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  • Data quality for big data should include a focus on usability

    To help make big data analytics applications more effective, IT teams need to augment conventional data quality processes with measures aimed at improving the usability of data. Continue Reading

  • Managing the big data ecosystem requires agility amid disruptions

    In some regards, the term big data management can be viewed as an oxymoron. In fact, oxymorons abound in this industry and society -- virtual reality, artificial intelligence, science fiction and awfully good, the latter of which can apply to the challenges encountered in managing the onslaught of big data from multiple sources. There are countless tools, techniques and practices available for the big data ecosystem to properly gather, mine, prep, store and analyze data and help smooth operations, build marketing campaigns, improve customer service and develop the next new product disruptor. As simplistic as this may sound, it's up to data managers to sort it all out as their data lakes swell beyond capacity.

    "The data lake isn't where data goes to die," Gartner analyst Merv Adrian said at the 2017 Pacific Northwest BI Summit, "it's where data goes to live."

    October's Business Information opens with our editor's note and advice for data managers to move beyond traditional data control to the critical task of improving data quality and delivery -- taking all that raw data and making it useful. Whether for internal or external business use, the demands for instantaneous data access continue to accelerate, spurred on by mobile apps, artificial intelligence (AI), machine learning and internet of things (IoT).

    In that vein, our cover story examines companies that use their big data ecosystem to divert data lakes toward developing new strategies, products and revenue streams -- in the process, smashing their old business patterns. In another feature, IoT and machine learning technologies help take the guesswork out of estimated times of arrival for transport companies whose businesses depend on shipping and receiving goods.

    Also in this issue, a business intelligence project combined three data warehouses into one to reduce warehouse size by 80% and data load time from several weeks to just days while causing IT staffing problems in the process. In other features, learn how metadata programs can ease mega management woes; semantic technology could be a blessing or curse to AI; companies are gearing up for greater big data management deployments; and all data must be treated equally in the search to find value.

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  • From all the data chaos emerges big data value

    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. Continue Reading

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