The EU’s General Data Protection Regulation is approaching its 4th year anniversary since it was implemented in May 2018. Since its inception, it has been hailed as a groundbreaking framework for making users’ rights on the Internet a human right. Its impact in many other markets has been undeniable and it truly has affected how the world of the Internet works, even outside EU borders.
Finance professionals know that data matters, but stories convey truth in ways that mere numbers simply cannot. Those who work in finance may describe themselves as “numbers people.” They have a natural affinity for quantitative information, as well as a knack for drawing meaningful conclusions when presented with a collection of numerical figures. Even so, finance team members probably understand and retain information more readily when it’s presented in narrative form.
The term “observability” means many things to many people. A lot of energy has been spent—particularly among vendors offering an observability solution—in trying to define what the term means in one context or another. But instead of getting bogged down in the “what” of observability, I think it’s more valuable to address the “why.” What are we trying to accomplish with observability? What is the end goal?
The end goal of clinical technology organizations in the US and abroad is to use modern technology to bring life-saving new treatments to fruition. Leaders in this sphere help generate the evidence and insights to help biotech, pharmaceutical, medical device, and diagnostic companies accelerate value, minimize risk, and optimize outcomes. Life sciences clients recognize that technology is the answer to inefficiencies and delays in delivering new treatments to the public.
So, you’re working for a medium to large enterprise that uses Microsoft Dynamics 365 Finance & Supply Chain (D365 F&SCM) as its ERP system. You have multiple options for reporting and analysis available to you from Microsoft. But if your business is growing, you are probably looking to push beyond the out-of-the-box capabilities to develop your own custom analysis and meaningful data insights.
In the exponentially growing data warehousing space, it is very important to capture, process and analyze the metadata and metrics of the jobs/queries for the purposes of auditing, tracking, performance tuning, capacity planning, etc. Historically, on-premise (on-prem) legacy data warehouse solutions have mature methods of collecting and reporting performance insights via query log reports, workload repositories etc. However all of this comes with an overhead of cost-storage & cpu.
The skyrocketing value of data has created a global supply and demand for data, data applications, and data services. This new data economy is powered by technologies that enable data access and sharing, including cloud platforms, exchanges, and marketplaces.
