Big Data Analytics, Essay Example

The matter of concern is still very relevant, even though “big data” has been overused and frequently misinterpreted. According to the findings from Google Trends, people’s interest in this matter has increased over the previous five years. When big data sets are evaluated, and when those analyses are combined, a corporation’s existing organizational procedures and business strategy have the potential to undergo significant change (Sas.com, 2022). To be successful in today’s market, you need to have a solid grasp of the concepts that underlie big data. Convert your company into a data-driven one, and then take the necessary safeguards to restrict access to your information to only the people who are authorized to see it. It is straightforward to locate the data value with the highest likelihood with the assistance of recently created technologies such as artificial intelligence (AI), data lakes, platforms for collaboration, and sophisticated analytics. It is vital to promote innovation, solve problems as soon as they arise, and make communication simpler for decision-makers and technical employees to bring about a change in the firm’s culture. The impact that big data projects have had on corporate decisions and the growth of new markets must be recognized and acknowledged. Organizations have been collecting data, carrying out analytics, and obtaining insights, all of which have led to the discovery of knowledge that might be applied in making future decisions based on the information gained. By engaging in in-depth research of sizable amounts of data, management can produce a plan that is both more specific and more proactive.

Benefits of using big data analytics in Businesses

Increasing the Number of Customers, You Have While Preserving the Existing Ones, online behaviors might reveal a lot about their demands and preferences for goods or services. Using big data analytics, companies may monitor their customers’ behaviors and respond by adapting their products and services to each customer’s specific requirements. As a result, customer satisfaction and loyalty are increased, and as a direct consequence, sales are significantly increased. For example, Amazon has developed the ultimate customized shopping experience to capitalize on its enormous data advantage. This shopping experience generates recommendations for customers based on their previous purchases, the purchases of other customers, their browsing tendencies, and a variety of other factors.

The Identification of Potential Threats

The UOB bank in Singapore stands out for its innovative use of big data for risk management. If adequate risk management procedures are not in place, running a financial institution involves significant inherent risk. A risk management system based on big data was recently put through its paces at the UOB bank. Because of the extensive data risk management system, the bank can reduce the time it takes to determine the amount at risk. The time required has been cut down from 18 hours to just a few minutes, thanks to the use of risk management tools that use big data. As a direct consequence of this attempt, the bank might be able to conduct risk assessments in real-time in the distant future.

Utilizing Big Data Analytics to Offer Marketing Insights and Solution to the Problem Facing Advertisers

As an example of a large brand using big data analytics for customized advertising, look no further than Netflix. Since it has over a hundred million customers, Netflix has amassed an enormous amount of data, and this data is crucial to maintaining the high standing it enjoys in the business (https://www.facebook.com/kopanakis, 2018). If you’re a subscriber, you’re probably already aware of the service’s practice of sending recommendations for films to watch next. Most of this is accomplished by utilizing information from previous searches and monitoring sessions. They use this information to understand better what the user is most interested in. Below, you can see a screenshot of how Netflix gets enormous amounts of data.

Broad & Deep Capabilities

Big data’s workload is as diverse as the data assets intended to examine. Any [hbig data application may be built on a broad and deep platform, and it can support any task regardless of data volume, velocity, or diversity. AWS has over 50 services and keeps adding hundreds of new capabilities each year, giving you all the tools essential to gather, store, handle, evaluate, and display big data in the cloud.

Using big data makes hiring qualified candidates easier

Finding and employing the appropriate staff members is not always a simple task. On the other hand, looking at a candidate’s social media and professional profiles through big data makes it feasible to learn more about that candidate. With the help of this method, the Human Resources department will be able to locate and hire the most qualified applicants in the shortest period feasible. In addition, the use of big data may assist you in gaining a deeper understanding of prospective workers before you recruit them. Finally, utilizing data mining technologies makes it possible to do research across a wide variety of databases and websites. For example, you can forecast whether a new employee will be successful or unsuccessful based on the historical performance data of previous employees.

