Data mining is the process of using all of the data your application has collected to discover patterns and spot trends that otherwise would’ve gone unnoticed. It’s becoming increasingly important to businesses large and small, thanks in part to the rise of machine learning and other intelligent algorithms that can process large volumes of data at velocities that are truly mind-boggling. Everything from your customer data, to your business processes, to the air conditioning controls at your office, can now be analyzed almost instantaneously and provide your applications with a ton of value.

One of the first important things data mining can provide organizations are baselines, those numbers, metrics, and ranges that are considered “normal” for everything your application does. As the volumes of data your apps collect increases, the more data mining becomes a necessity given the sheer amount of information that needs to be analyzed. Once you have baselines established, your applications can then easily spot and forecast trends as well as identify outliers and the things that are not normal and require your attention or further examination. Baselines provide much-needed context for historical data and it can provide the basis for all future forecasting. The more data you have, the more accurate your baselines will be.

Data mining is also incredibly useful in modeling or testing different scenarios and inputs to predict various outcomes. Machine learning is now smart enough to not only know your baseline metrics, but it can also dig down into the data to determine the components and factors that likely created them. This allows applications to model the effects of say, raising prices or entering new markets, with surprising accuracy using the troves of historical data they have already been collected while simultaneously updating those models with new data as it comes in.

But before any of your apps can perform data mining, you first need to collect, connect and integrate all of the data you already have. Platforms like Qrvey make this task easy, allowing companies to not only collect new data but also integrate with any types of existing data, whether it be structured, unstructured or even semi-structured. Unstructured data, in particular, has historically been very difficult to analyze, but Qrvey’s modern cloud architecture can import even unstructured data with ease. By bringing all of this data together into one platform, businesses can then use analytics, automation and data mining to move their applications to a new level of awareness, productivity, and efficiency.

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