“Big Data is at the foundation of all the mega trends that are happening.”- Chris Lynch
Big data is something that everyone is talking about. But how big is Big Data? Well, EMC would have us believing that 2020 will have 40 zettabytes bytes of data! Not only this Big data is growing at such a fast pace that the big data analytics market is set to reach a whopping $103 billion by 2023! (Source: TechJury)
Big data is essentially data that is huge in size and growing at an exponential rate with time. The size and complexity of the data are such that traditional data management tools are not enough to store or process it.
Big data facilitates the detection of hidden patterns, correlations, and other insights by examining large volumes of data.
Big data enables organizations to leverage their data to identify new opportunities.
Big data has become an important technology in current times because of the benefits it offers:
Big data technologies such as Hadoop and Cloud-based analytics benefit an organization by cutting down costs in areas such as storage of large volumes of data. They also enable the organization to identify more efficient ways of doing business.
Speeds up Decision Making and Enhances the Quality of Decisions made:
The speed of Hadoop and in-memory analytics coupled with the ability to analyze new sources of data enables businesses to not only analyze data fast but also make decisions based on the data.
Helps Enhance Customer Satisfaction:
Analytics empowers businesses to gauge to identify customer needs. This enables businesses to offer new products and services aligned to the needs of the customer leading to better customer satisfaction.
It is because of these benefits that 97.2% of businesses are investing in Big Data and AI (Source: TechJury).
Volume: As the name suggests, Big Data is about huge volumes of data. In fact, it is the size of data that determines whether it is big data or not. The size of the data plays a vital role in extracting value out of the data.
Velocity: The ‘velocity’ of data refers to the speed at which data is generated. The actual potential in data to meet the demands is assessed by the speed of generation and processing of data.
Big data velocity deals with the speed at which the information flows in, from multiple sources like business processes, application logs, networks, social media sites, etc. The data flow from these sources is continuous and massive.
Variety: Variety of Big data refers to the heterogeneous sources of data as well as the type of data (structured or unstructured). Data could be in the form of emails, photos, videos, monitoring devices, PDFs, and many more. This data is used in analytical applications. The unstructured data poses problems in areas such as storage, mining, and analysis.
Variability: It refers to the inconsistency in the data that makes effective management of data difficult.
Structured Data It is data that can be stored, accessed, and processed in a fixed format. Several techniques have been developed to work with structured data successfully. This type of data is easy to search with both human-generated queries and algorithms using, the type of data and the field names which may be alphabetic, currency, dates, etc.
Some common applications include airline reservation systems, inventory control, sales transactions, etc.
However, the challenge comes when this data grows in size, especially in the range of several zettabytes.
Unstructured Data Any data that lacks form or structure is unstructured data. It is not only huge in size but also presents difficulties in processing to derive value from it.
This type of data cannot be harnessed with the help of traditional data mining tools. It is a challenge to derive value from valuable sources such as rich media, network, customer interactions, and social media data, etc. There are data analytics tools in the marketplace for unstructured data but there is reluctance among businesses to invest in them because of the uncertain development roadmaps. Consequently, despite organizations, having huge amounts of valuable data, they are unable to leverage it because it is in its raw or unstructured form.
Semi-Structured Data It comprises both structured and unstructured data. One of the most common examples of semi-structured data is Email. While email’s native metadata enables classification and keyword searching without additional tools, advanced tools are necessary for thread tracking and concept searching.
The problem of storage and processing of big data is resolved by Hadoop. Hadoop is an open-source distributed processing framework. It facilitates data processing and storage of big data applications in clusters of computer servers. Hadoop systems offer users the flexibility for collecting, processing, analyzing, and processing both structured and unstructured data. This makes it suitable for big data applications.
There are different Big Data analytics tools available that can be used to harness big data effectively.
Some of the big data software available are:
It is a Contemporary data analytics tool that facilitates running SQL queries against a database, process data in R, visualize the results, and make beautiful and interactive dashboards during a matter of minutes Its reasonable pricing makes it a cost-effective tool to share insights with various stakeholders. It also supports a powerful embedding feature that enables adding analytical features easily to websites or web applications.
Answer Dock helps make better and faster data-driven decisions without the need for data analysts. It is essentially an AI-driven Big Data analytics solution that leverages Natural Language Processing to answer the queries of business users. Answer Dock also enables business users to create their own reports and dashboards by typing their questions.
It is a software from IBM that helps to find relationships between data and predict what is likely to happen next. It is essentially a statistical solution that enables businesses of any size in enhancing efficiency and risk management with the help of predictive analysis, deployment of big data, and a library of machine learning algorithms.
It is a versatile software that is suitable for both startups and industry-leading organizations. It offers the best of traditional databases, flexibility, scalability, and the performance that the modern-day apps require.
For building, securing, and deploying big data applications, Amazon web services (AWS) offers a broad and fully integrated portfolio of cloud computing services. The benefit of using was is that you do not need to procure hardware or maintain infrastructure. This facilitates focusing your resources on uncovering new insights. Besides, AWS constantly adds new features and capabilities, enabling you to leverage the latest technologies without investing in long-term commitments.
Big Data is now finding applications in addressing some “Big Issues”. Automotive industry applying Big Data to Avoid Accelerator-Brake Confusion:
Toyota Motor Corp plans to roll out a new emergency safety feature to avoid accelerator-brake confusion in cars that leverages Big data. This new system, namely accelerator suppression function will be a feature in Toyota’s new cars starting in Japan. This objective of this feature is to eliminate accelerator-brake confusion which a cause for most accidents on roads.
This feature is built based on all the data collected from internet-connected cars on the road. This feature differs from the existing safety options because it does not warrant the presence of an obstacle to functioning.
The accelerator suppression function uses big data to overlook the accelerator in case it determines that the driver has stepped on the accelerator unintentionally.
How Big Data Helps Tech Firms Fight Against Coronavirus?
Big data is being leveraged by tech firm`s mobile operators in China to track and prevent pneumonia caused by the coronavirus.
A team comprising over 100 big data technicians and experts has been set up by Unicom (a Telecom giant in China). This team provides data analysis and intelligent applications to the government with the help of algorithmic models.
Big data analysis reports on the epidemic-related population is provided to 31 provincial traffic and health departments.
Efforts are being channeled to mobilize communities and village-level authorities to launch grid-based health management to track the health of residents with the help of big data and public.
Yonyou, a software and cloud services company, has updated its cloud platform with the help of big data technologies and the Internet of Things (IoT) for connecting the supply and demand of medical resources between medical enterprises and hospitals managing the epidemic.
This enabled the platform to release the demand for medical supplies of 30 hospitals for items such as protective clothing, masks, surgical gowns, and shoe covers.
Big data mining has enabled Chinese tech firms to provide full information to the public about the epidemic.
Some of the information that the public can use is:
“Some of the best theorizing comes after collecting data because then you become aware of another reality.”- Robert J Shiller
Data has grown Big and is growing at an exponential rate, the onus is on us to harness Big data effectively to benefit from it!