While having a pool of data to work with is fantastic, not all data in the big data analytics universe is equal. The proportion of valuable data is constantly growing, making it necessary for banks to filter through unnecessary data. The outcomes of big data analytics are also heavily influenced by data quality. Separating structured data from unstructured data to verify its validity, correctness, completeness, timeliness, and other characteristics is crucial to the success of big data analytics. Due to the limited potential to process large amounts of transactions quickly, older banking systems can sometimes become barriers to big data. Using outdated infrastructure to collect, store, and analyze the requisite data volume puts the system’s stability at risk.
It has become critical to leverage unstructured data and structure it properly to uncover correlations between them. Using innovative analytics tools in conjunction with machine learning, natural language processing, and AI helps deliver accurate results. Business disruption is an essential factor that must not be overlooked. However, it is not always possible to eliminate legacy systems quickly, which is why banks must transition these systems effectively to join the big data narrative. Working to increase processing capacity or reconstructing systems using cutting-edge technology, such as cloud computing, could be a viable option.
At this juncture, various banking analytics companies are entering the market to cater to the different needs of enterprises. To help them choose the solution that best fits their requirement, Banking CIO Outlook has compiled a list of some of the most promising banking analytics solution providers. Besides, the magazine also comprises insights from thought leaders on industry trends, best practices, recent innovations, and their advice for aspiring CIOs.
We present to you Banking CIO Outlook’s ‘Top 10 Banking Analytics Solution Providers - 2021’.
