ Data Science
Data Science is a field that deals with both unstructured and structured data and encompasses everything related to data purification, processing, and analysis.
Statistics, mathematics, programming, problem-solving, data capture in novel methods, the ability to see things in new ways, and the process of cleansing, preparing, and aligning data are all part of data science. This umbrella phrase refers to various strategies for collecting insights and information from data.
Data scientists use exploratory analysis to uncover new information from the data. They also employ various powerful machine learning techniques to predict the presence of a future event. This entails looking for hidden patterns, unseen correlations, market trends, and other pertinent business data.
Applications of Data Science
Search engines are the most useful application of Data Science in search engines. We all know that when we need to find something on the internet, we use search engines like Google, Yahoo, Safari, Firefox, and others. As a result, Data Science is employed to speed up Searches.
For example, if we search for “Best walking shoes” on the search engine, we will be directed to links that will be either an advertisement or the most frequently used information on running shoes. As a result of this analysis, we can obtain the Topmost Visited Web Links.
In transport – Data Science has also made inroads into the realm of transportation, such as with self-driving cars. It is simple to lower the number of accidents using driverless vehicles.
For example, with driverless cars, the training data is fed into the algorithm. The data is examined using Data Science techniques to determine the speed limit on the highway, busy streets, and narrow roads, among other things. And how to deal with various scenarios while driving, among other things.
In finance – In the financial industry, data science is crucial. The financial sector has long had a problem with fraud and the possibility of losing money. As a result, Financial Industries must automate risk of loss analysis to make strategic decisions for the organization. Financial Industries also employ Data Science Analytics techniques to forecast the future. It enables businesses to forecast client lifetime value and stock market movements.
Data Science, for example, is a critical component of the stock market. Data Science is utilized in the stock market to evaluate past behavior using historical data to predict future outcomes. Data is assessed so that future stock values can be indicated over a defined timeframe.
In eCommerce – Ecommerce Data Science is used by websites such as Amazon, Flipkart, and others to improve the user experience by providing customized recommendations.
For example, when we search for something on an e-commerce website, we are given suggestions based on our previous purchases and recommendations based on the number of people who have bought the product, rated it, searched for it, and so on. All of this is possible thanks to data science.
Healthcare – Data science is a boon in the healthcare industry. Data science applications are Tumour detection, Drug development, Image analysis in medicine, Medical bots in the virtual world, Genetics and genomics, Predictive modeling for diagnosis, and other applications.
Image recognition – Data Science is now being used in image recognition. When we submit a photo with a buddy to Facebook, Facebook suggests tagging who is in the photo. Machine learning and data science are used to do this. When an image is recognized, Facebook performs data analysis on one’s Facebook friends, and if the faces in the photo match with someone else’s profile, Facebook proposes auto-tagging.
2️⃣ Big Data
The term “big data” refers to massive data sets. It’s the buzzword to define an immense amount of data. They have outperformed traditional data management systems because of their scale and the complexity and changing nature of large data sets. As a result, data warehouses and data lakes have emerged as the go-to solutions for dealing with large amounts of data, vastly outperforming traditional databases.
Based on requirements, prominent data specialists explain the structure and behavior of a big data solution and how it may be provided utilizing big data technologies such as Hadoop, Spark, and Kafka. It’s used to analyze insights, which leads to better decision-making and strategic business activities.
Types of Big Data
Structured Data – Data that is organized. Structured data refers to any data set that follows a predefined structure. These structured data sets are easier to process than other data kinds because users can precisely identify the data’s structure. A distributed Relational Database Management System (RDBMS), which stores data in organized table structures, is an excellent example of structured data.
Semi-structured Data – Although this form of data does not adhere to a precise structure, it does have some observable structure, such as groupings or ordered hierarchies. Markup languages (XML), web pages, emails, and other forms of semi-structured data are examples.
Unstructured Data – Data that does not correspond to a schema or a predetermined structure is classified as this type of data. When dealing with extensive data, the most prevalent sort of data—text, photos, video, and audio all fall under this category.
Applications of Big Data
Financial services – Big data is used by credit card companies, retail banks, private wealth management advisories, insurance companies, venture capital firms, and institutional investment banks. The vast amounts of multi-structured data live in various separate systems, which big data can solve, is a shared challenge among them all. As a result, big data is employed in a variety of ways, including customer, compliance, fraud, and operational analytics.
Communications – Telecommunication service companies’ primary priorities include gaining new subscribers, retaining customers, and increasing their current subscriber bases. Combining and evaluating the massive amounts of customer-produced and Machine-derived data generated every day holds the key to solving these problems.
Retail – Whether you’re a brick-and-mortar store or an online retailer, the key to staying competitive is to understand your customers better. This necessitates the capacity to examine all of the different data sources that businesses deal with on a daily basis, such as weblogs, consumer transaction data, social media, store-branded credit card data, and loyalty program data.
3️⃣ Data Analytics
Applying an algorithmic or mechanical approach to draw insights and sifting through numerous data sets to hunt for relevant correlations is what data analytics is all about. It’s used in several industries to help businesses and data analytics firms make better judgments and verify and refute existing hypotheses and models. Inference, which is the process of concluding based on what the researcher already knows, emphasizes data analytics.
Numbers are translated into plain English by data analysts. Data is collected by every organization, such as sales numbers, market research, logistics, and transportation expenses. A data analyst’s role is to take such information and use it to assist businesses in making better decisions. The Data Analyst Course will guide you through the process of becoming a certified Data Analyst.
Applications of Data Analytics
Healthcare – The fundamental issue for hospitals is to treat as many patients as possible as quickly as possible while maintaining a good standard of care. Data from instruments and machines are increasingly being used in hospitals to track and optimize patient flow, treatment, and equipment. By leveraging software from data analytics businesses, it is anticipated that a 1% efficiency gain will result in more than $63 billion in worldwide healthcare savings.
Travel – Through mobile/weblog and social media data analysis, data analytics can improve the shopping experience. Websites that cater to travelers can learn about their preferences. Upselling products can be done by tying current sales to an increase in browse-to-buy conversions through customized bundles and incentives. Social media data analytics can also be used to provide customized travel recommendations.
Energy management – In utility organizations, data analytics is used for energy management, such as smart-grid management, energy optimization, energy distribution, and building automation. Controlling and monitoring network equipment and dispatch personnel, alongside managing service disruptions, are all part of this program. Utilities have the potential to integrate millions of data points into network performance, allowing engineers to monitor the network using analytics.
What are the different skill sets required for a Data Scientist, Big Data professional, and Data Analyst
Below is a table comparing various skill sets required by all these technologies:
| Big Data Professional | Data Analyst | Data Scientist |
| Technologies like Hadoop, Hive, Spark, etc. | Data Warehousing | Statistical & Analytical Skills |
| Working with unstructured data | Hadoop-based Analytics | Data Mining Activities |
| General Purpose Programming | Adobe & Google Analytics | Correlation |
| SQL/Database coding | Programming skills | Machine Learning |
| Familiarity with MATLAB | Scripting & Statistical skills | Deep Learning principles |
| Business skills | Reporting with data visualization software | Deep programming knowledge |
| Data Visualisation | SQL/Database coding | SQL/Database coding |
| ————– | Spread-Sheet proficiency | SAS/R Coding |
Source: https://www.edureka.co/blog/data-science-vs-big-data-vs-data-analytics/
Conclusion
Finally, big data, data analytics, and data science all assist individuals and organizations in dealing with extensive data collections and extracting useful information from them. Data will become increasingly important in the technology landscape as its value grows dramatically.
Tags: Big data, Data Analysis, Data Analytics, Data Science