The following five-step approach can help get more profitability out of data mining implementations. Identify a high-value business problem. It’s important to understand and quantify a high ...

The Cross-Industry Standard Process for Data Mining, better known as CRISP-DM, has been around for more than a decade, and it’s by far the most widely-used analytics process standard.It’s an ...

With the massive expansion of information technology, the demand for data mining also grew hugely. But data mining — or the process of wading through huge amounts of data and finding what you consider ...

The data mining process is usually broken into the following steps. Step 1: Understand the Business Before any data is touched, extracted, cleaned, or analyzed, it is important to understand the ...

Process mining combines data mining and process analytics to recreate the state of an enterprise’s workflow and processes and by doing so, expose bottlenecks, congestion, logjams and deviations.

Process mining goes a step further—it combines the discipline of process improvement with data mining and AI. The result is the ability to take a detailed look at each step of a process, from ...

Data mining is a process that turns large volumes of raw data into actionable intelligence, and it's used by a wide variety of industries. ... The process can be broken down into six steps.

Process mining might begin with an audit of data logged by enterprise resource planning and customer relationship management tools, for example. If an agency or department is beginning (or planning) ...

Process mining has helped the state address decades-old problems that many government agencies are familiar with, including outdated approaches with data scattered across various systems, emails ...

The technology — in this case, provided by Celonis, the first time a U.S. state has used its process mining tools — uses specialized algorithms that analyze data so users can better understand ...