Security Today Demands New Approaches Built on Machine Learning and Big Data
So much has been spoken, written, and, frankly, overhyped about Big Data in recent years that the most important considerations with regard to Big Data might seem to be: what to believe, and where to start. Actually, IT and data science teams deploying data management solutions are expressing three primary concerns to Stratecast with regard to Big Data:
1. Ensuring that they are accessing all relevant data from all sources to meet the needs of the organization—driven by the fear, or at least healthy skepticism, that they are not.
2. Managing the “firehose” of data that results from comprehensive data access, such that rather than drowning in data, a company can dive in and focus on what is most important.
3. Empowering users with focused, actionable information by making it simple for organization users to query the data to quickly obtain the information they need.
If an organization can step up to these challenges, it can achieve what data management is supposed to be all about: achieving a high degree of accuracy in assessing current conditions, studying past events and outcomes to spot relevant trends, and using insights from current and past occurrences to predict future behaviors.
An important question remains: where might data management be best applied?
About this report
This Stratecast report analyzes the challenges of today’s expanding threat landscape, and how Big Data-powered machine learning can help meet those challenges.
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