Get An In-depth Insight About Data Science, Big Data, and Data Analytics World

Today, the world has become data-driven in a true sense. However, we cannot avert from the fact that the amount of existing data increases at lightning speed and transforms the way we live. 

 

Several frameworks such as Hadoop have solved the issues related to storage, and so the primary focus from data has shifted to processing it. 

 

When we think about data processing, several terms come to our mind, such as big data, data science, and data analytics. These 3 terms have prime importance in the data world. 

Not to mention that everything is pretty confused about these terms as they think of them the same, but there is a sheer difference between all three. No doubt certain aspects are quite similar, but other areas are different. 

 

Therefore to eliminate the confusion and bring clarity, this blog will shed light on everything you need to know about all three in detail.

 

Topics to Cover

 

1. Introduction to Data Science

- The Working of Data Science

- Applications of Data Science

 

2. Introduction to Big data

- Attributes of Big Data

- Applications of Big Data

 

3. Introduction to Data Analytics

- Applications of Data Analytics

 

4. Are you Ready to Leverage Data Solutions in your Business?

 

Introduction to Data Science

 

 

Currently, the amount of data we have generated will surprise you because it's enormous. Especially due to the pandemic situation, people have become digital, whether it is work, study, entertainment, cooking, fitness, health, finance, and whatnot.

 

Billions of devices are connected to the internet; imagine the amount of data we produce every day. The data science field is proliferating and transforming several sectors, which has made it difficult to give a clear and concise definition for the same. 

 

To put it simply, data science means drawing clean information from raw data to receive actionable insights. It is a combination of algorithms, tools, and machine learning principles with an aim to find hidden patterns from the raw data.

 

Also Read: Machine Learning in Banking

The Working of Data Science

 

Data science includes plenty of disciplinaries and expertise to generate a holistic and refined look into raw data. Therefore, data experts must be adept in everything from maths, statistics, data engineering, data visualizations, etc., to properly filter chunks of information and convey the most critical parts to drive innovation & efficiency. 

 

Data scientists also depend widely on artificial intelligence, mainly machine learning & deep learning, to build models & make forecasts with algorithms and different techniques. 

 

Applications of Data Science

 

Data science helps us attain significant objectives that were almost impossible or needed ample time and effort a few years back. Below are a few examples of how companies use data science to advance their businesses and products to have a competitive edge over others. 

 

Logistics Sector

 

Driving happens a lot in America. As recorded, automobiles in America generated over 140 billion gallons of fuel. As much as Americans love driving, it also leads to climate change. This is where data science comes into the picture.

 

No doubt, public transport, and bikes can reduce emissions to an extent, but data science can also curb emissions by optimizing travel routes. Although the data-driven adjustments are minor, they can still save tons of gallons, especially when you are a frequent traveler. 

 

UPS, a package delivery company, uses data science efficiently to deliver packages. The company implements AI and ML to solve complex logistics algorithms like overcoming bad weather and rerouting packages. Network Planning Tools allow engineers to determine the best route from the options. As per sources, UPS saved about $100 to $200 million by 2020 using this solution. 

 

Healthcare Sector

 

Back in time, data science left a significant impression on the healthcare sector. For example, Google employees found they could track real-time flu outbreaks by navigating locations on flu-related searches. 

 

Moreover, data science has led to numerous breakthroughs in the healthcare sector. With a broad data network available from clinical databases to medical experts, personal fitness trackers discover new ways to understand the disease, detect diseases quicker, learn preventive medicine, and find new treatment options. 

 

Moreover, it would excite you to know how data science has helped Google in the healthcare field. Google has built a new solution, Lymph Node Assistant, for recognizing breast cancer tumors that spread nearly to lymph nodes. This can barely be seen by the human eye, especially when the cancer is at a nascent stage. 

 

There is one more instance; the famous Clue app uses data science to predict users' menstrual cycle by following the start dates, hair condition, mood, and other parameters. Isn't it awesome how data science works in the healthcare industry?

 

Finance Sector

 

Data science and machine learning together have saved millions for the financial sector. For instance, JP Morgan's COiN platform leverages NLP to process and draw crucial data from around 12k commercial credit contracts annually. 

 

Due to data science, it finishes manual labor tasks much faster. Moreover, FinTech companies such as PayPal and Stripe are massively investing in data science to develop ML tools that instantly track and mitigate malicious activities. 

 

E-Commerce Sector

 

Gone are the days of window shopping. Today everyone shops on their smart devices connected to the internet, where they get personalized online shopping experiences due to data science. Moreover, it should be noted that some online stores adjust prices based on the customers who can afford and who can't. 

 

Airbnb leveraged Data Science to reform its search function. Earlier, it prioritized high-rated vacation stays situated in a specific vicinity from the city's center. This kept the users away from the vacation rentals in the cool neighborhood. However, data scientists resolved that problem. At present, vacation stay gets prioritized in the search ranking if it is located in an area having a high booking density.

