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The Teachings Of The Pandemic

Data Points Represent People The logic is simple, data is generated by people. So, the lesson here is to think about what good practitioners can do through data and the unintended consequences of the published data at a policy level decision. Data that is used to inform broad public decisions like health and safety measures should be treated with caution than normal public datasets Wrong representation of data can minimize the intensity of the information and influence their decisions around important regulations. A common example for this is what is happening around the vaccine numbers. By sharing misinformation about the vaccine numbers, people responsible are creating a problem by encouraging people to take up vaccines while having supply issues. Data can show the true picture of the intensity of a tragedy COVID-19 showed the true power of data visualization, not on screen but via symbolic representations like candles lit for every life lost or flags meant for social distancing. While data on screen was there, nothing came close to the visual representation and that is the takeaway. Though a data analyst has the numbers, the understanding of those numbers will only come via proper representation. Bias and inequalities in data shouldn’t be tucked away In the US, COVID-19 data is represented at national, state, and district levels, but it took a lot of months for states to release data race-wise. States were insisted to do so because indigineous, Black, and Hispianic communities constitute the essential workers group who were under the risk. The data then showed inequalities in the impact faced by privileged people who had the liberty to work from home and those who had to be on the line daily. Only when analysts don’t hide these inequalities in data, people work on understanding the cause and come with a remedy. Don’t Rely On Just One Data Source Lots of reports saw light during the initial days of COVID-19 regarding positive cases, hospitalizations, and deaths. And towards the end of 2023, different reports came out talking about the mortality and recovery rate which, when compared, showed a complete picture of the impact. This implies that one shouldn’t trust data from one instance. Keeping in mind data’s dynamic behaviour, results should only be judged after a thorough collection. Data transparency matters. While the world is grappling with challenges about the case counts and bias, a lot of mistrust is being created. To make the right data more accessible, the above mentioned issues should be fixed.

Data analytics came as a boon to businesses when they were sitting hand-on-head during the beginning of the pandemic. Data analytics helped organizations sieve through tons of data to get insightful information that helped them understand the changed consumer wants. But the on-going COVID-19 pandemic taught some data lesions that are practical and provocative, ranging from the importance of trust, collaboration, and addressing the limitations and chúng tôi logic is simple, data is generated by people. So, the lesson here is to think about what good practitioners can do through data and the unintended consequences of the published data at a policy level decision.Wrong representation of data can minimize the intensity of the information and influence their decisions around important regulations. A common example for this is what is happening around the vaccine numbers. By sharing misinformation about the vaccine numbers, people responsible are creating a problem by encouraging people to take up vaccines while having supply issues.COVID-19 showed the true power of data visualization, not on screen but via symbolic representations like candles lit for every life lost or flags meant for social distancing. While data on screen was there, nothing came close to the visual representation and that is the takeaway. Though a data analyst has the numbers, the understanding of those numbers will only come via proper chúng tôi the US, COVID-19 data is represented at national, state, and district levels, but it took a lot of months for states to release data race-wise. States were insisted to do so because indigineous, Black, and Hispianic communities constitute the essential workers group who were under the risk. The data then showed inequalities in the impact faced by privileged people who had the liberty to work from home and those who had to be on the line daily. Only when analysts don’t hide these inequalities in data, people work on understanding the cause and come with a chúng tôi of reports saw light during the initial days of COVID-19 regarding positive cases, hospitalizations, and deaths. And towards the end of 2023, different reports came out talking about the mortality and recovery rate which, when compared, showed a complete picture of the impact. This implies that one shouldn’t trust data from one instance. Keeping in mind data’s dynamic behaviour, results should only be judged after a thorough collection. Data transparency matters. While the world is grappling with challenges about the case counts and bias, a lot of mistrust is being created. To make the right data more accessible, the above mentioned issues should be fixed.

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10 Best Data Analytics Projects With Source Codes

Introduction

Not a single day passes without us getting to hear the word “data.” It is almost as if our lives revolve around it. Don’t they? With something so profound in daily life, there should be an entire domain handling and utilizing it. This is precisely what happens in data analytics. People equipped with the technical know-how spend hours on end muddling with datasets. But how do you get there? It may seem an intimidating area, but it is rather intriguing. All you need is a basic understanding of data technologies work, experience working on data analytics projects, and an eye for detail.

