How much data does your business possess? The chances are you've got a lot more than you did a few years ago. And it's also likely that the next few years will see your data volumes continue to grow at an extraordinary rate.
Trends such as cheap cloud storage, faster networking speeds and Internet of Things sensors that can connect enterprises to a much wider range of data sources mean the data explosion is only just beginning. Indeed, by 2025, it's forecast global data volumes will have reached 163 zettabytes by 2025 - more than ten times that of today.
What's more, around 60 percent of this will be held by enterprises, so businesses will find themselves with huge resources at their disposal. But the key question is; how can they take best advantage of this? Having big data analytics solutions that are able to process huge amounts of data quickly is all well and good, but how do you know which bits of this will actually be of value, and how do you turn this into useful insight?
Understanding data mining
This is where data mining comes in. This refers to efforts to dig deep into your pre-existing data in order to find the most precious pieces of information that can be turned into usable insight for the business. Like mining for rare metals or precious gems, it can take a lot of effort to sift through the soil in order to find the valuable pieces, but the rewards can be huge.
It's a term that can be used quite broadly and is sometimes viewed as synonymous with big data analytics. But while data mining is certainly a key part of these processes, it most often refers to looking for specific, relevant pieces of information within wider data sets, as opposed to traditional big data analytics which process the entirety of the set.
However, the phrase may also be easily misunderstood to mean mining for data, as opposed to mining within data. In other words, you aren't looking for information - that will already be there - but you will be searching for key patterns or trends within this data that can be used to inform business decisions.
The tools you need for success
There are a wide range of analytics tools and techniques that fall within the remit of data mining, and understanding how to use them and what insights they can deliver is essential to making any such activity a success.
For instance, one useful technique is to look for any anomalies within large data sets, which can give insight into a variety of areas. This could, for example, flag up strange activity, or alert businesses to any potential errors that need to be examined and corrected.
Dependency modelling and association are also valuable tools. This involves looking at relationships between various data points, and is hugely useful for understanding patterns and customer habits. For example, this can help retailers identify what products are often bought together and at what times, which can be fed back to marketing teams for use in their campaigns.
This can also help guide predictive tools that seek to identify patterns within data that can be used to infer what will happen in the future. The more data that can be analyzed using these tools, the more accurate future forecasting is likely to be.
What can you find with data mining?
Insights that can be identified using data mining can help guide the direction of a business, spot opportunities before they are readily apparent, and protect an enterprise against threats.
For example, fraud detection is one useful application for data mining that can be used by organizations across multiple sectors. Being able to identify unusual activity within a large set of transactional data enables companies to spot activity that doesn't fit known patterns and send out an alert to start an investigation.
Similar techniques can also help businesses, such as manufacturers, spot faulty equipment or flag up key signs that a failure is imminent before it actually occurs. As well as helping to keep things running smoothly and reducing downtime, insight from this can also be used to better optimize controls to cut out future errors and boost efficiency.
Meanwhile, sales and marketing professionals can also delve deep into data to help guide their strategies. If they can see which campaigns or techniques have proven to be most successful, they can improve their future efforts. Similarly, being able to look at what repeat customers are buying, drawing on historical data and wider trends, can help retailers better forecast future sales and adjust their supply chains to ensure they are well-placed to take advantage of this.