{"id":4908,"date":"2023-01-10T13:03:19","date_gmt":"2023-01-10T18:03:19","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=4908"},"modified":"2023-02-09T16:51:13","modified_gmt":"2023-02-09T21:51:13","slug":"developing-an-eim-for-life-sciences-strategy","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/developing-an-eim-for-life-sciences-strategy\/","title":{"rendered":"Five Key Factors for Developing an EIM for Life Sciences Strategy"},"content":{"rendered":"

\"EIM<\/p>\n

Solutions Review\u2019s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, Global Head of Offerings for IQVIA Human Data Science Cloud<\/a> Katie Laughlin offers key factors to consider when developing an EIM for Life Sciences strategy.<\/strong><\/i><\/p>\n

\"ExpertAs the post-COVID world moves from a pandemic to an endemic<\/a> environment, individuals are learning to live with the ongoing presence of the coronavirus. This factor, combined with the pivot to virtual communications in medicine and other arenas, points to an ongoing and greater reliance on digital transformation.<\/p>\n

To adapt successfully, life science companies need sophisticated enterprise information management (EIM) systems<\/a><\/strong><\/span> purposefully built to meet their distinctive requirements and challenges. When developing an effective EIM strategy, companies must consider five primary factors.<\/p>\n

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EIM for Life Sciences<\/strong><\/h2>\n
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The Enterprise Data Environment<\/strong><\/h3>\n

Many life science companies invest vast sums of money<\/a> in healthcare data. To properly utilize and benefit from these assets, management needs a clear grasp of the company\u2019s data environment. This includes understanding their information, where it\u2019s stored, and how best to utilize it. They require meticulous processes to access the raw data, analyze and convert it into viable business intelligence, and apply these insights to the organization.<\/p>\n

As data quantities continue to expand<\/a>, capturing, engaging, and optimizing data becomes ever more challenging. Artificial intelligence (AI) and machine learning (ML) applications offer tremendous promise in this respect. Strategies such as cloud migration, developing data lakes and bridging data silos across organizations all help to promote consistency, continuity, and quality. Increased data literacy and tools such as healthcare dashboards<\/a>, as well as data cataloging, data governance, and forecasting solutions<\/a>,<\/strong><\/span> enable teams to maintain a birds eye view of their assets.<\/p>\n

Healthcare Data Governance Requirements<\/strong><\/h3>\n

Healthcare data management<\/a><\/strong><\/span> demands unique governance protocols due to the information\u2019s high value for all stakeholders. Accurate, secure, and high-quality data<\/a> affects patient outcomes and impacts care delivery, as well as offers enrichment opportunities for fraudsters.<\/p>\n

An EIM system purpose-built for the life sciences industry needs a robust governance framework that specifies user rights and responsibilities, accountability, and entrenched data management practices. Such a framework must also outline the rules applied to data acquisition<\/a> and management, and the impact of any changes to business processes.<\/p>\n

Demanding Data Quality Management<\/strong><\/h3>\n

According to the EIM Institute, data is considered high quality<\/a> only if it matches the intended usage and is accurately represented. To enable life science companies to rely on their analytics, it\u2019s vital for their data governance to incorporate quality assessments and procedures to improve data across all datasets.<\/p>\n

A purpose-built EIM enables companies to deploy stringent quality management practices that give users confidence in the reliability of their data. The assurance that data assets are complete and up to date allows for faster decision-making and more effective strategic initiatives.<\/p>\n

Comprehensive Data Acquisition Methods<\/strong><\/h3>\n

Incorporating data acquisition methods into a purpose-built EIM is an important step toward transparency and strong governance. Some of the methods life science companies employ to acquire data include:<\/p>\n