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5 Best Practices to Increase Productivity with Data

By |2021-03-02T19:58:00-05:00March 2nd, 2021|Information Governance and Management|

Productive companies compete more effectively in the marketplace. They also generally report increased profitability, improved employee engagement, happier customers, and opportunities for growth. Knowing best practices to measure and increase productivity with data will help your business thrive. Traditional methods of measuring productivity can prove misleading. For instance, an employee may look busy, work many hours, or even generate large numbers of finished products. But inefficiently used time and low-quality work actually reduce productivity. When businesses gather the right data and analyze it effectively, they can identify top performers as well obstacles that stand in the way of productivity. Armed

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Balancing the Potential and Challenges of the Chief Data Officer

By |2020-11-19T03:58:18-05:00November 11th, 2020|Information Governance and Management|

In 2002, Capital One appointed Cathryne Clay Doss to the role of Chief Data Officer (CDO). The first to hold that title, Doss covered a wide range of duties relating to the strategic oversight of data. Nearly two decades later, the role of CDO continues to evolve. The corporate world took some time to catch the CDO vision. But the explosion of data in recent years has highlighted the critical need for data leadership at executive levels. At the same time, business leaders struggle to agree on what the CDO role entails, what skills it requires and how the reporting

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Managing Data at the Edge

By |2020-11-19T03:51:10-05:00October 28th, 2020|Information Governance and Management, Technology|

Smart technology powers our lives, from wearable tech to traffic control and smart factories. Data analytics promise safer travel, more efficient energy consumption and increased productivity. At the same time, the explosion of data has created new challenges. Increasingly, experts look to the benefits of managing data at the edge to address those challenges. Simply put, edge computing refers to processing data closer to where the data is created or consumed. For example, sensitive factory machinery generates vast amounts of data. Processing that data at the source saves bandwidth. Additionally, access to real-time analysis means that the machinery can adjust

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