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

1. Critical Success Factors for Big Data Analytics:

• Clear Business Goals - 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.Practical implementations and the approaches to goal setting might differ, yet the result will be the same: setting a clear business goal is essential to ensure the analysis success.
• Relevancy of the data sources - It’s obvious that in order for data mining to provide some credible results, the data should be collected from relevant sources. Gathering the data on average car tire prices will not help increase the sales of burritos, etc. 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.
• Completeness of data - 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.
• Strong commited sponsorship - Big data analytics requires a strong and consistent sponsorship to get the best results.
• A fact based decision making culture - A most important factor to get success in big data analytics
• Alignment between the business and IT strategy - There should be an alignment between the business and IT departments.
• The right analytics tools
• Right people with right skills - Like every field, Big data analytics requires highly skilled people with the relevant skills.

2. Enablers of Big Data Analytics:

• In-memory analytics - storing and processing the complete dataset in RAM.
• In-databases analytics - placing analytic procedure close to where data is stored.
• Grid Computing and MPP - Use of many machines and processors in parallel(MPP-massively parallel processing)
• Appliances - combining hardware,software,and storage in a single unit for performance and scalability.

3. Challengers of big data analytics:

• Data volume - the ability to capture,store and process the huge volume of data in timely manner
• Data integration - the ability to combine data quickly and at reasonable cost.
• Processing capabilities - The ability to process data quickly.
• Data governance - (security, privacy, access)
• Skill availability.

4. Business Problems Addresses by Big Data Analytics:

• Process efficiency and cost reduction
• Brand management
• Revenue maximization (e.g., cross-selling/ up-selling/bundled-selling)
• Enhanced customer experience
• Churn identification, customer recruiting
• Improved customer service
• Identifying new products and market opportunitie
• Risk management
• Regulatory compliance
• Enhanced security capabilities

5. How can Big Data benefit large-scale trading banks?

• Big Data can potentially handle the high volume,high variability, continuously streaming data that trading banks need to deal with
• Traditional relational databases are often unable to keep up with the data demand.

6. How did MarkLogic infrastructure help ease the leveraging of Big Data?

• MarkLogic was able to meet two needs: (1) upgrading existing Oracle and Sybase platforms, and (2) compliance with regulatory requirements.
• Their solution provided better performance, scalability, and faster development for future requirements than competitors.
• Most importantly, MarkLogic was able to eliminate the need for replicated database servers by providing a single server providing timely access to the data.

7. What were the challenges, the proposed solution, and the obtained results?

• The Bank’s legacy system was built on relational database technology. As data volumes and variability increased, the legacy system was not fast enough to respond to growing business needs and requirements.
• It was unable to deliver real-time alerts to manage market and counterparty credit positions in the desired timeframe. Big data offered the scalability to address this problem.
• The benefits included a new alert feature, less downtime for maintenance, much faster capacity to process complex changes, and reduced operations costs.