Leveraging Big Data Analytics for Service Management

Many of the most exciting innovations and advancements in data management today are occurring within the semantic layer of data architectures. For example, we’re witnessing new or improved approaches to semantic modeling, data cataloging, data lineage, and more. Even older forms of semantics—such as metadata and virtualization—are being infused with new techniques for augmentation and automation, including intelligent tool algorithms driven by machine learning and the use of graph analytics to generate data maps and automatically document data elements found via graph.

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.

As rare diseases are so rare, patients are often misdiagnosed for many years, or never correctly diagnosed. Electronic health records hold much important information that can be used to correctly diagnose and treat these patients, but identification of phenotypic sets is hard to extract from reams of data.

In companies today, numerous documents are in circulation. Yet the data inside needs to be captured so it can be used for further processing. Just think about how the Finance team needs access to data in invoices. Or how the market analytics department needs access to utility bill data to perform market research. Brokers who need to retrieve data from purchase orders, IT departments that require data extraction as a part of a paperless workflow. The list goes on.