Irrespective of your place in the data journey, data analytics projects add significant value to your expertise, resume, and the real world. This article enlists and discusses the 10 best data analytics projects.

Let’s get started with a few fundamental concepts first.

Types of Data Analytics Projects

There are four primary types of data analytics projects: descriptive, diagnostic, predictive, and prescriptive. Each type has its own goals and objectives. Read on to learn more about each explicitly.

Descriptive Analytics Projects

Descriptive analytics is one of the most widely used types of analytics, primarily because it conveys “what is there and what has happened.” Consequently, descriptive projects focus on using historical data and getting an understanding of trends/patterns for future use. The main goal is to gain insights into trends and patterns to help inform future decisions.

Descriptive analytics projects may include the following.

Social media analytics for platforms like Instagram.

Marketing campaigns’ performance analysis to study sales patterns.

Stock market analysis.

Diagnostic Analytics Projects

As the name suggests, diagnostic analytics refers to identifying a problem and then seeking its root causes. As a result, the projects involve analyzing data to understand why something happened and what factors contributed to it.

One of the most standard applications of diagnostic analytics is in the cybersecurity domain. Cybersecurity specialists utilize the same to study data breaches and find a connection between them and security ratings.

Examples:

Examining Market Demand

Improving Company Culture

Identifying Technology Issues

Predictive Analytics Projects

The subsequent step to any descriptive analytics task involves predictive analytics. The latter is all about using statistical methods and machine learning models to predict future states. Consequently, predictive analytics projects aim to use these predictions to make more informed decisions and optimize business processes.

Such projects often involve:

Root-cause analysis: to think “why?” (implying that predictive projects also involve diagnostic analytics).

Data mining: to find any possible correlations between data from different sources.

Sentiment analysis: to determine the sentiment associated with the text.

Prescriptive Analytics Projects

Prescriptive analytics combines predictive analytics with several optimization techniques to recommend or “prescribe” specific tasks or remedies. These projects aim to optimize and improve business processes, resource allocation, and strategic decision-making.

These tasks are tailored to achieve the desired outcome. Prescriptive analytics is widely used for resource allocation, designing personalized marketing campaigns, energy grid management, and a lot more.

Steps Involved in Data Analytics Projects

1. Customer Segmentation Analysis

Imagine pitching premium products to a customer who shops economically or offering bundled products to someone who prefers a single yet priced product. Will this convert?

Probably not. None of the policies checks out the one-size-fits-all criterion, as customers have unique needs and expectations. This is where customer segmentation analysis can save a lot of time and ensure maximum results.

A customer segmentation project aims for data analysts to identify different groups of customers with similar needs and behaviors so that companies can tailor their marketing, product development, and customer service strategies to meet their needs better. This can be done by clubbing them as per: marital status, new customers, repeat customers, etc.

Luxury car manufacturers like Rolls Royce often use lifestyle-centric segmentation analysis to segment their top customers. Clearly, a data analyst familiar with customer segmentation would be a great asset to such businesses.

Putler

You can find the source code for customer segmentation analysis projects here.

2. Sales Forecasting Analysis

Estimating future sales, or revenue for that matter, is a pronounced and essential business practice. As per Hubspot’s research, more than 85% of B2B companies use such data analytics, making sales forecasting projects well-decorated project ideas for analysts.

These projects estimate the revenue the company expects to earn over a pre-decided period, usually 1 year. This amount is computed using several factors, including previous sales data, market prices, demand, etc. As sales forecasting is an ongoing process, the work involves constant updates and bug fixes. Working as a sales forecasting data analyst would be a great option if you are proficient and prompt with constantly running data pipelines.

Companies like BigMart, Amazon and Flipkart rely heavily on sales and revenue forecasting to manage inventory and plan production and pricing strategies. This is primarily done during peak shopping seasons like Black Friday or Cyber Monday.

Toptal

You can find sales forecasting analysis source code here.

3. Churn Prediction Analysis

Customer behavior is still a mystery for all. More often than not, businesses need to predict whether customers will likely cancel their subscription or drop a service, also known as “churn.” Churn prediction analysis aims to identify customers at risk of churning so companies can proactively retain them.

A data analytics project based on predicting customer churn has to be highly accurate, as many people, including customer success experts and marketers, depend on the project findings. This is why data analysts work with high-performing Python libraries like PyPark’s MLIB and some platforms and tools like Churnly.

