Data originates everywhere in the healthcare industry – in laboratories, within insurance agencies, in Electronic Medical Record (EMR) systems, etc. – and each of these sources represents a valuable set of information. Healthcare professionals need a comprehensive method that collects and transfers data across systems in a way that is reliable, secure and compliant. But because healthcare system formats are rarely ever the same, it becomes challenging to securely and efficiently share data between vast and diverse data and information sources.
In recent years, the concept of a “data lake” has grown in popularity across the world of big data. From financial services to healthcare, companies are recognizing the value they can bring to data analytics and discovery.
Topics: Data Lake
We are at an inflection point in the financial services industry. The evolving and overwhelming demands of regulatory compliance have forced organizations to acknowledge the need for data governance and most are developing their strategy.
Picking up where we left off in my previous post, companies have been looking to data lakes in the past few years to relieve them of the expensive and time-consuming burden of creating data warehouses. However, as noted in the chart below, both present their own unique challenges and are imperfect solutions at best for effective enterprise data management, discovery and analysis.
Recently, Michele Goetz, Principal Analyst at Forrester Research, and Ben Szekely, VP of Solutions here at Cambridge Semantics, sat down to discuss how organizations can give up the keys to their data systems without creating anarchy. The secret to doing this is utilizing a Smart Data Lake, allowing analysts, decision makers, partners, and customers to gain insights from the data in real time without sacrificing security or governance. These are the slides from their webinar, showing the step-by-step process of democratizing data using the Anzo Smart Data Lake.
Exploratory analytics represents the evolution—and perhaps culmination—of conventional analytics and business intelligence options. It’s a combination of real-time data discovery and ad-hoc, graph-aware analytics that provides automatic, self-service insight for end users on all their data.
With no inherent means of adhering to governance and security protocols, data lakes are akin to the Wild West in that they are devoid of order and consistency. Each user manipulates his or her own data at the risk of the reuse of that data for others.
Unstructured data is all around us: in news stories, web pages, journal articles, social media posts, patents, research reports, presentations, and a variety of other sources. These items are unstructured in that they don’t start out with a predefined, explicit schema or structure. Historically, these documents have been read by humans looking to find information relevant to their particular tasks or roles. In today's deluge, however, the need for scalable reading, repeatability, traceability, and speed has driven the advent of text analytics platforms.
Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes is rapidly emerging as the preferred way to manage big data initiatives.