Case Study On Big Data.
What is Data?
Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things.
“Data are becoming the new raw material of business By Craig Mundie, Senior Advisor to the CEO at Microsoft”
What is Big Data?
Big Data is also data but with a huge size. Big Data is a term used to describe a collection of data that is huge in volume and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently.
What Comes Under Big Data?
Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data.
- Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
- Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.
- Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
- Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station.
- Transport Data − Transport data includes model, capacity, distance and availability of a vehicle.
- Search Engine Data − Search engines retrieve lots of data from different databases.
Examples of big data:-
The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.
Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.
YouTube
We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to YouTube alone.
A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Petabytes.
- Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.
- Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.
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
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 are stored in data warehouses. Thus comes to the end of characteristics of big data.
Challenges
At the same time, working with digital trace data instead of traditional survey data does not eliminate the traditional challenges involved when working in the field of international quantitative analysis. Priorities change, but the basic discussions remain the same. Among the main challenges are:
- Representativeness. While traditional development statistics is mainly concerned with the representativeness of random survey samples, digital trace data is never a random sample.
- Generalizability. While observational data always represents this source very well, it only represents what it represents, and nothing more. While it is tempting to generalize from specific observations of one platform to broader settings, this is often very deceptive.
- Harmonization. Digital trace data still requires international harmonization of indicators. It adds the challenge of so-called “data-fusion”, the harmonization of different sources.
- Data overload. Analysts and institutions are not used to effectively deal with a large number of variables, which is efficiently done with interactive dashboards. Practitioners still lack a standard workflow that would allow researchers, users and policymakers to efficiently and effectively.
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?
1) Healthcare
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.
2) Academia
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.
3) Banking
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.
Manufacturing, IT, Retail & Transportation etc.
Big Data Case study:-
1. Walmart
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.
Big Data Used By Industry:-
I hope you understood about the types of big data, characteristics of big data, use cases, etc.
Thanks for reading
