Access your Pro+ Content below.
Machine learning, IoT bring big changes to data management systems
This article is part of the Business Information issue of October 2017, Vol. 5, No. 5
On a recent commute to TechTarget's Newton, Mass., office, I passed by an Apple Maps vehicle -- a white SUV topped with cameras and spinning light detection and ranging equipment that collects vast amounts of data about streets all over the world. I have a terrible sense of direction and rely on map apps more often than I'd care to admit -- so much so that Google Maps knows my routine inside and out. It proactively updates me on the time it will take to get from home to work or lets me know that I'm only 10 minutes from a Target store. I don't take for granted what a technological feat this level of personalization is; it requires massive amounts of data to be collected in real time, stored and validated before it can be put to use in a handy iPhone app. Commanding lakes of data Yet, with so many connected devices and widely available sensor technologies, access to data isn't the problem. Indeed, most companies are swimming in data. The real challenge is managing all that data and putting it to use. But once the hard part's ...
Access this PRO+ Content for Free!
Features in this issue
Organizations hungry for more revenue are using Hadoop and other big data technologies to break their existing business molds and pursue new strategies and product offerings.
Getting real-time information on where goods are in a supply chain is commonplace with sensors and big data, but some firms use machine learning to predict more accurate ETAs.
When Swisscom needed to merge two SAP ERP systems and several SAP BW data warehouses, it upgraded to one SAP BW on HANA system to reduce data from 5 TB to 1 TB.
Unsung and unheralded, semantic technology is a key component in artificial intelligence and other big data applications. Yet, like AI, it still faces hurdles to going mainstream.
Columns in this issue
Companies are using big data systems, deep learning and machine learning techniques to drive software advances. To go even further, their data management systems must also evolve.
Big data often comes with big data management problems. Clean, well-defined metadata can make the difference in analyzing big data and delivering actionable business intelligence.
Businesses spend millions of dollars to collect, mine, prep and analyze data to gain an edge in the marketplace. Yet, they have a hard time determining big data's actual value.