Applying Big Data to Human Capital Analytics

HRResource

Big data is here. Data analysts are leveraging big data to help businesses better understand their customers, markets and operations. HR can learn and leverage some of these techniques to better understand how to attract and retain talent, engage employees, enhance managerial effectiveness and drive business performance. We will provide an overview of big data, how it is being used in the consumer world and discuss applications for the human capital world. We will also provide best practices for getting started with analytics for HR.
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Spotlight

It’s important to understand that not all data is the same. Structured data is most often located in cells in a database, and usually deals with a clear, predetermined business purpose. Instead, unstructured data is most everything else. The most complex portion of unstructured information is textual social media data, news feeds, transcripts, documents, etc. It can’t be easily organized into a database, and it can be ambiguous and difficult to manage because it is characterized by an important trait: human language. Unstructured data is the opposite of structured data: everyday language can contain endless amounts and types of information, is expressed in many different ways, and meaning depends significantly on context. For example, consider that the 500 most common words in everyday language have an average of 23 different meanings. This means that even a simple sentence of just 10 words could have a huge number of different meanings.

OTHER ON-DEMAND WEBINARS

Top BI Trends for 2019

Caserta

Join us for a webinar with Looker where we explore the top Business Intelligence trends for 2019. In this 50-minute webinar, you will learn about the issues affecting BI, the role of analytics as a critical success factor for organizational success, and how companies are leveraging BI and analytics for high-value insights.
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Strategies for Transitioning to a Cloud-First Enterprise

McKnight Consulting Group

A great comfort with cloud deployment has emerged. Businesses are migrating databases to the cloud or building databases there as a result of scale challenges with the on-premises model, the cloud becoming the “center of gravity”, on-premises databases reaching capacity or emerging uses cases that are specific to the cloud. But not all organizations! And some struggle mightily with the move!Learn about the factors that impact organizations when shifting data and applications to the cloud. What must you consider as you move significant applications and data to the cloud? This webinar will cover the major decision points that management needs to consider when moving to the cloud. These include changes to the software model, development and quality assurance, recovery outage, and disaster recovery as well as new concerns about query performance and service levels, data interchange in the cloud, safe harbor and cross-border restrictions, and security and privacy.
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Transform Retail with IBM Big Data Solutions

TechD

New technologies have permanently transformed how customers communicate, interact, research and shop for goods and services. Retail data paired with retail analytics, can assist retailers in understanding and responding with actionable retail insights to the disrupted landscape and changing customer expectations.
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Cloud Data Warehouse Modernization

tdwi.org

The economic model and elegance of cloud environments are motivating companies to assess their existing on-premises data warehouses and modernize their enterprise information environments. However, confusion about what is meant by “modernization” has led some to believe that “lifting and shifting” their on-premises implementation to a cloud environment is the default approach. In these cases, the results are mixed—while the system has technically been moved to a modern cloud platform, by no means is it modernized.
Watch Now

Spotlight

It’s important to understand that not all data is the same. Structured data is most often located in cells in a database, and usually deals with a clear, predetermined business purpose. Instead, unstructured data is most everything else. The most complex portion of unstructured information is textual social media data, news feeds, transcripts, documents, etc. It can’t be easily organized into a database, and it can be ambiguous and difficult to manage because it is characterized by an important trait: human language. Unstructured data is the opposite of structured data: everyday language can contain endless amounts and types of information, is expressed in many different ways, and meaning depends significantly on context. For example, consider that the 500 most common words in everyday language have an average of 23 different meanings. This means that even a simple sentence of just 10 words could have a huge number of different meanings.

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