Businesses today have access to more data than ever before. However, despite the ability to collect and process more of the stuff, many are still struggling to use it—as a recent Forbes article points out. Indeed, according to one study by Forrester, the consultancy, almost three-quarters of business data never gets used for analytical purposes.
Data mining is the process of extracting value from a company data’s and turning it into something useful. Let’s learn more about what data mining is and explore some data mining use cases to illustrate its potential.
What is data mining?
Data mining refers to the process of discovering patterns and useful information in large data sets. Many companies hold vast troves of data in a variety of databases, but struggle to make sense of it all. Data mining is therefore the process of extracting insights and information. There are several techniques data scientists use to extract these insights.
Perhaps the biggest attraction of data mining is that it can reveal unexpected insights which you may not have previously been aware of. It can also draw your attention to interesting patterns that can inform your decision-making.
6 data mining use cases
Data mining can provide value to businesses in practically any industry, as the following six data mining use cases show.
Banks collect vast amounts of data about customer behavior, transaction patterns, loan repayments and so on. By setting up a data mining team, a bank can ask key questions of its data, including things like:
- What sorts of transactions are typical of fraud or money laundering?
- Are there certain types of behavior that are typical in the run up to someone declaring bankruptcy?
- Is it possible to identify patterns around when people apply for a mortgage – and then use this to inform marketing activities?
- In-store retail
There are plenty of data mining use cases for retail chains to learn more about their customers and deliver added value. By conducting a data mining project, you can start to ask questions like:
- What patterns can we discern in neighborhood demographics and the kinds of products most often bought in our stores?
- Is there a link between the kinds of food people buy and the kinds of alcohol they consume?
- What does the data tell us about where a new outlet should be opened?
eCommerce success stories like Amazon are at the forefront of data mining, finding ever more intelligent ways to predict what their customers want and suggesting products to them. Other eCommerce businesses can learn from this data mining use case and ask questions like:
- What kinds of images convert site visitors to sales?
- What length of product description is most impactful?
- What is the probability of someone buying a product if they see re-directed ads for that item on other websites?
Insurers can also benefit from conducting a data mining project. Insurers collect huge amounts of information about plans, claims, and the type of products their customers choose. Mining this data allows them to ask questions like:
- What insurance price point is most likely to lead to purchases from different demographics?
- Which kinds of customers are most likely to commit insurance fraud?
- Where and when do people begin applying for certain kinds of insurance?
The manufacturing industry also provides plenty of data mining use cases. Business leaders can ask the following kinds of questions:
- Which raw materials or components do we run low on most frequently?
- Can we better predict when equipment needs to be maintained?
- What is the correlation between production line bottlenecks and the day of the week?
The marketing profession provides countless examples of data mining use cases. Businesses that want to make their marketing operations more productive and efficient can ask things like:
- When are customers most likely to unsubscribe from our services?
- How many interactions does it take to convert leads to paying customers?
- Which marketing activities generate the highest value sales?
As these data mining use cases show, there are endless questions that different kinds of businesses can ask of their data. To make sense of the numbers, you will of course need to set up a data mining team and identify the most important questions to ask of your company’s data.
Data mining and RPA
Robotic process automation (RPA) involves the use of technology that can automatically perform a wide range of computer-based tasks that would normally be carried out by humans. RPA principally focuses on doing things that are time-consuming yet low value, such as data entry or copying and pasting information from one database to another. RPA can support your data mining activities in a number of ways:
- Real-time dashboards: Data mining projects give you a snapshot of your data at one time. However, if you use an RPA system, it can continually check back on your data and notice any changes, then feed this into your dashboard.
- Integrates systems: Data mining and RPA work really well together when it comes to integrating systems. Rather than using your data scientists to hunt for information in several different siloed databases, you could simply set up an RPA that does it for you.
- Cleaning data: Another benefit of data mining and RPA is that you can use an RPA to clean anomalies, duplicates, or blanks from your data sets and therefore save yourself significant amounts of time.
Data mining and RPA with Nintex
Nintex’s powerful RPA solutions can support your data mining use cases by handling a wide range of repetitive tasks. By defining what information the RPA needs to seek out and how often, you can set up an automated data mining solution which can provide you with the most useful, up-to-date information to support your decision-making. Learn more about how Nintex’s RPA can support your data mining activities here.