Future of Data Life Cycle management

The FinTech industry is going through a phase of high technology spending and high level of uncertainty. Firms are taking a big hit on the profit margin, so they are looking at increasing their margin on the product and the processes and reduce their involvement by increased STP rates. At the same time firms have been taking brave decisions of cutting down the spending on operations where there is a rarity of chances to amplify the margins or increase the company’s market value.

 With each passing day as financial services companies gains a better understanding of customers and their preferences to provide more efficient services and solutions 

The amount of data grows every day, & now the data collection occurring more frequently, data become more complex. The challenge is not only the growing data; it is volatility of the data and how to extract information from data which is real-time more accurate and lower data latency also to create taxonomies that are future proof.

Data life cycle


  • Data Acquisition – The data is semi-structured or completely unstructured acquired from various sources like the corporate discourse, regulators, blogs, websites, and media, etc. Data is the biggest asset and for the firms today, it gives the competitive advantage to the company, financial firms have been investing heavily in data aggregators.
  • Information Extraction – Removing noise and erroneous data is necessary for the interpretation of relevant information for the end users like the financial advisors, regulators and investors to make good decisions.
  • Ontology Learning – Capturing the knowledge and adapting it dynamically to the incoming data stream, Ontology is a formal representation of the knowledge, it plays a significant role in knowledge and data integration, as the data is growing very fast and access to a structured source of data is becoming more and more important. Crowdsourcing ontologies are becoming more popular as it combines the computing power of the machines and analytical accuracy of humans. 
  • Sentiment Analysis – Sentiment analysis or opinion mining is a combination of natural language processing (NLP) and semantic techniques that can determine the positive, negative or the neutral view associated with the process or the product.
  • Information IntegrationEnhancing time-series and other structured data, with knowledge obtained from unstructured sources. The financial firms are working on creating a taxonomy that is “Future proof” for few years, through the information integration from structured and unstructured sources.
  • Information Visualization End user visualization is the final step in the data lifecycle management process which helps the end user to make more accurate investment decisions.




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