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From all the data chaos emerges big data value
This article is part of the Business Information issue of October 2017, Vol. 5, No. 5
In psychology, the term gestalt describes the search for meaningful perceptions in a chaotic world -- finding the one reality that explains the whole. The same principle applies for data managers trawling to find meaning in overflowing lakes of undefined, unstructured data. Businesses pour millions of dollars each year into purchasing and developing all variations of hardware and software to collect and analyze data from multiple sources. Depending on whom you ask and what survey you read, value gets mixed reviews from the industry's foot soldiers. Data scientists, business executives, analytics users, industry consultants and research analysts believe those x-bytes of collected data biding time and doing the backstroke in data lakes have plenty of value or very little value. Most companies capture only a fraction of the potential value from data and analytics, a 2016 McKinsey Global Institute report concluded. The biggest barriers companies confront in extracting value are organizational, and many struggle to incorporate ...
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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.
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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.