A few other benefits include: Utilizing big data technologies such as cloud-based analytics may significantly reduce the costs associated with ample data storage (for example, a data lake). Examining significant volumes of data may also assist businesses in developing more effective strategies for running their operations. For example, companies can make prompt and well-informed decisions by making better judgments quickly, using in-memory analytics and new data sources, including streaming data from the Internet of Things (IoT). This is a significant advancement.

These are a few significant trends that will affect how businesses use data.

Big data analytics cannot be summed up in a single technology. In theory, sophisticated analytics may be applied to extensive data, but in practice, various technologies work together to enable you to extract as much value as possible. Listed here are the leading contenders:

Cloud computing is a delivery method based on subscriptions that offer the scalability, speed, and IT effectiveness necessary for successful big data analytics. This approach also allows users to pay only for the resources they use. Because it removes a significant number of the physical and monetary impediments that stand in the way of aligning the IT needs with the changing business goals, businesses of all sizes find it fascinating.

Data management; before data is evaluated accurately, it must first be of high quality and have enough governance. To set and maintain standards for the data quality that an organization gathers, that organization must first develop techniques that are capable of being repeated. After ensuring that their data are accurate, businesses should establish a program for master data management as soon as possible.

Data mining; If you have access to a large amount of data, you may mine it for patterns, which can then be utilized to resolve complex business issues. In addition, the tools for data mining can sort through the redundant and irrelevant data to find helpful information, which enables better decisions to be made in a shorter amount of time.

Data storage– The term “data storage” refers to the act of storing data and encompasses both the “data lake” and the “data warehouse.” Corporate users and data scientists need to have the capacity to store and retrieve massive volumes of structured and unstructured data, respectively. A single data lake can hold vast quantities of raw data anytime. Data lakes are also known as data ponds. Large amounts of data, such as those gathered from social networking sites, images, music, and streaming data, can be preserved in this database. Other types of data that can be saved include streaming data. Streaming data is one example of an additional data type that can be saved. A data warehouse is a centralized database that stores a substantial amount of structured data in an organized fashion. Because these two methods of storing are complementary to one another, many businesses use both.

In-memory analytics; Instantaneous insight and action may be gained from your data using in-memory analytics, which uses system memory (rather than your hard drive) to analyze it. This technology eliminates the latencies associated with data preparation and analytical processing, enabling businesses to carry out iterative and interactive analytics scenarios.

Most big data technologies necessitate extensive server clusters, which require lengthy deployment and configuration procedures. You may nearly immediately deploy the necessary infrastructure if you use Amazon Web Services (AWS). Because of this, your teams will be able to be more productive, it will be simpler to experiment with new ideas, and projects will be able to be implemented sooner.

Conclusion

So, in the end, it is safe to say that we can add significant value to various industries and businesses through the use of Big Data Analytics Examples because we can quickly and easily discover the answer to any complex query simply by mining a large data set. We can also predict future analyses, allowing us to make more precise business judgments. A growing company should consider investing in big data analytics. Businesses can gain a competitive edge, lower operating costs, and increase customer loyalty by utilizing big data analytics. Businesses can easily access a wide data range from their customers. Data is becoming more readily available to all enterprises due to the rapid evolution of technology. In a purely technical sense, corporations already have data available to them. Each organization’s responsibility is to build data analysis systems that can manage large data amounts. You may learn from the examples above and start implementing a brand strategy today.

References

https://www.facebook.com/kopanakis (2018). 5 real-world examples of how brands are using big data analytics. [online] Mentionlytics. Available at: https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/  [Accessed 11 Jul. 2022].

Sas.com. (2022). Big Data Analytics: What it is and why it matters. [online] Available at: https://www.sas.com/en_us/insights/analytics/big-data-analytics.html  [Accessed 11 Jul. 2022].