When we think of mining it sounds manual, tedious, and unfruitful after all hacking away at rock walls for hours on end. Hoping to find gold sounds like a lot of work for a very small reward. Data mining however is quite the opposite. Without doing much work at all, you can reap rewarding results. And that’s because we have modern solutions which do it for us. These softwares can sift through terabytes of data within minutes. Giving us valuable insights on patterns, journeys, and relationships in the data.
So let’s dive into what data mining is, how we do it, and what its examples look like.
What is Data Mining?
Data mining is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data. Data mining tools comb through large batches of data sets with a broad range of techniques. To discover data structures such as anomalies, patterns, journeys or correlations. Though it’s been around since the early 1900s, the data mining we use today comprises of three disciplines:
- the first is statistics the numerical study of data relationships
- secondly we have artificial intelligence the extreme human-like intelligence displayed by softwares or machines
- last but not least we have machine learning the ability to automatically learn from data with minimal human assistance
These three elements have helped us move beyond the tedious processes of the past. And onto simpler and better automations for today’s complex data sets. In fact the more complex and varied these data sets are the more relevant and accurate their insights and predictions will be. SAS defines Data mining as: “Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes.” By unveiling structures within the data, data mining yields insights. These can then be used by companies to anticipate and solve problems, plan for the future, make informed decisions, mitigate risks and seize new opportunities to grow.
What are the steps in Data Mining?
According to Cubeware GmbH on Youtube, The overall process of data mining generally consists of six steps:
- The first is outlining your business goals. It’s important to understand your business objectives thoroughly. This will allow you to set the most accurate project parameters which include the time frame and scope of data. The primary objective of the project in question and the criteria needed to identify it as a success.
- Secondly, understanding your data sources with a deeper grasp of your project parameters. You’ll be able to better understand which platforms and databases are necessary to solve the problem. Whether it’s from your crm or excel spreadsheets, identify which sources best provide the relevant data needed.
- Third is preparing your data. In this step you’ll use the ETL process. Which stands for extract, transform and load. This prepares the data, ensuring it is collected from the various selected sources cleaned and then collected.
- The fourth is analyzing your data at this stage. The organized data is fed into an advanced application and different machine learning algorithms get to work on identifying relationships and patterns. This can help make inform decisions and forecast future trends. This application organizes the elements of data also known as your data points. And standardizes how they relate to one another.
- The fifth is reviewing the results. Here you’ll be able to determine if and how well the results and insights delivered by the model can assist in confirming your predictions, answering your questions, and achieving the business objective.
- And last we have deployment or implementation. Upon completion of the data mining project the results should then be made available to the decision makers via a report. They can then choose how they would like to implement that information to achieve the business objective.
Without proper data management and preparation, data mining could actually work against you by providing inaccurate insights and forecasts. However when done correctly and by the right software, data mining enables you to sift through chaotic data noise to understand what is relevant. From there you can make active use of that information in your decision making.
Data Mining Examples
People tend to assume that more data equals more knowledge. But in reality it’s less about how much data you have and more about what you do with it. Let’s look at a few examples of companies who’ve understood this and have done it right through their smart use of data mining
Groupon: groupon aligned their marketing efforts such as ad campaigns and sales offerings closer to their customers’ preferences by data mining one terabyte of customer data. This data was analyzed in real time and helped the organization identify emerging trends within their audience segment that they could leverage on.
Domino’s Pizza: from its point of sale systems and 26 supply chain centers to text messages social media and amazon echo domino’s pizza improved its marketing and sales performances while enabling one-to-one buying experiences across various touch points it accomplished this by data mining 85 000 structured and unstructured data sources
Air France: Air France klm created personalized travel experiences for their flyers through building a 360 degree customer view based on data mined from trip searches, bookings, flight operations, website, cookies and social media. Gauthier Le Masne, their chief customer data officer said each and every traveler is unique with our big data and talent platform. We offer made just for me travel experiences from purchase planning through the post flight stage.
Well there you have it, now that you understand what data mining is, how it works and the critical role it plays in transforming the way companies do things. perhaps you can start thinking about how these tools can empower you and your teams too. And Remember,
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