The issue for a data analyst is that businesses create too much information to handle. Algorithms need to sift through this overwhelming amount of data to find the nuggets of value.
Data analytics firm Splunk has published an in-depth guide on understanding the value of machine data and its different use cases.
Machine data has been available for decades, but we have only recently started deploying tools that correlate and analyze data. This linking of data provides a business with actionable insights, to improve the customer experience, prevent fraud, and monitor remote equipment health.
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As a provider of the Data for Everything platform, Splunk is invested in providing these insights to businesses of all shapes and sizes.
“Machine data is valuable because it contains a definitive record of all the activity and behavior of your customers, users, transactions, applications, servers, networks, and mobile devices,” said Splunk.
“It includes configurations, data from APIs, message queues, change events, the output of diagnostic commands, call detail records, and sensor data from industrial systems, and more.”
The issue for a data analyst is that businesses create too much information to handle. Algorithms need to sift through this overwhelming amount of data to find the nuggets of value.
The guide covers a wide range of use cases, including user data, application data, middleware, networking, operating system, virtual infrastructure, physical infrastructure, IoT, social media, and human resources.
To read the full guide, click this link.
