There are many reasons Big Data can no more be considered a mere buzzword, as it has already turned into a business imperative. The huge opportunities it offers to extract value from business-information can no longer be ignored by organizations. For instance, financial services firms can use Big Data tools to create new customer-centric products to grab decisive opportunities in extending market-leadership. They can also analyze their data on customer-behavior to derive the buying patterns, which then can be used to recommend the products and services to the customers, precisely at the right time. Not only can this help the firms to enhance customer experience, but also enable them instigate newer market trends. However, there are many challenges that financial services firms have to cope before they can get to leverage the several Big Data advantages.
Isolated data chunks
Massive chunks of information that are scalable and extensible form the foundation of Big Data. Isolated and unstructured data from disparate sources makes it difficult to process and derive relevant utility. Inappropriate methods of data storage and retrieval, along with constant mergers and acquisitions are some of the reasons for this type of unstructured and siloed data.
Insufficient Analytical Capabilities
Shortage of analytical capability may appear as the subsequent part of the siloed data issue, but this in fact is the preceding factor of the problem. With limited capabilities to analyze the data, silos of data remain out of scope for considerable duration, leading to the persistence of the issue.
Combining innovative technology and adequate leadership ingenuity to organize, restructure, and leverage data from distinct systems can help in overcoming the existing challenges. Business can start by defining an explicit strategy for their Big Data initiatives, and chart a roadmap to achieve it.
- Initially, financial institutions must focus on critical market observations and spend their resources on gathering adequate knowledge. Following this with assessing their needs and challenges to develop a unique approach to quantify the Big Data is imperative.
- The subsequent step requires maneuvering these initiatives to validate requirements and value additions. Once the plan achieves successful strides, it can be deployed to garner advanced Big Data analytical results by embedding it into operational processes.
Utilizing these approaches to Big Data analytics can help finance companies to overcome the underlying risks of inaccurate assessments and formulating products with rudimentary defects. The ideal approach can help financial service companies harness the power and benefits of Big Data to maximize profits and growth.
- Financial services firms can utilize Big Data analytics to develop customer-centric products. This can help them seize crucial business opportunities, which can help them achieve market-leadership.
- Isolated chunks of unstructured data and insufficient analytical capabilities are the major challenges faced by the financial institutions.
- Businesses can initiate explicit strategy for their Big Data initiatives and chart a roadmap to achieve it.
- The iterative approach can help financial service companies to utilize the power and benefits of Big Data to drive business results.