What is Data Mining and Why is it so Important?

The process of data mining is used to detect abnormalities or inconsistencies, patterns, and correlations within data sets to anticipate outcomes. People performing data mining apply a number of techniques to generate important and meaningful inferences that help businesses to boost their revenues, reduce costs, address market risks, gain new customers, and strengthen their relationships with their new customers.

Data mining is the process of filtering out similar data from a large chunk or batches of Big Data using specific software for the purpose. Data mining also involves identifying similarities or patterns between data sets that help businesses gather analytics on user behavior and use those analytics to develop effective marketing strategies.

Another great aspect of data mining is that it can efficiently predict future trends that businesses can leverage and use to make informed decisions. With the global trend of digital transformation sweeping across industries worldwide, data mining has gained traction among marketers seeking to be future-ready and solve business problems.

Businesses struggle with numbers as they have to deal with large volumes of data, which cannot be interpreted often. Unstructured data, which accounts for 90 percent of the digital information, is useless unless and until you are able to interpret them into meaningful information that can provide vital insights and knowledge to businesses.

With data mining, an analyst will be able to:

• Organize chaotic and repetitive data into meaningful formats.
• Identify facts and figures that really matter to you and use the decoded information fruitfully to get expected results.
• Use meaningful and insightful data to make informed decisions.

You can apply data mining techniques on:

• Information repositories
• Object-oriented databases
• Data warehouses
• Relational databases
• Spatial databases
• Transactional databases
• Legacy databases
• Text databases
• Legacy databases
• Multimedia databases

The process of data mining is a well-structured approach consisting of several phases and steps. It starts with an analyst’s efforts to understand the business goals or the purpose of the data mining activity and ends with the deployment of the findings. We would walk you through the data mining phases:

1. Understanding business goals

   This is the phase when an analyst is made aware of the business and data mining objectives.

   • An analyst needs to discuss with his client to understand why he is looking for data mining and what he wants to achieve. Sometimes, he needs to understand what his client wants because his client may not have a definite idea of what he actually wants.
   • He has to understand the present data mining landscape and include constraints, assumptions, and resources in his analysis.
   • The analyst should define the data mining goal based on situational parameters and business objectives.
   • The plan should be detailed and it should be directed to accomplish both data mining and business objectives.

2. Understanding Data

   This is the phase when an analyst should check the data to evaluate whether he can use this data to meet the data mining goals.

   • The analyst will be given access to data that is available from multiple data resources including flat filer or data cubes.
   • During the data integration process, the analyst may face issues with regard to schema integration and object matching. This is an extremely complicated and tedious process because of the fact that data that is obtained from diverse sources would not relate to each other easily. For example, if there’s a column named cust_no in Table A and another column named cust-id in Table B, the analyst may find it difficult to understand if both these objects belong to the same data set. In such cases, they use metadata to minimize data integration errors.
   • In the next step, the analyst would try to identify the characteristics of the collected numbers and facts. The analyst may choose to explore the facts and figures by finding answers to questions by making use of query, reporting, and visualization tools.
   • The analyst then evaluates the data quality based on query results. In this phase, he has to acquire missing figures as well.

3. Preparing Data

   The information preparation process takes up almost 90 percent of the project time. This is the phase when analysts select, clean, transform, format, anonymize or construct statistics that they have collected from multiple sources. The following processes are typically included in the data preparation phase:

   • Data Transformation
     The success of the process depends to a large extent on data transformation operations.
   • Smoothing
     This refers to the process of removing unwanted details from a set.
   • Aggregation
     This refers to the process of creating a summary. For example, weekly sales figures can be compiled to find the monthly or yearly figures.
   • Generalization
     In this process, the analyst uses concept hierarchies to replace low-level data with high-level concepts.
   • Normalization
     This is the process of scaling up or scaling down the attribute data. For example, you can make data fall within a range after normalization.
   • Construction of Attribute
     The analyst constructs attributes that would facilitate the data mining process. This process leads to the creation of the final set that analysts would use in modeling.

