Access your Pro+ Content below.
A strong metadata management process eases big data woes
This article is part of the Business Information issue of October 2017, Vol. 5, No. 5
Big data can mean big insights, big trends, big forecasts and big returns. And it can also mean big problems in managing the data that delivers all those goodies. Metadata -- the data that describes all the collected big data -- is the first line of defense. But if your metadata program isn't well planned and properly implemented, a big problem can get worse. Let's suppose you're mining historical sales data to plot buying trends within a certain demographic, and your results will be passed on to marketing to plan a new campaign. If your demographic is millennials, and marketing defines that group as being in an age range different from what you're using, then marketing's results -- based on your results -- will be off-target. If you and marketing aren't using the same definition, your parameters won't deliver what marketing needs. The point is clear: The business that operates best is the one that works from the same page when it depends on the data that describes data. There are several rules to consider in a metadata ...
Access this PRO+ Content for Free!
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
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.