Question

Fundamentals of Big Data Analytics
• Critical Success Factors for Big Data Analytics.
• Enablers of Big Data Analytics
• Challenges of Big Data Analytics
• Business Problems Addressed by Big Data Analytics
Top 5 Investment Bank Achieves Single Source of the Truth
Questions for Discussion
4. How can Big Data benefit large-scale trading banks?
5. How did MarkLogic’s infrastructure help ease the leveraging of Big Data?
6. What were the challenges, the proposed solution, and the obtained results?

Answer 1

Fundamentals of Big Data Analytics is the process of transforming, inspecting and modeling the data with the aim of finding the applicable information. In fact, data analysis is having more than one approaches, technology and also encompassing diverse into the business and science domains. As well as, data analytics will divide into the various categories they are Business perspective, Data science, Real-time usage, Job marketing.

Even the most expensive and sophisticated Big Data analytics system is utterly useless if the results of its work cannot be applied to improve the current workflow, increase the brand awareness or market impact, secure the bottom line or ensure a lasting positive customer experience with the product or service the business delivers.

Big Data mining can be a success only if it has some tangible, certain goals: find out what product or service is the least popular and what can be done to improve the situation.

It’s obvious that in order for data mining to provide some credible results, the data should be collected from relevant sources. However, determining the relevant information sources for a Big Data mining project is not enough. Keeping the dataset size close to the minimally appropriate is essential too.

The next step is making sure the data set is complete, meaning all the essential characteristics and metrics of the intended analysis are covered by at least 1 relevant data source. Having more data sources is better than having only a few, of course, yet the dataset should be kept as lean, mean and efficient as possible to minimize the resources spent.Once you lay your hands on the Big Data analysis results, it’s important to take action to apply them and reach the business goals set.

Challenges

• Uncertainty of Data Management Landscape: Because big data is continuously expanding, there are new companies and technologies that are being developed every day. A big challenge for companies is to find out which technology works bests for them without the introduction of new risks and problems.
• The Big Data Talent Gap: While Big Data is a growing field, there are very few experts available in this field. This is because Big data is a complex field and people who understand the complexity and intricate nature of this field are far few and between. Another major challenge in the field is the talent gap that exists in the industry
• Getting data into the big data platform: Data is increasing every single day. This means that companies have to tackle a limitless amount of data on a regular basis. The scale and variety of data that is available today can overwhelm any data practitioner and that is why it is important to make data accessibility simple and convenient for brand managers and owners.
• Need for synchronization across data sources: As data sets become more diverse, there is a need to incorporate them into an analytical platform. If this is ignored, it can create gaps and lead to wrong insights and messages.
• Getting important insights through the use of Big data analytics: It is important that companies gain proper insights from big data analytics and it is important that the correct department has access to this information. A major challenge in big data analytics is bridging this gap in an effective fashion

4. Big data can handle the high volume, variability, and continuously streaming data that banks deal with.Through Big Data the information is easily located on a single large scale and it becomes easier to reduce the number of risks. Everything that is needed by the banks becomes available at a central platform. This reduces the chances of them losing any information. It also helps them avoid being ignorant towards a fraud. They can easily detect them and in turn reduce all kinds of risks.

5. Mark Logic infrastructure replaced 20 disparate batch-processing servers with a single operational trade store resulted the quick response to the transactions accurately and completely in real time.

6.Being the system based on relational database, with the increase in data volume, it was not able to provide real-time alerts to manage market and counterparty credit positions in desired timeframe. Derivatives trade store based on Mark Logic was introduced replacing the incumbent technologies and upgrading of existing Oracle and Sybase technology to overcome the challenges.The solution resulted drastic changes to the speed of the transaction, accuracy and completeness of data as well as the operational cost is reduced as comparison to previous system.