A Guide to Data Mining: The How, Why and Best Practices [2019]

cnxt_dev
cnxt_dev
2019/08/22 20:00

data mining what is

Wondering why your business should care about data mining and how to get started? This is the guide you need.

Where diamonds and gold were once the only things that people were after, today, data is the gleaming bar of gold that everyone wants to get their hands on.

From small start-ups to large corporations, having the right kind of data on hand will give you a powerful competitive edge that you can use to boost your brand as well as your bottom line.

Data Mining 101: An Overview of the Basics

data mining

Data mining is the process of using large volumes of source data to examine underlying patterns that could be useful to your business.

Also known as information harvesting, the basic goal of data mining is to make connections between streams of data and pick up on patterns that aren’t obvious.

Data mining is made up of statistical information, database insights, machine learning and artificial intelligence, all of which can be used for many different purposes, one of which is to predict future values using current data trends.

Data Mining: The Pros and Cons

So, how would your business benefit from data mining and what are the reasons you might not want to consider it at all?

Pros

  • Helps prevent potential problems and obstacles
  • Offers valuable insights that can be used in strategic decision making
  • Discover information that wasn’t known before or expected to be found
  • Since no prior computer science experience is required, every department in the business can access the data and make their own interpretations
  • Reduce the risk of losing new and existing clients
  • Improve customer service and build stronger client relationships
  • Discover new products and services
  • Identify new growth and business opportunities

Cons

  • Additional budget may be required for training and high-performance tools and software
  • Collecting data will require additional time and effort
  • Data security is another aspect that needs to be considered
  • If processes are not perfected, businesses could end up making decisions based on inaccurate data

Data Mining: Understanding the Different Techniques

data mining techniques

  • Classification

Classification is one of the more complex data mining techniques and it’s used to obtain relevant information from data and metadata. As the name suggests, this techniques helps classify data into different classes or groups. For example, low, medium and high would be different classifications.

  • Clustering

When the clustering technique is used, data is classified according to similarities. Basically, you would use this technique if you wanted to understand the differences and similarities between data.

  • Regression

Also known as predictive power, the regression technique is most often used to predict future values. Regression highlights the links between two pieces of data or variables and identifies the likelihood of a specific variable, provided there are other variables present.

  • Association

If you wanted to find a hidden pattern within a data set, this is the technique you would use. Using underlying models in database systems, the association technique will find an association between two or more properties.

  • Outer Detection

The outer detection technique looks for data anomalies. Basically, the aim is to identify data items that don’t necessarily follow a predictable pattern. While this technique is often used to detect fraud or faults, it can also be used in other scenarios such as the buyer’s journey.

  • Decision Trees

At the base of a decision tree is a basic question that has a number of possible answers. Based on all of the possible answers, it’s possible to narrow the choices down to the best answer. For example, if you had to ask someone whether it would be a good idea to go for a run today, the base of the decision tree could be a response such as “if the weather permits”. From there, other answers/options could be added to the mix such as what time it’s predicted to rain or whether it’s going to be too hot outside.

  • Sequential Patterns

If you want to identify patterns or trends in transaction data over a specific period, this is the data mining technique you would use. For example, a business could find out more about why some items are purchased at the same time during a specific time of the year, giving them the opportunity to provide certain customers with better deals.

Data Mining: How to Get It Right

Regardless of the technique you decide to use, there are a few best practices that you should follow if you want to get the most value out of the data mining process.

  1. What are your objectives? It’s one thing to have all the data you need but unless you know what you want to achieve, it’s going to be difficult to decide on a technique. Knowing what questions you want to answer and what your business problems are is the first step to making the most of the data mining process. Set some objectives before you go any further with this process.
  2. Get clear on data modelling. Data modelling refers to the process of creating a system that knows what data you want to collect and how you want to store and organise it. You will probably need the assistance of a developer or data modeller for this part of the process.
  3. Store the data. Once you have data, it’s important not to archive or delete it after it’s been processed. It takes time and effort to collect this information, which is why it’s so important to store it safely.
  4. Don’t forget about post-sales. In most instances, businesses use data to help them get more sales but tend to forget about the post-sales part of the journey. To really get the most value out of your data, be sure to look at factors such as refunds, write-offs, returns and cancellations.

Most businesses will need to embrace data mining at some point or another, which means there’s never been a better time to look into tools and solutions that can help you mine the data you need.