Braze

You can find churn prediction analysis source code here.

4. Fraud Detection Analysis

The next on our list of analytics projects deals with fraud detection. Fraud detection analysis aims to prevent financial losses and protect businesses and customers from fraud. This is done using several KPIs (key performance indicators) mentioned below.

Fraud Rate.

Incoming Pressure (the percentage of attempted transactions that are fraudulent).

Final Approval Rate.

Good User Approval Rate.

Data analysts are expected to calculate these metrics using historical customer and financial data and help companies detect fraud. One example of a company hiring data analysts for fraud detection is PayPal. PayPal uses manual review processes to investigate suspicious transactions and verify user identities.

Spiceworks

You can fin fraud detection analysis source code here.

5. Social Media Sentiment Analysis

Sheerly, because of the vast number of people using social media to voice their opinions and concerns, it has become increasingly vital to analyze the sentiment behind it. Many companies undertake sentiment analysis to ensure these platforms are safe and sound for society.

Working on real-life big data projects as a learning data analyst gives an idea of how the knowledge is relevant and applicable to the real world. Moreover, social media is transforming into a highly sought-after area of work as social media giants like Facebook, Instagram, etc., are rapidly hiring professionals to analyze sentiments.

eduCBA

You can find social media sentiment analysis source code here.

6. Website User Behavior Analysis

Analyzing how users behave and interact with a product/service on your website is vital to its success. Once you understand their behaviour more deeply, you can discover more pain points and tailor a better-performing customer experience. In fact, 56% of customers only return if they have a good experience.

To ensure everything sails smoothly on a website, data analytics projects involve visualizations (using heatmaps, graphs, etc.) and statistical analysis of user survey data. You will use Python libraries like matplotlib, seaborn, and NumPy, R libraries like ggplot2, dplyr, etc., to map proper user behavior.

Tech companies like Google and Microsoft and medical research companies like Mayo Clinic hire data analysts to work, especially on user behavior analysis.

Hotjar

Here is the source code for website user behavior analysis.

7. Inventory Optimization Analysis

The process can also involve forecasting demand for each product, analyzing inventory turnover rates, and identifying slow-moving or obsolete products. You will be:

Finding target personas,

Studying purchasing (or sales) patterns,

Identifying key locations and seasonal trends,

And optimizing the inventory size.

With experience in inventory analysis, you can seek professional opportunities in e-commerce companies like Amazon, Myntra, Nykaa, etc.

Appinventiv

You can find the source code for inventory optimization analysis.

8. Employee Performance Analysis

As the name suggests, employee performance analysis is a process of analyzing employee data to identify patterns and trends that can help improve employee productivity, engagement, and retention. It can be an excellent practice area as you will deal with data containing different data types, like numerical (attendance, turnover rates, etc.) and categorical (job satisfaction, feedback, etc.).

In such a project, you will need to:

Set goals and decide on performance metrics,

Collect feedback data,

Use this data for preprocessing and analysis,

Infer who performs the best.

You can also work with visualization tools like PowerBI and create dashboards for each department. Or you take up a proper data analytics workflow and do exploratory analysis using Python’s Pandas, NumPy, matplotlib, and Seaborn. Getting good at this analysis will open doors for a promising career in almost any field.

QuestionPro

You can checkout the source code for employee performance analysis here.

9. Product Recommendation Analysis

This is one of the most common data analytics projects. It involves collecting and analyzing data on customer behavior, such as purchase history, browsing history, product ratings, and reviews. The practice is so common that the recommendation engine market is bound to reach over $15,13B by 2026!

It is widely used by e-commerce websites that believe a product display influences shoppers’ behaviour. It has been researched that over 71% of e-commerce websites now offer recommendations after a comprehensive review of historical website data. Analysts spend days and weeks visualizing sales, purchases, and browsing histories using Python libraries like Seaborn, matplotlib, etc.

Proficiency in this data analytics segment can help you build a promising career in companies like YouTube, Netflix, and Amazon.

Project Pro

You can checkout source code for product recommendation analysis here.

10. Supply Chain Management Analysis

Supply chain management involves the planning, execution, and monitoring of the movement of goods and services from suppliers to customers. Following the same, a data analytics project on supply chain management requires you to work on the following:

Demand forecasting,

Inventory management,

Analysis of supplier performance,

Logistics optimization, etc.

