Moving Data to Make Big Data

Many different industries all require data technology to achieve innovation and growth

Cleo CEO Mahesh Rajasekharan, Ph.D., visited the 2015 Big Data & Business Analytics Symposium at Wayne State University in Detroit to talk about the growing need for secure big data integration across industries. Healthcare, financial services, and retailers are just a few of the industries using Hadoop and other analytics tools to gain valuable business insight and maintain a competitive edge. But how does this data get where it needs to be?

Extracting value from big data to create actionable intelligence is a challenge, but an advanced big data gateway software solution works seamlessly within your infrastructure to reliably route information and drive business outcomes and initiatives.

Some thoughts from Mahesh, during this five-minute video, on what a trusted solution provides to help achieve the innovation and growth your company seeks:

  • Security: Data should be encrypted at rest and in flight, and controlled to allow only credentialed users to access it.
  • Compliance: Big data solutions should aggregate while enabling data masking to maintain compliance in highly regulated fields, such as healthcare.
  • Transfer flexibility: Accommodate synchronous and synchronous transfers of data.
  • Carrier-grade scalability: Think of how fast data volumes have grown in just the past few years. Will you be able to handle future exploding data volumes?
  • Easy analytics integration: Combing data to come up with prescriptive and predictive intelligence helps to achieve ultimate business outcomes.
  • High-speed transfer capability: Support information movement in a timely manner to enable this actionable intelligence.
  • Various ingestion support: Companies often use a mix of databases, enterprise data warehouses, and data lakes for information ingestion. Ensure you have a schema-on-read approach where structured and unstructured data (Twitter feeds, email aggregations, etc.) can be transformed and analyzed later on.
  • Pliability and agility: Make use of an existing data sources, add new data sources, and be able to leverage normalized data to make business decisions.

And some words of advice from Mahesh for engineering students: Balance the tools aspect with the data science in a curriculum to be more advanced professionals as you graduate and head off into the workforce.