4. Applying Modeling Techniques

   Analysts typically use mathematical models to identify patterns. They would apply appropriate modeling techniques on the final dataset depending on business objectives.

   • Analysts evaluate model quality and validity by creating a test scenario.
   • After this, they would apply the model on the final dataset.
   • The outcomes are then reviewed by all stakeholders to determine if the analysis objectives have been fulfilled.

5. Performing Evaluation

   This is the phase when analysts check if the identified data patterns meet the business objectives. Gaining an in-depth understanding about business requirements is an iterative process and during this process, new business requirements may emerge as a result of data mining. During this phase, project stakeholders make a ‘go’ or ‘no-go’ decision to use the model to the deployment phase.

6. Deploying the Findings

   In this phase, the findings of the process are applied to day-to-day business operations.

   • It is important for analysts to make the findings easy-to-understand for non-technical stakeholders.
   • The analysts create a comprehensive deployment plan detailing out courses of action with regard to data transfer, maintenance, and monitoring.
   • They then prepare a final report highlighting key takeaways such as experiences and lessons learned. This helps them on process improvisation for their future projects.

Analysts apply a broad range of data mining techniques to find relevant data and interpret them as per specific business requirements. Here are the common techniques involved:

Data mining is not an easy task and analysts need to address a lot of challenges to derive meaningful and relevant findings.

• Formulation of queries demands the involvement of skilled and experienced professionals.
• Analysts need to work on large databases, which are quite difficult to manage.
• Analysts may have to modify the existing business practices in order to determine how they can use the findings.
• Results of may be inaccurate if the data set is not diverse.

Heterogeneous databases and global information systems often contain complex integration information that is hard to interpret.

Analysts use diverse data mining tools and in this article, we have created a list of the top 10 data mining software that are commonly used by top data mining professionals:

1. SAS Data Mining

   Statistical Analysis System (SAS) was developed by SAS Institute to support analytics and data management. With SAS, analysts can mine, modify, and manage data from multiple sources and conduct statistical analysis. This platform offers a graphical user interface to help non-technical users to understand data patterns easily. The software supports big data analysis and generates accurate insights and it is appropriate for data and text mining and optimization.

2. Teradata

   Teradata is a licensed enterprise data warehouse featuring powerful data mining software and data management tools. Teradata facilitates business analytics and allows analysts to gain an in-depth insight into critical decision-making data with regard to product positioning, sales, customer preferences, etc. The software also allows analysts to segregate ‘hot’ data from ‘cold’ data and to store rarely-used data in a slow storage portion of the database. The server nodes of Teradata have their own processing power and memory.

3. R-Programming

   The R-programming language is used for statistical computing and graphics as well as for analysis of big data. It supports the implementation of multiple statistical tests. The software allows for efficient data management and features strong storage capabilities. It features a collection of operators for performing calculations on matrices and arrays. It comes equipped with powerful big data analysis tools and even supports analysis through interesting graphics.

4. BOARD

   The Board software supports activities related to business intelligence and analytics. It is one of the most preferred tools used by companies to improve their decision-making process. With Board, analysts can collect data from different sources and organize the data in an appropriate manner to generate well-formatted reports. Board features a very interactive interface and allows users to monitor workflows and performance and to conduct multi-dimensional analysis.

5. Dundas

   Dundas is an advanced data analytics and reporting tool that features an easy-to-use dashboard. The software allows analysts to perform rapid integration and get access to insights very quickly. Analysts are able to discover multiple data transformation patterns through illustrious graphs, charts, and tables. Users can save data in a particular manner in well-defined structures in order to simplify data processing. The software uses relational methods to support multi-dimensional analysis and generates important insights on critical business matters.

6. InetSoft

   InetSoft’s data intelligence software brings together machine learning and business intelligence capabilities. It is based on a flexible data mashup technology that allows customized reports and dashboards to be combined with machine-based intelligence. It is based on a highly-scalable architecture, which makes it a cost-effective substitute of a data warehouse.

