Data Mining

Data Mining

Data mining is defined as the exploration and analysis of data with the main aim of discovering meaningful rules, patterns, and future trends. It is thought of as a discipline under the field of data science.

It is quite different from predictive analytics since that aims to give a description of historical data, while data mining concerns itself with predicting possible outcomes in the future. It is through data mining techniques that machine learning (ML) models are built so that they can power modern AI applications. These applications include search engine algorithms as well as recommendation systems.

So many businesses consider data mining as a proven technology and a critical factor in business decision making. Many cases and case studies offer the capacity to mine and analyze data. Shockingly, there are so many implementation failures reported in this field, which has led to businesses misplacing their business priorities and clouding business objectives. While reported implementations are battling through these challenges, some have failed in delivering the right data insights and their usefulness to the business.

As we have already mentioned, data mining helps in uncovering hidden patterns in large structured datasets and then using them to predict future outcomes. Think of structured data as data that is organized into rows and columns. This allows such data to be accessed with ease and their modification to be done efficiently.

If you are smart, you can use machine learning algorithms to mine data for a variety of use cases. This will help you avoid risks, reduce costs, and improve revenues. What you need to bear in mind is that, at its core, it is composed of two principal functions – description and prediction. The description is all about interpreting large databases, while prediction is concerned with finding insights, like relationships and patterns in known values.

How do you decide on the data mining approach to use? Well, the answer lies in what your business objective is and what value creation you intend to drive. When you have a complete blend of business understanding and technical capabilities, you are certainly going to make big data projects, both successful and valuable.

There are five significant elements of it. These include extraction, data storage & management, data access for analysts, data analysis using a wide range of tools, technologies, and software, and data presentation in a more understandable format for the general audience.

Importance of Data Mining

As far as data is concerned, organizations, businesses, and industries share similar challenges. If you ask any organization, the probable response would be that they cannot find the data they need. However, even if they knew where to find it, they might not know how to get their hands on it.

On the other hand, some have access to the data but cannot seem to understand what it means. The worst thing is, the data they need may be readily available to them and they can understand it. However, for some reason, they cannot put that data into good use.

Well, if your company falls into one of the categories mentioned, then understand that there is hope for you. In other words, this is where it comes in to play.

One of the main reasons why it is essential is the fact that it facilitates the conversion of data from its raw form to processed form/ information. It is this processed form of data that is then converted into knowledge one can use when making critical business decisions.

Recently, many companies across the world have faced the problem of data explosion. In other words, there is a sudden surge in data production, causing an increase in demand and a subsequent increase in the amount of information and knowledge. This means that there is a need for this kind of expansion to be quickly, effectively, and efficiently processed into a form that can be consumed. The good news is that it offers that solution. You can think of it as a long-awaited solution.

It is expected that organizations and business enterprises that maintain such massive databases employ the use of it. The truth is, such a sheer size of the database and its corresponding amount of information requires an equal measure of organization and analysis that can only come from it.

It is through data mining that users and analysts can look at the data from varied points of view when conducting their analysis. This will also make it easier to put processed information into categories based on such features as patterns, correlations, and relationships, among others.

Applications of Data Mining

The use of data mining techniques is something that can be witnessed in so many business sectors and industries:
Retail and Service
In the service and retail sectors, the sale of consumer goods and services gives rise to large amounts of data. The primary role of techniques in this sector is to improve the ability of the company to drive proper customer relationships, supply chain, financial, procurement, and core operations management. The most common areas where it is useful in the industry to include:

  • Product Pricing : Data mining plays a critical role in product pricing policies, as well as price models.
  • Promotion Effectiveness Analysis : This is where the company is responsible for gathering and analyzing data from previous campaigns and promotions along with the benefits and costs the campaigns offered the company. What this does is provides insight into the key elements responsible for increasing the success of future promotions and campaigns.
  • Profitability Analysis : Here, data mining goes a long way in evaluating and comparing various branches, stores, and other business units of the company. This way, the management is in a better position of identifying what areas are profitable in the business so that they can make appropriate decisions.
  • Customer Segmentation Analysis : Data mining techniques will help companies look at customer feedback and classify them into segments to identify a sudden shift in demographics, among other segmentation outcomes.
  • Budgetary Analysis : This is where companies are interested in getting a clear comparison of their expenditures, relative to their budgeted expenses. The good news is that the knowledge derived from data mining will go a long way in informing budgeting for subsequent quarters.
  • Inventory Control : Here, data mining plays a significant role in monitoring and analyzing the level of inventory movements relative to lot sizes and safety stocks. Additionally, it also gives more insight into the lead-time analysis.

Just like in the areas of retail and service industry, it is also applied in similar areas of the manufacturing industry. However, one thing you will note about the manufacturing industry is that it uses data mining to inform quality improvement (QI) initiatives.

In other words, data obtained from quality improvement programs, like Kaizen and Six Sigma, are analyzed. The results are then used in resolving product quality issues that the company faces.

