How big Is Big Data?
Last updated on 29th Sep 2020, Artciles, Blog
“Big data is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations.
- It refers to a massive amount of data that keeps on growing exponentially with time.
- It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
- It includes data mining, data storage, data analysis, data sharing, and data visualization.
The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data.
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Types of Big Data
Now that we are on track with what is big data, let’s have a look at the types of big data:
- Structured-Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner.
- Unstructured-Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data.
- Semi-structured-Semi structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of types of data. Lets discuss the characteristics of data.
Characteristics of Big Data
Back in 2001, Gartner analyst Doug Laney listed the 3 ‘V’s of Big Data – Variety, Velocity, and Volume. Let’s discuss the characteristics of big data.
These characteristics, isolatedly, are enough to know what is big data. Let’s look at them in depth:
1) Variety- Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM posts, and so much more. Variety is one of the important characteristics of big data.
2) Velocity- Velocity essentially refers to the speed at which data is being created in real-time. In a broader prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and activity bursts.
3) Volume- Volume is one of the characteristics of big data. We already know that Big Data indicates huge ‘volumes’ of data that is being generated on a daily basis from various sources like social media platforms, business processes, machines, networks, human interactions, etc. Such a large amount of data is stored in data warehouses. Thus comes to the end of characteristics of big data.
Advantages of Big Data (Features)
One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks. By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services.
Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money.
Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money.
With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behavior to understand the customer trends and provide a highly ‘personalized’ experience to them.
Who is using Big Data? 5 Applications
The people who’re using Big Data know better that, what is Big Data. Let’s look at some such industries:
Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals and HCPs are now able to provide personalized healthcare services to individual patients. Apart from that, fitness wearables, telemedicine, remote monitoring – all powered by Big Data and AI – are helping change lives for the better.
Big Data is also helping enhance education today. Education is no more limited to the physical bounds of the classroom – there are numerous online educational courses to learn from. Academic institutions are investing in digital courses powered by Big Data technologies to aid the all-round development of budding learners.
The banking sector relies on Big Data for fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards, archival of inspection tracks, faulty alteration in customer stats, etc.
According to TCS Global Trend Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In the manufacturing sector, Big data helps create a transparent infrastructure, thereby, predicting uncertainties and incompetencies that can affect the business adversely.
One of the largest users of Big Data, IT companies around the world are using Big Data to optimize their functioning, enhance employee productivity, and minimize risks in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems.
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Big Data has changed the way of working in traditional brick and mortar retail stores. Over the years, retailers have collected vast amounts of data from local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventory, and so on. Now, they’ve started to leverage this data to create personalized customer experiences, boost sales, increase revenue, and deliver outstanding customer service.
Retailers are even using smart sensors and Wi-Fi to track the movement of customers, the most frequented aisles, for how long customers linger in the aisles, among other things. They also gather social media data to understand what customers are saying about their brand, their services, and tweak their product design and marketing strategies accordingly.
Big Data Analytics holds immense value for the transportation industry. In countries across the world, both private and government-run transportation companies use Big Data technologies to optimize route planning, control traffic, manage road congestion, and improve services. Additionally, transportation services even use Big Data to revenue management, drive technological innovation, enhance logistics, and of course, to gain the upper hand in the market.
Big Data Case studies
Walmart leverages Big Data and Data Mining to create personalized product recommendations for its customers. With the help of these two emerging technologies, Walmart can uncover valuable patterns showing the most frequently bought products, most popular products, and even the most popular product bundles (products that complement each other and are usually purchased together).
Based on these insights, Walmart creates attractive and customized recommendations for individual users. By effectively implementing Data Mining techniques, the retail giant has successfully increased the conversion rates and improved its customer service substantially. Furthermore, Walmart uses Hadoop and NoSQL technologies to allow customers to access real-time data accumulated from disparate sources.
2. American Express
The credit card giant leverages enormous volumes of customer data to identify indicators that could depict user loyalty. It also uses Big Data to build advanced predictive models for analyzing historical transactions along with 115 different variables to predict potential customer churn. Thanks to Big Data solutions and tools, American Express can identify 24% of the accounts that are highly likely to close in the upcoming four to five months.
3. General Electric
In the words of Jeff Immelt, Chairman of General Electric, in the past few years, GE has been successful in bringing together the best of both worlds – “the physical and analytical worlds.” GE thoroughly utilizes Big Data. Every machine operating under General Electric generates data on how they work. The GE analytics team then crunches these colossal amounts of data to extract relevant insights from it and redesign the machines and their operations accordingly.
Today, the company has realized that even minor improvements, no matter how small, play a crucial role in their company infrastructure. According to GE stats, Big Data has the potential to boost productivity by 1.5% in the US, which compiled over a span of 20 years could increase the average national income by a staggering 30%!
Uber is one of the major cab service providers in the world. It leverages customer data to track and identify the most popular and most used services by the users. Once this data is collected, Uber uses data analytics to analyze the usage patterns of customers and determine which services should be given more emphasis and importance.
Apart from this, Uber uses Big Data in another unique way. Uber closely studies the demand and supply of its services and changes the cab fares accordingly. It is the surge pricing mechanism that works something like this – suppose when you are in a hurry, and you have to book a cab from a crowded location, Uber will charge you double the normal amount!
Netflix is one of the most popular on-demand online video content streaming platforms used by people around the world. Netflix is a major proponent of the recommendation engine. It collects customer data to understand the specific needs, preferences, and taste patterns of users. Then it uses this data to predict what individual users will like and create personalized content recommendation lists for them.
Today, Netflix has become so vast that it is even creating unique content for users. Data is the secret ingredient that fuels both its recommendation engines and new content decisions. The most pivotal data points used by Netflix include titles that users watch, user ratings, genres preferred, and how often users stop the playback, to name a few. Hadoop, Hive, and Pig are the three core components of the data structure used by Netflix.
6. Procter & Gamble
Procter & Gamble has been around us for ages now. However, despite being an “old” company, P&G is nowhere close to old in its ways. Recognizing the potential of Big Data, P&G started implementing Big Data tools and technologies in each of its business units all over the world. The company’s primary focus behind using Big Data was to utilize real-time insights to drive smarter decision making.
To accomplish this goal, P&G started collecting vast amounts of structured and unstructured data across R&D, supply chain, customer-facing operations, and customer interactions, both from company repositories and online sources. The global brand has even developed Big Data systems and processes to allow managers to access the latest industry data and analytics.
Yes, even government agencies are not shying away from using Big Data. The US Internal Revenue Service actively uses Big Data to prevent identity theft, fraud, and untimely payments (people who should pay taxes but don’t pay them in due time).
The IRS even harnesses the power of Big Data to ensure and enforce compliance with tax rules and laws. As of now, the IRS has successfully averted fraud and scams involving billions of dollars, especially in the case of identity theft. In the past three years, it has also recovered over US$ 2 billion.
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