Challenges of Big Data Systems

When dealing with huge volumes of data that are derived from multiple independent sources. It is a significant undertaking to connect, link, match, clean and transform data across systems. However, it is necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control. Data governance can help you determine how disparate data relates to common definitions and how to systematically integrate structured and unstructured data assets to produce high-quality information that is useful, appropriate and up-to-date.

Big data technologies not only provide the infrastructure to collect large amounts of data, they provide the analytics to understand it and take advantage of its value. The goal of all organizations with access to large data collections should be to harness the most relevant data and use it for optimized decision making.
Some examples:

  • Send tailored recommendations to mobile devices at just the right time, while customers are in the right location to take advantage of offers.
  • Recalculate and revalue entire risk portfolios and provide supplementary analysis providing strategies to reduce risk and in addition mitigate risk impact.
  • Mine customer data for insights that drive new strategies for customer acquisition, retention, campaign optimization and next best offers.
  • Generate special offers at the point of sale based on the customer’s current and past purchases, ensuring a higher customer retention rate.
  • Analyze data from social media to detect new market trends, changing customer perceptions and predict changes in demand.
  • Use pattern matching, fuzzy logic and deep layer data mining of the Internet click-stream to detect fraudulent behavior.
  • Identify and log root causes of failures, issues and defects by investigating user sessions, network logs and machine sensors.