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.
Recent developments in big data technologies have significantly impacted the prowess of contemporary analytics; the most profound of these involves the deployment of semantically enhanced semantic data lakes. These centralized repositories have revolutionized the scope and focus of analytics by enabling organizations to analyze all data assets with a specificity and speed that wasn’t previously available. The value derived from such an approach improves the analytics process at both the granular and macro levels, expediting everything from conventional data preparation to informed action.
Legacy applications that have exceeded their useful life can be expensive to maintain. They often require specialized skills and old versions of software and hardware to support. But, they can also contain very valuable data that needs to be retained for business or compliance purposes.
Mike Atkin of the EDM Council speaks eloquently about the "perfect storm" for data in Financial Services. Two converging forces, regulatory reporting requirements and the need for customer insight, are placing unprecedented demands on the data infrastructure in most financial institutions.
Data integration projects can be time consuming, expensive and difficult to manage.Traditional data integration methods require point to point mapping of source and target systems. This effort typically requires a team of both business SMEs and technology professionals. These mappings are time consuming to create and code and errors in the ETL (Extract, Transform, and Load) process require iterative cycles through the process.
Driving business value from your data often requires integration across many sources. These integration projects can be time consuming, expensive and difficult to manage. Any short cuts can compromise on quality and reuse. In many industries, non-compliance with data governance rules can put you firm’s reputation at risk and expose you to large fines.
The FDA has adopted the CDISC SDTM standard for clinical trial submission. While the standard has the potential to simplify the reporting processes, adoption poses challenges to and raises questions for pharma companies testing their medicines in the clinic. In response, organizations have tasked groups with managing clinical trial metadata in compliances with these standards.
We at Cambridge Semantics have been working with these groups to address these challenges with Anzo Pharma SmartBench, a user-driven platform for developing flexible data collaboration, integration and analytics solutions. Anzo Pharma SmartBench is based on Semantic Web Technology – the same standards used by CDISC to represent SDTM.
Anzo Pharma SmartBench features –
As FDA outlines, “the submission of standardized study data enhances a reviewer’s ability to more fully understand and characterize the efficacy and safety of a medical product,” and adopted CDISC standards based on semantic technology to be the standards for submitting and using study data. They further “envision a semantically interoperable and sustainable submission environment that serves both regulated clinical research and health care.”
There is a massive transformation underway in the financial services industry and it’s all about the data. Unprecedented regulatory reporting demands and increasing competitive pressures are forcing organizations to integrate data across the enterprise. Specifically, they need to change how they discover, track, manage, combine and consume data. For many organizations, this data is not easily accessible -- it is distributed across the enterprise, often trapped in local business units, applications, data warehouses, spreadsheets, and documents.
Traditional technologies are struggling to address this challenge and many industry leaders believe a new approach is required. Some of the new big data solutions do help. They are good at liberating and co-locating data. However, they often struggle to make it usable. Creating a "data lake" without any structure can result in yet another silo of unusable data where context, meaning, and sources are lost.