Turn Raw Data into Actionable Insights with Data Mining

If you’ve heard of big data, you’ve probably heard of data mining.

Because the truth is, the value of big data isn’t the data itself—it’s the stories and insights your business can glean from it.

Data mining is how you make the transition from millions of numbers to useful information. Let’s break it down.

What is Data Mining?

Data mining, in the simplest terms, is how companies turn raw data into information they can actually use. Think of a miner sifting through rock to find a gold nugget and you’ve got the right idea, symbolically speaking.

That said, data mining isn’t a process of quantitative wizardry. In fact, it uses many similar statistical methods used on smaller datasets to figure out things along similar lines.

The difference, of course, is that a data miner, scientist, or analyst is dealing with a dataset around five million or five billion instead of just five.

And before you ask—yes, there are that many data points out there about your customers.

We generate data every time we buy coffee, comment on a Facebook post, or even send a text. IBM estimates that about 2.5 billion gigabytes of data were generated every day in 2012.

Remember, mobile phone saturation has shot up every year since then. Which means the amount of data available for companies and governments to use has only grown with time.

The Data Mining Process

If you don’t have the means to conduct useful analysis on the ocean of data available to you, that data will never be much more than a lot of numbers.

Data mining is part of big data analytics, the processes by which companies turn data lakes into insights.

Generally, we can break it down into six steps: problem definition, data exploration, data preparation, modeling, evaluation, and deployment.

Problem Definition

As we’ve noted, there are billions upon billions of gigabytes of data available. The sets your company works with may be a bit smaller than that, but the fact remains that the datasets are enormous.

For this reason, your data analyst and data analytics team need to know what they’re looking for before they start digging.

Once they know the goal of the project, this can be translated into a data mining problem definition. This way, they’ll come back with only the information that’s relevant to the project instead of wasting hours on the information you don’t need.

Data Exploration

Once they know what direction they’re facing, your team can begin the process of data exploration.

This is where the meaty work of data mining techniques comes into play. During data exploration, your analysis team will collect and describe the data available. They may also identify any quality problems that need to be addressed.

Data Preparation

After your data miners have collected the information they need to make insights for the project, domain experts will prepare the data for modelling.

This mostly involves cleaning and formatting the data, as some mining functions will only accept the data in a certain format (which isn’t necessarily model-friendly).

They may also ascribe new attributes to the data at this point, like averages. They will not change the overall meaning of the data, just select tables, attributes, and records that make it easier to translate the data into a model.

Modelling

From here, the data team can turn the data and associated attributes into models.

This is the point at which data miners have to be in frequent communication with the domain experts in the previous step. That’s because some mining functions require specific data types in order to produce a model which the miner can assess.

Evaluation

During the evaluation, the data miners will look at the model they ended up with based on the previous steps. If the model doesn’t meet their expectations, they can go back to the modelling phase and tweak its parameters accordingly.

From here, they can decide what they’re going to do with the data results in accordance with the business objectives outlined in the problem definition phase.

Deployment

This brings us to the final phase: deployment.

This is when the results of the whole process will be translated into a format that others can use. For example, they may export their results into a database.

Once they complete this step, other members of your company can use their results to work towards other business goals depending on the results of the data.

Why is Data Mining Important?

Without data mining, you’re missing key components that will help your business thrive.

For example, data mining techniques can help you identify your most profitable customers—or your least profitable customers.

This will allow you to build smarter business goals, like targeting your most profitable audience more directly.

Essentially, data mining helps you understand your business in a new light.

Key Data Mining Techniques

With that in mind, let’s talk about a few data mining techniques that could prove useful to your business.

These are used in the data exploration phase, which is when data miners sort through data to find the relevant information for the immediate goal.

Regression

Regression is probably the easiest tool available to a data miner. It’s also the least powerful.

Basically, you use regression when you want to predict the value of one feature based on the values of related features in the data.

For example, if you wanted to predict the resale value of a house using a number of variables like square footage or remodelling work, you could use regression. Of course, data miners are working with much bigger datasets to tell you something about your customers rather than predicting the value of a house.

Classification

Classification is a slightly more complex mining tool, though you’ve definitely done something similar before.

In classification, as the name implies, a data miner will group various attributes into categories that will allow them to draw conclusions based on the groupings. For example, you might classify groups based on their credit risk, which would allow you to learn more about them based on the groups.

Clustering

Clustering is actually one of the oldest data mining methods available. There are various types, including:

At its core, clustering is about grouping data into categories. A healthcare provider, for example, might segment their patients based on their age, insurance type, type of injuries, or frequency of visits.

The most popular clustering algorithm is called Nearest Neighbor, which is when a miner will predict one value by looking at related records with a similar estimated value.

Induction Decision Tree

As the name implies, the induction decision tree looks like, well, a tree. In fact, it’s rather similar to clustering or classification.

Basically, each branch of the tree is a classification question. Each leaf is a partition of the dataset related to the specific classification.

The important part of this technique is to properly grow the tree. That is, you need to ask the right question at each branching off point of the tree in order to segment the data in a way that’s useful.

However, the tree cannot grow indefinitely. Growth will halt if:

As with other techniques that require grouping, the places where the data split off can tell the data miner something about the data in question, depending on where the split occurred and what the groups look like after the split.

Association Rule

Finally, the association rule, which is also called association rule discovery.

The method is simple: find all rules that meet specified support and confidence constraints. Basically, we want to find a set of rules which reveals occurrences of one thing when it depends on occurrences of other things. Think of a rule as, “When X, then Y,” if X depends on Y to occur.

Support is defined as the following:

Support = number of transactions with X and Y / total transactions

Confidence is defined as the following:

Confidence = number of transactions with X and Y / total transactions with X

Using Data Mining in Your Business

Of course, it doesn’t do you much good to know what data mining techniques are if you aren’t using them to move your business forward.

That’s where we come in.

We believe that the quality of a company’s results is directly tied to the skills of those performing the task. That’s why we only recruit the best for big data.

Ready to start using big data to your advantage? Use our contact page to get in touch.