The main idea is to study all the factors and see how each one of them affects the chain. Many companies are indulging in supply chain analysis. For example, PepsiCo utilizes predictive analytics to manage its supply chains. As a result, the company actively hires seasoned data analysts familiar with supply chain management. The main idea is to study all the factors and see how each one of them affects the chain.

Network Computing

You can check the source code for supply chain analytics here.

Best Practices for Successful Data Analytics Projects 1. Data Quality and Integrity

A data analytics expert works with vast volumes of data during the entire process of collecting data, preprocessing it, and finally using it for analysis and interpretation. This makes it vital for them to prioritize some of the steps that ensure data cleaning and manipulation is done ethically. While they are free to wrangle data in any form demanded by the project, they must retain all the information, keeping the quality and completeness intact as it directly impacts the accuracy of results.

2. Collaboration Between Teams

Fostering an environment of collaboration and alignment among the team members and different teams sets the project on a successful track. This is because different teams, and individuals, bring different skills and perspectives to the table, resulting in a more diverse and complete analysis.

3. Communicating Results Effectively

Communication is key. It is not only a mantra to success but something that keeps everyone on the same page. Good communication ensures that each team member knows the project’s goals and expectations and can pass on the project findings to all technical and non-technical stakeholders.

4. Continuous Learning and Improvement

Data analytics is an iterative process, and there is always room for improvement. Continuous learning and improvement ensure that the data analytics project results are credible and all necessary changes to improve the accuracy and relevance of the insights are taken into account.

Programming Languages (Python, R)

Python and R are the most popular programming languages in data analytics projects. Both languages offer a wide range of tools and technologies for the same.

Python is a general-purpose programming language. It comes with a bunch of libraries and frameworks like matplotlib, scikit-learn, TensorFlow, pandas, numpy, statsmodel, and many more. These components are widely used in exploratory programming, numerical computation, and visualization.

R programming is a language specifically designed for data analysis and statistical computing. It offers numerous tools and technologies like dplyr, ggplot2, esquisse, BioConductor, shiny, lubridate, and many more.

If you do not wish to avoid getting your hands dirty during the data analysis process, you can work with some visualization tools. As you are probably working through the data domain, you must be aware of Tableau and Power BI.

Data blending,

Interactive dashboards,

Drag-and-drop interfaces,

Data Mapper, etc.

ResearchGate

On the other hand, Power BI is a business analytics service by Microsoft that works similarly and helps in data visualization. However, it is a bit more sophisticated than Tableau and hence, has a steeper learning curve. Power BI offers:

Natural language querying,

Interactive dashboards,

Data modeling, etc.

K21 Academy

Big Data Technologies (Hadoop, Spark)

Big data technologies like Hadoop and Spark are widely used for data analytics projects, especially when organizations need to process and analyze big data.

Hadoop is an open-source software framework that enables distributed processing of large data sets across clusters of computers. Hadoop offers:

Hadoop Distributed File System (HDFS),

YARN (for resource management),

MapReduce, etc.

educba

Spark, on the other hand, is an open-source, distributed computing system that is designed for processing large-scale data sets. Surprisingly, Spark is built on top of Hadoop. Data analysis tools and techniques that Sparks offers:

Spark SQL (for data processing SQL queries),

MLlib,

Spark Streaming, etc.

Crossroad Elf

Importance of SQL in Data Science Projects

If you’re not familiar with how to store structured data, manage its access, and retrieve it when required, you’ll have a hard time working as a data analyst or scientist. SQL is the most famous programming language for storing structured data in relational databases (containing data in a tabular format). As data science is a field brimming with tonnes of data, SQL comes in handy in the manoeuvring of data and storing operations.

In fact, many job positions require analysts to be proficient with SQL querying and manipulation. Moreover, several big data tools like Hadoop and Spark offer explicitly designed extensions for SQL querying just because of how extensive their usage is.

