生命科学行业的数据洪水已经达到了一个转折点,几乎不可能在内部执行核心信息管理任务。数据的数量和复杂性的增加导致许多人从第三方供应商那里采用IM平台,这些平台有望提高其产品组合中IM任务的更高速度,透明度和合规性。
But our recent survey of 300 IM services executives show they aren’t thrilled with the results so far. Adoption rates continue to lag and this is already an important indicator of how current solutions don’t seem to be attractive “enough” for the demanding audience. Only about 60 percent of companies use product master data management, customer master data management, data warehouse for sales analysis, and artificial intelligence features; and less than half are using reference databases or payer master data management. Among these users only 35-42 percent of respondents are ‘extremely satisfied’ with any of their IM solutions. IM solutions include a variety of components such as master data management, data warehouse, reference database, and artificial intelligence (AI) features.
It is important to note that the lack of satisfaction is due to a number of reasons – not all of which are the vendors’ fault.
Historically, companies begin adoption of third party IM platforms by deploying individual master data management and data warehouse management solutions within specific business units or regions. In many cases, they work with their vendors to customize these platforms to meet the immediate needs of a single user group rather than thinking more broadly about its application over the long term. This can be a simple way to test the technology and see immediate measurable gains. However, these customization efforts can backfire if the platforms aren’t adaptable to other use cases or to the evolving needs of the team.
当公司选择这些平台作为一个关闭解决方案时,也有可能找到无法集成的IM解决方案集合的风险。最终的数据孤岛限制了对单个数据集的访问,并降低了他们获得完整见解并进行有意义的,深远的分析的能力。王者荣耀kpl外围投注
These problems can be avoided when companies create an IM roadmap for the business that considers the long term information management needs of the enterprise as part of the technology vetting process.
Business leaders are also somewhat dissatisfied with the evolution of AI in IM systems. Healthcare industry companies are eager to leverage the promise of AI, machine learning, and natural language processing to achieve automation and predictive analytics.
尽管当前一代的IM平台正朝着这个方向发展,但该技术尚未成熟,无法满足业务领导者对AI和机器学习应该能够做什么的愿景,并且用户发现这令人沮丧。根据我们的调查,有91%的受访者希望显着增加AI在其数据管理活动中的使用,而46%的受访者表示,机器学习是他们最想要的功能,但觉得并没有广泛使用。
供应商正在积极开发这些功能,但是利用AI和机器学习为医疗保健行业IM应用所需的复杂水平是相当大的。lol买外围用什么软件这些不是插件解决方案。为了实现有效的自动化和可靠的预测分析,用户需要能够访问大型集成数据集,并在其团队上拥有专业知识来引起定义明确的业务问题,并培训ML算法如何识别相关趋势。实现生命科学公司需要利用这些功能的数据管理的水平需要时间,金钱和人才。
只有一半的受访者报告采用了参考证据,并且一般满意度的水平并不高(尤其是在EBP/小型公司中),尽管大部分的报告意愿在增加采用此类解决方案方面的意愿。
This can be linked to several factors, including quality, flexibility of the solution and availability, but it’s also fair to assume that the low level of adoption of master data management solutions can be considered an important related factor: without an MDM reference data are typically plugged into each consuming system directly, this generates a lot of point to point connectors and does not really allow leveraging the reference data at enterprise level.
再一次,需要便于整合和消耗信息的技术。必威官方在线
IM技必威官方在线术和产品正在不断发展,但是低采用率和低水平的满意度反映了双方的成熟度有限,并且可能在供应商提供的产品与客户认为所需的东西之间可能不匹配。确保双方受益的唯一方法是让客户在战略上与提供者谈论他们的期望以及如何最好地解决他们以及提供者将客户反馈纳入解决方案路线图中。通过这些合作,提供商可以更加了解购买者的需求,并帮助他们开发与他们需求相关的现实产品路线图。