Both business intelligence and data mining can be extremely valuable to your business. However, because the two terms are often used interchangeably, it can be confusing to understand exactly what they are, how they’re different and how they can be used. With this in mind, we’ve put together a guide to demonstrate what each process is and involves.
What is Business Intelligence?
BI is used to provide insights about both your own company and others such as rivals or business partners. It involves collecting and often processing large volumes of data, whether it be through your own internal metrics or third-party resources as specific as an Australian business directory database.
Ultimately it is used to help make more informed and therefore better business decisions, as well as making cost savings and finding new prospects. It can also be used to identify which processes and systems aren’t performing well enough, so managers can alter them accordingly.
Thanks to the wide range of tools now readily available, as well as the rise of big data and an increase in open data initiatives, BI has become much more accessible to companies of all sizes. Whereas it once relied on BI professionals to make sense of the data, these intelligent tools can now take over much of the process, leading to the rise of self-service BI.
What is Data Mining?
Data mining is the process of analysing data to identify useful patterns and insights. The software involved allows companies to analyse information from multiple sources to find trends. Given the huge amounts of data now available, companies use big data management solutions that can enact intelligent data mining to provide them with insights to make informed decisions.
Organisations have benefited in recent times from the increased accuracy provided by data mining software. The recent innovations and developments in these kinds of tools have also meant that it is much more simple and cost-effective to implement data mining techniques for all types of businesses.
Techniques for Data Mining
There are several different techniques for data mining that can be used, these include:
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Classification – This is used to segment data into categories. The process is performed by looking at the data that has already been categorised and then using the rules it has ‘learnt’ from those to apply them to the newly collected information.
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Association – This is used to identify patterns in datasets, as well as to determine the relationship that exists between them. It then uses the patterns identified to make predictions for future behaviour. An example would be the way retail sites like Amazon suggest products to customers based on their previous purchases.
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Clustering – This involves classifying data into segments which are created by the software. One of the biggest advantages of this technique is that the technology can spot small differences or similarities that may not be picked up by human eyes, and therefore create segments that are more accurate.
BI vs Data Mining
So, now we’ve explored the definitions of each of the processes, what exactly is the difference? Well, there are actually a number of ways that the two can be separated. These include:
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Volume – While BI will most often involve using large datasets to find insights, data mining usually involves smaller amounts of data for processing.
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Style – One of the main differences between the two is that while BI uses the tracking of metrics to gain insights, data mining uses computational intelligence and algorithms to uncover useful patterns.
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Outcome – BI provides insights that can help with decision making, while data mining gives answers to particular questions.
Getting the Most Out of BI and Data Mining
Business intelligence and data mining have a number of uses to help drive growth for your company. Let’s explore the ways that your business can implement them into your strategy.
Using Business Intelligence
Business intelligence is ideal for making sense of big data and generating actionable insights from potentially huge amounts of data. Here are some specific uses for the process:
Customer Analysis
In today’s digital world there are more opportunities for customers to engage with your brand than ever before. This includes social media, email, chatbots, website forms, as well as more traditional ways such as calling.
All of these touchpoints allow you to track numerous metrics and gain more data on your target market. This helps you determine which elements of your strategy are working and which aren’t, as well as identifying similarities and patterns in behaviour that will allow you to improve your marketing approach.
Market Research
BI allows you to gain an in-depth understanding of your rival companies, as well as the market as a whole. You could look at things like account statements, web traffic, reviews, social media mentions, and market trends to establish who your main competition is, and how you can gain an edge over them.
Find the Right Channels
When you have a lot of customer data from KPIs to process, it will give you a better understanding of how to find new prospects. This could be the types of channels that they are most likely to use, e.g. Facebook or Instagram, or knowing the types of customers that you want to attract.
For example, if you’re in the B2B sector, and after analysing your metrics find that you’re unexpectedly gaining a large following in the Netherlands, you could then use a reputable provider to source a list of companies in the Netherlands and target new leads directly.
Using Data Mining
Data mining is an extremely useful tool for keeping customers engaged with your brand and also finding new revenue streams that had previously gone untapped. It can also be used for processes such as sales forecasting, market segmentation, and database marketing.
Data mining allows you to increase your revenue by learning things like which products or services customers are interested in, which then enables you to tailor their experience, reducing churn. It also gives you the opportunity to find new opportunities in different markets by using databases and then performing relevant outreach. You can analyse past and present data to identify trends which will then allow you to make better business decisions.
While trying to establish the difference between business intelligence and data mining can seem daunting, so hopefully, we’ve made the distinction a little clearer. Ultimately both of these processes offer good opportunities to gain a full understanding of your customers and market as a whole, giving way to more effective marketing and increased efficiency.