 

Also Read: Dynamic Advantages Of Machine Learning in The Healthcare Industry

Introduction to Big Data

 

 

Big Data can be referred to as voluminous data produced worldwide at a phenomenal rate. This data can be structured or unstructured. We all know that data drives the world's modern businesses, and therefore, it becomes essential to derive a sense of this data and to unfold hidden patterns within the massive chunks of data.

 

Imagine what your data could do once converted into business intelligence! Improved and clean data can lead to better decision-making and planning strategically for businesses irrespective of their market share, size, geography, and market segment. 

 

Attributes of Big Data

 

Velocity - The pace at which new data is being created.

 

Scalability - The amount of data companies can gather huge; and therefore, the size of the data becomes a key factor in big data analysis. 

 

Forms - Data can be produced in various formats such as database, text, video, numeric, etc. 

 

Accuracy - It is essential to know if the data is coming from a credible source before decrypting and applying big data for business needs. 

 

Applications of Big Data

 

We have compiled a few awesome examples, which will help you understand why big data is a great tool for your business.

 

Marketing Sector

 

Targeting ads is not a new technique, but it was done earlier using the least data available, anticipating what consumers might like and their responses through surveys and interviews. 

 

But with evolution in the internet and big data field, marketing has evolved too. Currently, it has become easy to gather voluminous data that shows what most consumers look for and like. Moreover, you can also see how effective your marketing campaigns were through CTRs, impressions, and other performance metrics. 

 

Let's take an instance of a digital marketing agency. 3Q Digital agency leverages big data in a way that combines search engine, mobile, social, and video marketing. Their in-house experts create a great mix of marketing platforms by examining data on consumer patterns, transactions, and more.

 

Another marketing agency extracts data from several online & offline sources to make detailed consumer profiles, including location, buying habits, beliefs, and eco-friendly data like current weather conditions.

 

See, The Strategies of AI and ML to Win The Competitive Market

Business Insights

 

Several businesses gather much more data than they actually examine. For example, around 60 to 70% of the data is not utilized. This happens for many reasons, one of them being several analytics tools examining only small random samples of enormous data sets.

 

Therefore, to actively be ahead of your competitors and utilize maximum data, businesses incorporate big data into their businesses. 

 

Netflix, for instance, uses Hadoop and Teradata tools to collect, store, and process voluminous information. These solutions influence its decisions on what type of content it should create and promote to the audience. 

 

Netflix has a vast user base, i.e., around 148 million subscribers for collecting data. After collecting the data, it uses this data in several ways to engage people with its content by creating and verifying original program ideas, like House of Cards (the first Netflix original show).

 

Introduction to Data Analytics

 

Data analytics is a process of analyzing raw data with the aim of gaining insights from the information. It's about finding handy info from the data to help in decision-making. The process includes examining, cleansing, changing, and modeling data. 

 

For instance, a data analyst will gather massive amounts of data from tons of consumer surveys, clean them, and generate reports and visual representations of the data to learn ways to enhance the company's revenues. 

 

Applications of Data Analytics

 

1. Insurance Sector

 

The insurance business poses a risk several times. You should know that when insuring a person, the risk is not based on only information but also data that has been examined statistically before making a decision. 

 

Data analytics provides insurance business details on claims data, real data, and risk data, including all crucial decisions that the company needs to make. For example, an underwriter assesses before the person is insured, then ideal insurance is set.

 

That was about managing risk, now talking about how data analytics helps insurers in interacting with consumers.

 

Insurers can figure out a lot about their services by executing daily consumer surveys, especially after interacting with claim managers. They use this info to know which services are good and which ones need to improve.

 

Using data analytics, insurers can enhance customer experience depending on their behavior and detailed insights. To enhance the consumer experience, insurance businesses must render a vast range of communication methods for their clients.

 

2. Travel Sector

 

Data analytics helps enhance travelers' purchase experience through social media and mobile data analytics. Customers' likings and choices can be known from this; hence, businesses can sell products by connecting current sales to the latest browsers and buy conversion via personalized deals & packages.

 

Moreover, data analytics can also provide tailored travel suggestions based on the results from the social media data. 

 

Are you ready to Leverage Data Solutions in your Business?

 

Data science, Big Data, and Data Analytics are in most demand in several industries today. A blend of the right skill sets and real-life experience can help you build a solid career in these popular fields. 

 

Data science is an all-inclusive term that includes all the tools and technologies used in the whole life cycle of data. Big data refers to massive data sets that need expert and innovative technologies to use the data efficiently. 

 

With time, all three fields will get bigger and broader in all the industries. The demand for data engineers will grow due to data growing at a fast pace. Hopefully, this blog helped explain all the concepts extensively.

 

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