Conclusion

You must now know the vitality of data analytics projects. While they are vital, driving an entire project to success can be challenging. If you need expert guidance to solve Data Science/Analytics Projects, you’ve landed at the right destination. Analytics Vidhya (AV) is a career and technology-focused platform that prepares you for a promising future in data science and analytics while integrating modern-day technologies like machine learning and artificial intelligence. At AV, we realize the importance of staying up to date with recent technologies and hence, offer comprehensive courses. To fuel your career in the domain, we provide a Blackbelt Program in AI and ML, with one-on-one mentorship. Enrol and witness the best learning experience and interview guidance.

Frequently Asked Questions

Q1. Do you need programming skills to do data analytics projects?

A. Having programming skills can be helpful for data analytics projects, but it’s not always necessary. There are tools like Tableau and Excel that allow you to analyze data without coding.

Q2. What are some popular tools for data analytics?

A. Some prominently used data analytics tools used are Python, R, SQL, Excel, and Tableau.

Q3. What are some good data analytics projects for the intermediate level?

A. Some good data analytics projects for the intermediate level include predicting stock prices, analyzing customer churn, and building a recommendation system.

Why Synthetic Data And Deepfakes Are The Future Of Data Analytics?

Synthetic data can help test exceptions in software design or software response when scaling.

It’s impossible to understand what’s going on in the enterprise technology space without first understanding data and how data is driving innovation.

What is synthetic data?

Synthetic data is data that you can create at any scale, whenever and wherever you need it. Crucially, synthetic data mirrors the balance and composition of real data, making it ideal for fueling machine learning models. What makes synthetic data special is that data scientists, developers, and engineers are in complete control. There’s no need to put your faith in unreliable, incomplete data, or struggle to find enough data for machine learning at the scale you need. Just create it for yourself.

What is Deepfake?

Deepfake technology is used in synthetic media to create falsified content, replace or synthesizing faces, and speech, and manipulate emotions. It is used to digitally imitate an action by a person that he or she did not commit.

Advantages of deepfakes:

Bringing Back the Loved Ones! Deepfakes have a lot of potential users in the movie industry. You can bring back a decedent actor or actress. It can be debated from an ethical perspective, but it is possible and super easy if we do not think about ethics! And also, probably way cheaper than other options.

Chance of Getting Education from its Masters

Just imagine a world where you can get physics classes from Albert Einstein anytime, anywhere! Deepfake makes impossible things possible. Learning topics from its masters is a way motivational tool. You can increase the efficiency, but it still has a very long way to go.

Can Synthetic Data bring the best in Artificial Intelligence (AI) and Data Analytics?

In this technology-driven world, the need for training data is constantly increasing. Synthetic data can help meet these demands. For an AI and data analytics system, there is no ‘real’ or ‘synthetic’; there’s only data that we feed it to understand. Synthetic data creation platforms for AI training can generate the thousands of high-quality images needed in a couple of days instead of months. And because the data is computer-generated through this method, there are no privacy concerns. At the same time, biases that exist in real-world visual data can be easily tackled and eliminated. Furthermore, these computer-generated datasets come automatically labeled and can deliberately include rare but crucial corner cases, even better than real-world data. According to Gartner, 60 percent of the data used for AI and data analytics projects will be synthetic by 2024. By 2030, synthetic data and deepfakes will have completely overtaken real data in AI models.

Use Cases for Synthetic Data

There are a number of business use cases where one or more of these techniques apply, including:

Software testing: Synthetic data can help test exceptions in software design or software response when scaling.

User-behavior: Private, non-shareable user data can be simulated and used to create vector-based recommendation systems and see how they respond to scaling.

Marketing: By using multi-agent systems, it is possible to simulate individual user behavior and have a better estimate of how marketing campaigns will perform in their customer reach.

Art: By using GAN neural networks, AI is capable of generating art that is highly appreciated by the collector community.

Simulate production data: Synthetic data can be used in a production environment for testing purposes, from the resilience of data pipelines to strict policy compliance. The data can be modeled depending on the needs of each individual.

Applications Of Data Analytics In Hospitality

Most hospital industry players find it hard to attract new customers and convince them to come back again. It is important to develop ways to stand out from your competitors when working in a competitive market like the hospitality sector.

Client analytic solutions have proven to be beneficial recently since they detect problem areas and develop the best solution. Data analytics application in the hospitality industry has proven to increase efficiency, profitability, and productivity.

Data analytics assists companies to receive real-time insights that inform them where improvement is required, among others. Most companies in the hospitality sector have incorporated a data analytics platform to stay ahead of their rivals.

