The cliches are well known by now: data scientists spend the majority of their time simply preparing data for analytics, inheriting the responsibilities of IT teams that traditionally took months to process simple query results.
Conventional data discovery utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data discovery techniques, on the other hand, leverage linked data graphs, comprehensive data models, and a semantic standards-based approach to publish results to those same popular tools.
Data lakes are quickly becoming a hot topic as enterprises determine how best to organize and access the large volume of data they have been generating. Data Lakes are attractive for several reasons, including their ability to expand data across the enterprise while maintaining trust and security with data governance.
Regardless of the ROI of any data-centered solution, upper level management will not support it unless it adheres to governance and security conventions. By definition, data governance formalizes the roles, responsibilities, and rules required for data’s long-term sustainability. Its symbiotic relationship with security ensures that data is protected from the people and practices that negatively affect organizations.
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.