How Your Data Warehouse Can Make Data Mining Easier And More Efficient

Data mining is the process of deriving business insights from large or complex data sets, while data warehouses are typically the storage and processing infrastructure used for data mining.

This blog post explains how the data mining process works and the benefits of how an automated data warehouse make data mining easier.

Putting Your Data To Work With the Best Data Mining Techniques

Organizations today are able to gather data from all kinds of sources; their CRM, ERP, and social channels to name just a few. This quickly snowballs into a tremendous amount of data, which in the past would have been almost unmanageable.

So how do organizations make sense of this data? To manually go through it and find patterns, relationships and trends would be impossible. This is where data mining techniques come in. Data mining is the automated process of analyzing large data sets to find these patterns, relationships and trends and ultimately to generate business insights – which will be used to solve challenges and identify new opportunities. Data mining techniques combine multiple disciplines including machine learning, statistics, and database systems. Using the correct data mining tools, organizations can use past patterns to predict future behaviors and results.

Leveraging a data warehouse – where the data from the various sources is combined and stored – data mining is used throughout the organization, from sales and marketing applications to research, product development and finance. Data warehousing and data mining are the cornerstones of modern business decisions.

What's the Difference Between Data Mining and Data Warehousing?

The concepts of data mining and a data warehouse are often confused. While closely related, they each have their own specific roles to play when it comes to dealing with large amounts of data.

A data warehouse, which can be on-premises or in the cloud, is a system that collates data from a wide range of sources within an organization. Data warehouses are used as centralized data repositories for analytical and reporting purposes. Business Intelligence (BI) tools can then present this data visually, allow querying of the data, and assist in making specific business decisions.

Data mining is the process of extracting useful patterns from a large amount of data. Data mining techniques can be carried with any traditional database, but because a data warehouse contains quality data that has already been sanitized and tested, it makes sense to have data mining over a data warehouse system.

With data mining, not only can answers be found for specific business decisions, but also broader trends, patterns, and previously unseen relationships can be discovered that can drive present and future business moves.

Examples of Data Mining Techniques

The data mining process can be used in every business unit to drive data-oriented decision making.

An example of data mining:

Walmart decided to combine the data from its loyalty card program and its point of sale systems. This data was mined, and a number of correlations surfaced. Many of these were not unexpected, such as people who buy gin are likely to buy tonic. There was one correlation that did not make any sense: the correlation between the sale of diapers and beer.

After much investigation, it was discovered that men would come in to a Walmart to buy diapers, particularly before the weekend, and while there would also pick up some beer supplies. As no one could have predicted this correlation, no one would ever have asked this question in the first place which also by the way shows a key difference between data mining and querying a database (including a data warehouse).

So Walmart started stocking beers near the baby section, and sales of both diapers and beers went up. Now this is a great story that demonstrates the power of data mining. This particular one may not be true, but is a wonderful way to illustrate the point.

The data mining process can be leveraged in all industries. Other examples of data mining functionalities include:

In the banking and insurance industries, data mining techniques can be used to examine fraudulent transactions, and based on past occurrences predict future fraudulent activity.

In retail sales, data mining techniques can be used to analyze customer data, product data, and sales, and predict which products to send to which stores in a widely distributed geographical area.

Data Mining Tools

The process of data mining includes various components.

  1. Preprocessing, data cleansing and preparation: this step ensures that the base of data from which you are working from is solid and without errors that could affect the outcomes of the data mining process.
  2. Association rule learning: this step searches for relationships and correlations in the data set.
  3. Clustering: here,  similar groups or clusters are identified.
  4. Classification: in this step, the process will try and generalize known structures to apply to new data points.
  5. Regression. a technique that is used to predict a range of numeric values when given a data set.
  6. Summarization: provides a representation or report of the findings.

A number of data mining tools exist to assist in discovering key organizational insights. These include open source data mining tools such as Rapid Miner, Orange, Weka and Knime, along with products from the likes of IBM and Microsoft. For more, including a data mining tutorial, check out this post.

Good Data Mining Starts With the Right Data Warehouse

The data mining process, however, doesn’t come without its risks and challenges. The key element here is that the data upon which the mining is based is complete, valid and accurate.

So good data mining practice is to ensure that your data warehouse is optimally set up. This means that everything from the process of extracting, transforming and loading (ELT) the data must be set up correctly, testing must be done on the data, and the right data warehouse for your business needs must be chosen.

Data mining’s power is undermined if the underlying data is shaky or inaccurate. The benefits of a data warehouse mean that reliable data is readily available, and data mining can be performed quickly and accurately – even on the largest data sets.

Even more powerful, is an automated data warehouse. This is a smart cloud data warehouse that automates the collection, modeling, and scaling of any data.

Get the Advantage Over Competitors

If it’s used correctly, data mining combined with the right data warehouse can give you a phenomenal advantage over competitors.

The right data warehouse starts with Panoply. Panoply is an autonomous data warehouse build for analytics professionals by analytics professional.

Get a free consultation with a data architect to see how to build a data warehouse in minutes.
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