Finance and Insurance
If you look at banks, among other financial organizations, it is actively used as a critical component of business intelligence initiatives. However, it is mostly used in risk management. In other words, it is used to determine and subsequently lower the credit and market risks financial institutions are often faced with. Other risks include operational and liquidity risks, which are uncovered using data mining tools.

On the other hand, it is often used to conduct credit analysis of customers. Insurance companies also use these kinds of tools when conducting claims and fraud analysis.

It is important to note that the transport industry is often concerned about logistics, making it one of the areas where it is extensively applied. Because of this, logistics management benefits hugely from the use of data mining techniques. Various states and governments are also using data mining tools for such activities as traffic control, road construction, and rehabilitation, among others.

Healthcare and Medical Industry
Each day, experiments, studies, and research reveal that there are tons of data generated in the healthcare and medical industry. It is through the use of data-mining that these experiments, researches, and studies see the light of day.

One important point to note is that the real estate industry cannot be thriving without relevant information gleaned from property evaluation, thanks to data mining. Here, the bottom line is not entirely on sales; instead, it is on property valuation trends over the years as well as the appraisal comparisons.

Telecommunication and Utilities
Various organizations that deal with utility services are recipients of data mining benefits. For instance, most telecommunication companies do record analysis. The electric and water companies are interested in electricity and water usage, and it plays a huge role in conducting consumption analysis.

Additionally, with the growing popularity of cellular phones, all transactions and information have become a playground for security threats from hackers. This is what spurred the invention of coral systems that are meant to bust fraudsters. It is through data mining that these fraudsters can be tracked down through their cellular usage patterns analysis when doing fraudulent activities.

Data mining and current market

According to Giga’s research, the market has already hit the billion-dollar mark. This is inclusive of services and software used in data mining. In business intelligence alone, it represents about 15% of the market. What is interesting to note is that it is quickly evolving from transitional packages to data mining applications, ERP, integrated CRM, and other business applications.

There are many different data mining products on the market. KDNuggets has a long list of all the companies that offer data mining products. Some of these companies include:

This is probably one of the largest data mining vendors based on the number of market shares it owns. For several decades, SAS has been in the field of statistics. One thing you should note about the SAS base is that it provides a wide range of statistical functions you can employ when performing a wide range of data analysis tasks.

Additionally, the SAS Scripting language is one of the most powerful. Its enterprise miner was born in 1997 and has since grown into a multi-million dollar enterprise. It offers a range of data mining algorithms like regression, association, decision trees, and neural networks among others. It also offers support for text mining tasks.

Another of the large statistics organizations, SPSS offers a wide range of data mining products like Answer trees and SPSS base. In 1998, the organization acquired ISL (a UK-based company) along with its Clementine data mining package. At the time, Clementine was an industry leader in introducing the data mining workflows that helped its users clean and transform data, as well as train models using the very workflow environment. The good thing about Clementine was the fact that it offered a wide range of tools important in managing it’s project cycles.

IBM has a wide range of data mining tools. With IBM there is a data mining product referred to as Intelligent miner. This tool is composed of visualization tools and algorithms. What this tool does is to export mining models in a predictive modeling markup language (PMML), originally characterized by the data mining group (DMG).

The PMML documents are simply extensible markup language files that contain model pattern descriptions and statistics that can be used to train the dataset. The good thing is, the DB2 database can load these files.

Microsoft Corporation
Of all those present in the market, the first leading vendor to add data mining into their relational database was Microsoft. For instance, SQL server 2000 contains at least two data mining algorithms that are patented. These algorithms are the Microsoft decision tree and Microsoft clustering. Aside from the algorithms, the next most important it’s feature was the OLE DB, which refers to the industry-standard whose major role is to define a language in the same style as SQL, as well as a set of schema’s rowsets targeting database developers. It is through this API that data mining components can be embedded, like prediction features into the user applications.

Oracle 9i was released in 2000 and contains several algorithms for data mining based on the Naïve Bayes and Association techniques. On the other hand, Oracle 10g has a wide range of tools and algorithms for data mining purposes. The other thing about oracle is that it incorporates JAVA data mining API, which is a JAVA package designed to perform various tasks.

One of the data mining tools produced by this company includes the knowledge studio which is well known for performing data mining activities including constructing a decision tree, cluster analysis as well as predictive models. It also plays a significant role in helping users mine data from a wide range of perspectives and to understand it.

Angoss also has powerful data visualization tools that support and explain their discoveries. They also have content viewer controls that work well with data mining algorithms in SQL server 2000. The good thing is that you can plug its algorithms into the SQL server platform.

This is a French company providing data mining software. It offers access to a wide range of data mining algorithms such as regression, segmentation, SVM, and time series among others. They also offer OLAP cube data mining solutions and it is also known for developing an Excel plug-in that gives users a friendly environment for their projects.


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