Below we discuss the applications of data analytics in hospitality.

1. Unified Client Experience

Most customers use several gadgets when booking, browsing, and knowing more about hotels. This makes it essential to have a mobile-friendly app or website and make sure the customer can shift from one platform to the other easily.

The customer’s data should be readily accessible despite the booking method or the gadget used during the reservation. Companies that create a multi-platform, seamless customer experience not only enhance their booking experience but also encourage their customers to return.

2. Consolidates Date from Various Channels

Customers enjoy various ways to book rooms and other services, from discount websites to travel agents and direct bookings. It is essential to ensure your enterprise has relevant information concerning the customer’s reservation to provide the best service. This data can also be important for analytics.

3. Targeted Discounts and Marketing

Targeted marketing is an important tool. Remember, not all guests are looking for the exact thing, and you might share information they are not concerned about by sending them the same promotions.

However, customer analytic solutions assist companies in sending every individual promotion they are interested in, which causes an improved conversion rate. Companies also use these analytics to target their website’s visitors, not just those on the email list.

4. Predictive Analysis

Predictive analysis is an important tool in most industries. This tool is the most suitable course of action for a company’s future projects, instead of simply determining how much a certain project has been successful.

These tools enable businesses to test various options before determining which one has a high chance of succeeding. Consider investing in robust analytics since it saves you significant money and time.

Discussing The Scary Word ‘Pandemic’

Discussing the Scary Word ‘Pandemic’ Public Health prof David Ozonoff is keeping cool, but yes, he’s worried

People in Mexico were wearing face masks in public yesterday. Photo by hmerinomx

As swine flu continues to spread, the World Health Organization yesterday raised its alert level to Phase 4, indicating that “the likelihood of a pandemic has increased, but not that a pandemic is inevitable.” Phase 6 would mean that a global pandemic is under way.

This morning, two elementary school boys from Lowell, Massachusetts who had recently returned from a trip to Mexico were confirmed to have swine flu. They are the state’s first confirmed cases. About 200,000 doses of antiviral medications are being shipped to the state from the national stockpile.

All schools and universities in Mexico have been closed, canceling classes for 13 Boston University students currently in the Guadalajara Engineering Program until at least May 6 (just before finals were scheduled to take place). The study-abroad program officially ends on May 21. Joseph Finkhouse, director of institutional relations for BU International Programs, says that while BU is not encouraging students in Mexico to leave early, the University is offering to help those who wish to do so with travel agent services and by “working with them to make sure they’re not penalized academically.”

Reached by e-mail yesterday afternoon, several students in the Guadalajara program said that they were hoping to leave by week’s end. David McBride, director of Student Health Services, recommends that any students returning from Mexico not come to campus immediately, but “go directly home if at all possible” and remain there for a week to ensure that they are flu-free.

For insights into the swine flu scare, BU Today put some questions to epidemiologist David Ozonoff, a School of Public Health professor of environmental health.

More information about swine flu is available from the CDC and the Massachusetts Department of Public Health.

Pigs infected with H1N1 get sick, but it usually doesn’t pass to humans. Every year, the CDC gets a report of one or two cases of people infected with swine flu, but in recent years it’s been a couple or three. And there have been instances — in 1976 and 1988 — when there was a small outbreak with human-to-human transmission, but it burned itself out after one generation. The current situation seems to be different. It’s probable that several generations of this virus have been passed through human-to-human transmission, and it seems to be more easily transmissible.

There’s an old saying about a lot of things: if you’ve seen one, you’ve seen them all. That’s not true with influenza. This is an incredibly unpredictable, tricky virus.

Our surge capacity, the reserve of staffed beds in our inpatient facilities, is less than it was a few decades ago, because we’ve been trying to cut the cost of health care by eliminating unnecessary hospitalization. The system is much more brittle today. In the summer, Boston’s city emergency rooms will be on diversion, meaning an ambulance will pull up and they’ll divert it to another place because they’re full. A bad flu overwhelms things; a pandemic would really be bad.

But most public health work in the United States is done at the state and local level. And we’re going to hear a lot about suspect cases. Most of them are probably not swine flu. State and local authorities are also involved in thinking ahead and planning.

Chris Berdik can be reached at [email protected].

Explore Related Topics:

What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use. Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy. This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in: