Data mining can help businesses to unearth their potential; here is what you need to know to get digging.
In this age of digitalisation, it can sometimes feel as though there is a lot of information going unused, due to the sheer volume of incoming data and a deficit of data analysts.
This is an immediate loss for businesses, as data is a valuable resource for the companies that know how to properly commodify it. So, what is data mining and how can a business take full advantage of it?
Detailed data mining
Essentially, data mining is the process of using computers and automation to extract patterns, trends and information from large datasets. The discovered information is then used to support decision-making and offer significant insight into data trends and patterns that would otherwise be incredibly difficult to unearth manually.
Data mining gives companies a competitive edge as not only does it expose the areas that are performing well, but it also indicates aspects of a business that are failing to meet expectations.
It has the potential to save a business money by ensuring operations run smoother with less waste and can even improve risk assessment by detecting fraud indicators and predicting legal, financial and security threats.
That being said, there are other, less positive elements that should be taken into consideration before starting your data mining journey. For example, the ethics around how data is collected, particularly via social media platforms and how it is used once gathered.
Simply put, it is important to be honest, clear and ethical when data mining. If you are, then it is likely you will see the benefits it has to offer.
Dig deep
Similar to traditional mining, it is unsafe to go in blind without some form of light or enlightenment to guide the way. So, beginners to data mining should inform themselves as best they can, by isolating specific needs, querying unanswered questions and determining how data mining can help you achieve the outcome you want.
Once you are on solid ground and you know the direction you are heading in, you can start constructing your data mining architecture, that is, the structure of the key components you will be using to perform tasks and pull information.
Selective sourcing
How you choose to source and prepare your data is incredibly important to the overall process. It can be pulled from a number of places, such as databases, files and online platforms and should be accurate and complete, to give insight relevant to your business.
When preparing your data in a stage known as pre-processing, you can clean the data, removing anything irrelevant or inaccurate, and make it far simpler to analyse for further use. You should ensure your data is of a high quality, so you get the best possible results down the line.
Allegorical algorithms
Algorithms tell a story and there are a number of models that can be applied to data mining, depending on what you are aiming to achieve. Popular examples used by companies to gain insight include regression algorithms which compare dependent and independent variables to make predictions.
Association algorithms ascertain how items correlate to one another, if they do at all. This is achieved by observing the rules that typically govern the relationships between database variables.
Anomaly detection algorithms are also useful, as they can identify abnormalities in the dataset that stray from the usual, expected pattern. The discovery of outliers in data can be used to protect against fraudulent activity and threats to security.
Verify through visualisation
A lot of people are visual learners and they understand an idea best when visualisation tools are utilised. Data visualisation enables the sharing and communication of complex data via mediums such as graphs, charts and maps.
This is an effective way to spot patterns and trends as well as identify and analyse deviations from the norm. It is important that companies relay information to the necessary parties in a way that is clear, concise and easy to understand. Data mining takes time and considerable investment, so it makes sense to present the results in the best possible light.
Maintain your mineshaft
Once your model is up and running and you are happy with the work being produced, it is not the time to rest on your laurels. Data mining is similar to a real-world mine in that everything can appear to be operating smoothly but one mistake can cause a complete cave-in of systems
Remember to continuously monitor your model’s performance, accuracy and efficiency, updating as necessary.
If you aren’t getting the results you had hoped for, or your model lacks in some regard, don’t be afraid to make tweaks and changes until it represents the vision you had and answers the questions you posed at the very beginning of your journey into the mines.
Good luck, intrepid adventurer.
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