Turning the benefits of Big Data from concept to reality
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Big Data Analytics has left no realm of the world untouched with its vast benefits. The power of Big Data Analytics lies in the fact that every industry across the globe is inclined towards it.
Eric Schmidt, Executive Chairman of Google, says, “There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.”
However, 80% of the data getting generated today is unstructured and cannot be handled by traditional technologies. High penetration of smartphones, rapidly growing mobile data & cloud computing traffic, and swift adoption of disruptive technologies such as AI, ML, and IoT, all contribute to an ever-increasing complexity and volume of data sets.
Big Data comes into play where these huge datasets are either too large or complex for the traditional data processing applications. Therefore, Big Data often includes data with sizes that exceed the capacity of a traditional software to process within an accepted time and value.
According to Tim Berners-Lee, Inventor of the World Wide Web, “Data is a precious thing and will last longer than the systems themselves.”
Today, Big Data’s popularity has extended beyond the tech industry to include supply chain management & logistics, BFSI, retail, healthcare, education, governance, aviation, and manufacturing, to name a few. Almost every big or small enterprise and organization is already leveraging the benefits of Big Data.
Real-time benefits of Big Data
While the benefits of using Big Data are enormous, let us delve in turning the benefits of Big Data from concept to reality.
Big Data in Retail: For the retail industry, Big Data means a greater understanding of consumer shopping habits and how to attract new customers. Some of the biggest retail giants in the world like Walmart, Walgreens, Costco, Sears and Holdings, and many more have Big Data analytics as an integral part of their organizations.
A service-oriented Big Data architecture that analyzes its customer’s data from multiple sources have helped many retailers in ensuring a seamless experience to their clients and a stress-free working of their system.
Big Data analytics in retail enables companies to create customer recommendations based on their purchase history, resulting in personalized shopping and improved customer experiences.
Big Data in Healthcare: There’s a huge need for Big Data in Healthcare. The abundant health data amassed from various sources like medical imaging, electronic health records (EHR’s), medical devices, genomic sequencing, pharmaceutical research, and payor records pave way to the “Big Data in Healthcare”.
The technological advancements have led to the evolution of healthcare applications such as Fitbit and Apple Watch that keeps a tab on the individual’s physical activity levels. The processed data is sent to cloud servers, providing information to doctors who use it as part of their health and wellness program. Big Data is certainly inching towards value-based healthcare by leveraging appropriate software tools, even while reducing costs.
Big Data in Supply Chain: While supply chains generate big data, the big supply-chain analytics turn that data into real insights.
As per Carly Fiorina, ex CEO of Hewlett-Packard, “The goal is to turn data into information, and information into insight.”
Big Data expands the dataset for analysis and applies powerful statistical methods to both the existing and new data sources. The resulting insights help in improving all aspects of supply chain decision-making. There is now a huge potential to redefine the process of planning using internal and external data sources to make real-time demand and supply a reality.
Big Data in BFSI: Experts say that the BFSI industry holds the highest market share among all other end-use verticals of big data analytics. The extrapolative power of risk models used in banking, financial, and insurance sectors can vastly be improved using big data analytics.
It is expanding in diverse segments of the business world using advanced mathematical and statistical models such as predictive analysis, data mining, and AI to gain new acumen ensuring quicker and superior business decisions.
Insurance companies are investing highly in big data analytics to deal with false claims to assess and underwrite risks. Big data analytics help BFSI sectors by providing a platform where real-time transactions can be recorded systematically. The need to deliver customer-centric and customized services and offers are driving the demand for big data analytics in the BFSI industry.
Big Data in Manufacturing: 3/4th of the industrial manufacturing companies have either started developing a big data strategy or are piloting and implementing big data projects, on par with their cross-industry peers. Not surprisingly, the use of big data to address operational optimization was a strong second-place objective among industrial manufacturers.
Innovative manufacturers are using big data technologies to focus simultaneously on customers and operations. The promise of achieving significant measurable business value from big data may be realized only if organizations establish an information foundation that supports the rapidly growing volume of data.
A big data strategy with a business-centric blueprint defines what enterprises want to achieve with Big Data to help ensure pragmatic acquisition and use of resources.
Big Data in Aviation: Right from targeting the customers with interesting offers to their in-flight experience, big data analytics has transformed the aviation industry to a large extent. As per the latest study by Aviation Analytics Market Outlook-2023, “The market size of Big Data Analytics in the global aviation industry is expected to reach $7178 million by 2023, with a CAGR of 17.5%.”
By implementing big data analytics, many airlines have performed well in terms of cost reduction and market share. Easy Jet saw an increase in profit by almost 20% per seat. Big data analytics help achieve differential pricing strategy and customer segmentation, thus generating maximum revenue from the available capacity. Route optimization can also be achieved using big data analytics thereby enabling airlines to increase their most profitable routes.
While we’ve seen enormous benefits of big data analytics scattered across diversified verticals, it is equally imperative to have a holistic Big Data Assurance strategy to address issues related to data quality and rein in the cost of excellence and delivery on projects.
Cigniti’s Big Data Analytics Assurance framework helps understand the essentials and best methods to integrate a big data assurance strategy into existing implementations and help organizations overcome challenges. The big data assurance strategy helps enterprises derive maximum value from their big data implementations. Cigniti’s Big Data assurance solutions include structured, unstructured, and semi-structured data ingestion tests. Cigniti leverages its experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data testing services and solutions such as BI application Usability Testing. Cigniti’s open source Big Data Testing tools help evaluate the reporting app for end-user’s adaptability and continuously review the observations with the user & dev group, as a part of Agile and DevOps testing.
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