Big data analytics projects are at the top of the IT priority list for many organizations looking to wring business benefits out of all the data -- structured, unstructured and semi-structured -- flowing into their systems.

  • Structured – transactional data from enterprise applications
  • Semi-structured – machine data from the IoT
  • Unstructured – text, audio and video from social media and Web application

Many companies now have data in Hadoop or NOSQL, and are faced with the issue of how to integrate that unstructured data with the rest of their corporate data so they can maximize its value. They might already be doing sentiment analysis on the data, but it’s time to dig deeper and turn it into actionable information.

As with any initiative that offers big rewards, there are also accompanying big risks. That's certainly true of a big data implementation, which makes planning and managing deployments effectively a must.

We recommend that you begin with a well-defined target of the business results you're looking to achieve, so you can establish a scope for the data management and analytics systems that need to be built along with the supporting technology that needs to be installed.

How we've helped others with their big data needs:

  • Developed a solution to pull data from a marketing research firm's data lake, allowing them to analyze customer behavior and product usage. We also used data integration technology to pull together big data and cloud applications to allow them to examine their customer accounts and profitability.
  • Worked with an insurance company's team of data scientists who were creating predictive models to examine the impact of weather and econometrics on customers, various insurance lines and products.