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Data Warehousing means a warehouse of data where it can be stored for analysis. It includes the process of data collection from various databases to one specific place to acquire efficient access. It involves the process of joining data from different resources and structuring it in such a way that it can be utilized for maximum benefit. Keeping the operational and transactional information to generate volume, density, and especially the data sources, is the crucial role of data warehousing.

Data Mining is not basically the extraction of data, but it is the process to extract value from any particular data. Actually, it is based on analyzing information, identifying patterns, and creating visualization from the already saved data to get meaningful insights.

Various fields and professions are utilizing data mining to improve their decision-making abilities. Hardly, any sector of science, commerce, or technology would avoid using data mining in the current age as it gives severe advantages.

How Data Warehousing and Data Mining Works Together?

Let me explain it with the help of an example. Suppose you need to cook a few items based on what ingredients you have already in your pantry. For the purpose, you will collect all the ingredients and will put them in one place so that you could have an idea what resources you have. This is basically data warehousing where you have collected all the data from different resources.

Now to make the most of your ingredients, you will analyze based on different recipes that how you could utilize them to make some good food. This is what data mining is all about; it enables you to make future decisions in a better way.

Some Major Data Mining Techniques

To capitalize on what you have got with your data warehousing, you may apply different data mining techniques. It will categorize your data to provide you meaningful statements. These techniques are originated based on artificial intelligence, machine learning, database management, and statistics. Let’s look at some of the methods that data mining experts have introduced after lots of hardships and research.

1. Data Sets Tracking

This is one of the basic techniques that revolve around the recognition of patterns in the data sets. It identifies the deviation of information at particular intervals. If any specific variable is moving back and forth with time, this technique enables you to highlight it.

Let suppose you have a stock of 2 or 3 types of jeans in your warehouse. You realize that in every starting 10 days of the month the demand for particular jeans is increased.

2. Classification of Details

This is a bit challenging technique to cope up with. It involves the classification of data based on different characteristics and turning them into functional groups. It is based on the keen observation that allows you to make further decisions and conclusion about your product and customers.

According to the demand and reviews of your clients about your products, you can classify it A, B, C so that you may re-price them respectively in future.

3. Event Association

It helps in analyzing the attributes that allow you to identify if a particular event is connected to another. It is another form of identifying data patterns which are based on conditional factors.

For example, you have done your data warehousing for your seasonal sale, and now you observed that some of your products (A, B, and C) were usually purchased together. In future you may then create a “customer also bought” section on your website. Whenever a customer buys a product ‘A’, the system will show ‘B’ and ‘C’ in the section “customer also bought”.

4. Detection of Uncertain Actions

It is not necessary that at every starting 10 days of a month could bring you more customers. You might face a couple of months where your sale could go down in the starting 10 days. This is what you need to detect based on your data warehousing. It intimidates you that, in the future, you need to be prepared for unexpected events.

5. Cluster Analysis

The particular technique is based on the in-depth analysis of similarities and dissimilarities of your data. You have collected the data and then using classification technique you have categorized it. Now using cluster analysis, you will look for the behavior of each category.

Let’s have an example of my current company, Australian Master. First 5 years were difficult for them as they were not meeting their break-even point. They hired a data mining team who analyzed the previous deliveries and came to the fact that we need to work hard on our dissertation sector.

data warehousing cluster analysis

6. Prediction of the Expected Picture

Due to better data warehousing and basic data mining techniques, we can analyze the behavior of information and the historical trend of data at a particular interval of time. This technique utilizes all this information to make future predictions of what type of data we will be handling in the future.

7. Creation of Decision Trees

These are basically created when you are facing any specific problem. Let’s suppose a product ‘D’ of your company is not progressing well. You will look into all its aspects, and analyze the data trends of the product to identify the problem and then offer a solution accordingly.

Listen to “Data Driven Shipping is How Shippers Gain “Shipper of Choice” Status” on Spreaker.

Start Data Mining Now

You might be thinking before reading this article that you will need machine learning experts and a highly equipped team to analyze your data and add value to it. Believe me or not, these techniques can be utilized using the database tools that you already have. It will make your data warehousing worthy and will help to improve your productions and future decisions.

Author Bio: Stella Lincoln lives with her daughters. Being a single mother, she works hard and associated with CrowdWriter as a part-time Computer Systems Tutor. Stella is also an SEO Analyst and is associated with the Australian Master. She loves to be updated with technological advancements. Machine learning and AI are her favorite sectors.