Wednesday, April 17, 2024

    Getting the Most from Data Mining Tools

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    Data mining falls into a specific use case of data analytics that involves looking for anomalies in large sets. Programs powered by artificial intelligence are typically required to perform highly effective data mining due to the extreme processing power required to derive insights this way. Think about how long it would take you to find something noteworthy in a set of one billion data points. You probably wouldn’t discover anything even if you spent your entire life poring over that one data set.

    While data mining certainly isn’t a free lunch (it takes work just like mining anything else), the rewards can be as splendid as silver or gold. This is because anomalies are often the basis for discovery. Mix in the fact that these things are only findable through processing power and you start to realize why data mining is such an important tool. There are, however, some things to keep in mind to get the most out of data mining tools.

    Set Goals and Intentions

    It’s smart to avoid starting something lacking a plan. This is particularly true when you’re dealing with large sets of data. You’ll waste a lot of time and resources starting off without a solid idea. Moreover, you could be risking the integrity and safety of the data itself.

    Some people think data mining is something like rubbing a magic bottle and having a genie come out to tell the future. While it’s almost supernatural in scope, to get anywhere with data mining requires being firmly planted in reality. There needs to be the intention behind every decision put into your overarching analytics program. Understanding where data mining fits into that overall picture is part of that process.

    Ensure Data is Easily Accessible and Automated

    You’re not going to derive much value from data mining if you’re not analyzing all data sets. Some enterprises have data siloed away behind different permissions walls. While there are certainly reasons for doing this, it’s not helpful when trying to get the most from data mining.

    Look for data mining tools that account for this by making it no issue to integrate separate data sets. This means you’re going to get a much fuller, more accurate picture when you receive results from queries. Business users, who have domain expertise, should have the capability to mine and explore the data on their own. That’s why ThoughtSpot enables organizations to incorporate unlimited users and data sources—vastly enhancing the overall quality of reports.

    Furthermore, the top data mining tools will offer automation features to enhance ad hoc analysis capabilities. There are several benefits to having a fleshed-out ad hoc analytics tool in your arsenal:

    • Always have the most up-to-date information – Real-time ad hoc analytics means you can get instant insights with constantly updated data. This allows for an unparalleled level of accuracy.
    • More people can use data for workflow decisions – Not only do ad hoc capabilities foster a data culture, they allow for far more people to use data in their daily work. This accelerates decisions and makes the organization more efficient.
    • Give data experts more time – Your top data workers don’t need to spend their time working on lower-level inquiries. Ad hoc analytics tools free up the time of data scientists and analysts to work on more pressing problems.

    Make Sure Systems Are Working Properly

    Your data mining efforts will all be for naught if your systems, databases, and tools aren’t working the right way. This will nullify the value of your efforts and potentially send you barreling toward disaster if you’re making decisions based on faulty information. It’s important to perform due diligence and routinely ensure all elements are working correctly so your data mining yields positive results.

    Like all other aspects of data collection and analysis, there’s much to know about data mining tools. Keeping these concepts in mind will help your organization be more effective when implementing data mining tools and strategies.

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