An effective data discovery strategy is crucial for organizations to make informed decisions and stay competitive. By establishing a data catalog, search function, governance framework, data stewardship program, and regularly reviewing and updating the strategy, organizations can ensure that their data is easily discoverable and usable across the organization.
Oftentimes we have to rely on a team of domain experts and data engineers to gain an appreciation and an understanding of the threat landscape facing our cyber infrastructures. With cyber-related datasets becoming more prevalent and being shared across more groups the ability to quickly tease out information in a short time-frame is important if we’re going to stay ahead of the advanced threats that are out there.
Data Lakes come with a tremendous promise of providing organizations a way to defer the analysis of data while making sure they collect everything they need. When improperly implemented data lakes can actually result in significant costs and not a lot of return on investment. Having the 5 critical strategies identified above in place can help ensure your organization avoids the traps of data lakes and sets you up for longer-term success.
The goal here is to show the true ease of zero to hero you can get with a tool like Elasticsearch. We will be importing data from a Continuous Glucose Monitor into Elasticsearch and walking through how to easily setup your very own Machine Learning/Anomaly Detection in Kibana. You don’t have to be a techno-wizard to get rolling and learn new things from your data.
Broadly defined, data analytics is the process of analyzing raw data to find insights, trends, and answer questions. Data analytics is a tool used across multiple industries and disciplines to answer a myriad of pressing questions that need an exacting and insightful answer.
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