{"id":30190,"date":"2019-09-25T14:59:36","date_gmt":"2019-09-25T21:59:36","guid":{"rendered":"https:\/\/www.orgchartpro.com\/?p=30190"},"modified":"2019-09-26T21:51:50","modified_gmt":"2019-09-26T20:51:50","slug":"hr-data-integrity","status":"publish","type":"post","link":"https:\/\/orgchartpro.de\/de\/hr-data-integrity\/","title":{"rendered":"Data Integrity in HR Projects"},"content":{"rendered":"

HR Data Integrity for Organizational Change Projects<\/h1>\n

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OfficeWork Software has provided our clients with workforce planning and organizational change solutions since 2005. During this time we have worked on many different organizational change initiatives: mergers, spin-offs, delayering, downsizing and gearing for rapid growth. Over time we have observed that data integrity problems either prevent, or significantly delay organizations, from achieving their goals..<\/span><\/span><\/p>\n

The Problem With HR Data.<\/span><\/span><\/h2>\n

Six hundred billion dollars annually is what poor data quality costs American businesses, according to the Data Warehousing Institute<\/span><\/a>, and it\u2019s one of the leading reasons why HR projects fail. When it comes to organizational change projects, the issue of poor data quality presents itself about 80% of the time, regardless of the type of company. It\u2019s a serious problem because it prevents the capture of an organization\u2019s current workforce state, and thus makes both modeling and end-state predictions impossible. Poor data quality brings projects to a halt, and resolution consumes enormous resources..<\/span><\/span><\/p>\n

Data quality problems are commonly identified by broken hierarchies, duplicate personnel records, misclassified positions, open positions and missing or incorrect dotted-line reporting relationships. With the resolution of data issues change projects are significantly expedited..<\/span><\/span><\/p>\n

The Data Challenge in Organizational Change Projects.<\/span><\/span><\/h2>\n

One of our clients, a large Internet search company, was merging with a telecom equipment manufacturer. Due to poor data quality the merger had stalled. Neither company could assemble a complete perspective of their organizations and as a result they could not create a visualization of their merging workforces. We helped the client overcome this obstacle and they were able to continue and complete the merger in a short time frame..<\/span><\/span><\/p>\n

Another client, a major television network, was engaged in succession planning and due to a myriad of problems with their data could not develop the necessary organizational views and promotion readiness metrics to support the project. After analyzing the client\u2019s data we provided them a roadmap to correct all gaps, enabling them to complete their succession planning efforts..<\/span><\/span><\/p>\n

The Solution.<\/span><\/span><\/h2>\n

Our agile, predictive process to manage and correct data quality issues have two key components:.<\/span><\/span><\/p>\n