In practice, big data and analytics allow organizations to manage huge volumes of disparate data in real time to generate actionable insights to drive decision making.
But what does the future hold for big data and analytics?
Al Nugent, co-author of the guide “Big Data for Dummies,” offers some predictions for how the technology might evolve, including:
1. “Retailers (and marketers) will benefit greatly from investments in big data analysis”
Social media has already had a significant and enduring effect on big data. That’s because people love to share details of their day-to-day lives with others. Going forward, big data analysis of the information consumers share will enable retailers to deliver the offers consumers are most likely to buy at the exact moment when they can be persuaded to buy – and to the devices they’re most likely at using at that moment.
2. “Data ingestion rates will increase dramatically”
To keep up with the increase of data from mobile and sensor sources, companies will also be required to quickly ingest huge amounts of different types of data. Technology will, for the most part, keep up with that requirement. However, “just because we can ingest that amount of data, should we do so?” Nugent asks. The answer will depend on the use case, he notes. “This is just the beginning and the potential demands we understand data lifecycles better than we did in the past,” he adds.
3. “Collateral tools and technologies will determine adoption velocities”
Improved analysis and data visualization tools are needed to comprehend relationships, patterns, etc. “Extract, transform and load technologies (ETL) have to evolve so they can perform their services across a broader set of inputs and outputs, all while providing a dynamic capability for extending to not yet known forms,” Nugent says.
Conventional wisdom indicates that users will stop using current ETL tools because they’re based on “legacy” thinking. “There is another market opportunity here,” Nugent notes. “Perhaps we use big data techniques to ingest, transform and stage big data.”