7. H30

   This is an open source data mining program that analysts use to analyze data that is contained in cloud-based systems. This software allows users to capitalize on the computing power of distributed systems and promotes smooth and easy production in both Java and binary formats. Analysts can use the R programming language on this software and can leverage its distributed, in-memory processing capability.

8. Qlik

   This is an advanced data-visualization and mining tool that supports diverse types of files and data sources. It provides interactive dashboards along with drag-and-drop interfaces to facilitate interactive data visualizations. The software can instantly recognize changes and interactions and supports data security across a number of devices. It features a centralized hub through which users can share their apps, stories, and analyses with each other.

9. RapidMiner

   Rapid Miner is an advanced predictive analysis system that is written in JAVA. It supports text mining, deep learning, machine learning & predictive analysis. The program can be used to support diverse business and commercial applications, application development, machine learning, training and educational research, etc. Users get access to public/ private cloud infrastructures as well as premise-based infrastructures. It features template-based frameworks that promote accuracy and speed.

10. Oracle BI

    The Oracle Business Intelligence Suite Enterprise edition is a scalable business server that makes BI applications accessible to a larger audience. It supports centralized data access and calculation, allowing anyone to use information in any form within the organization. The BI server supports business processes that need information, intelligent interaction capabilities, financial reporting, ad hoc queries, data mining, OLAP analysis, data mining, and web service-based applications including J2EE and .NET. The program features a fully integrated web environment that supports access, analysis, and multiple systems for information delivery. Each component addresses the needs of different users who need to use the same data in different ways. Notably, all these components are included in a single architecture, which enhances user experience.

• With this process, businesses get access to knowledge-based information.
• Insights allow businesses to make sensible and profitable operation and production-related decisions.
• It is much more cost-effective than other statistical data analysis processes.
• It allows for automated trend projections and automated revelation of hidden patterns.
• The process can be applied in both existing and new systems.
• The process facilitates the analysis of huge volumes of data in very less time.
• Companies may sell critical consumer data to other companies to earn money. American Express, for example, had sold its credit card sales-related information to other companies.
• Applications may often prove to be difficult to understand and work with. Such software demand thorough training and knowledge.
• Choosing the most appropriate tool to meet a specific project objective is a difficult task. This is mainly because of the fact that various tools are based on different algorithms and hence, they function in different ways.
• The techniques may not prove to be accurate and so it may lead to undesirable outcomes under specific circumstances.

Data mining has found practical application and widespread use in a number of areas such as:

1. Healthcare

   Data mining can positively transform the healthcare system in times to come. Data analytics can help in the identification of best practices that would enhance the level of care and make processes more cost-effective. Methods such as soft computing, machine learning, data visualization, and statistical analysis are used to project patient-volume in every category and to ensure that every patient gets the right level of care at the right time. By using different techniques, insurers can also detect fraud.

2. Market Basket Analysis

   This modeling method is based on the concept that if a buyer buys a certain set of items, he is most likely to buy another set of items. The technique helps in determining purchase behavior, thereby allowing retailers to modify their stores’ layout as per their buyers’ needs.

3. Education

   Educational data mining is a new field and it is directed towards determining students’ learning behavior and finding the impact of educational developmental programs. Educational institutions can use this process to anticipate students’ results and to make decisions. Institutions can identify their students’ learning patterns and can develop appropriate teaching techniques.

4. Manufacturing Engineering

   The process can reveal patterns in complicated manufacturing processes and can be used to identify relationships between product portfolio, product architecture, and data on customer needs. It can also be used to anticipate costs and the duration of product development.

5. Customer Relationship Management

   Businesses need information and proper insights to retain customer loyalty and make customer-focused strategies. Using data mining technologies, businesses can identify the areas that they should focus on in order to retain their customers.

6. Fraud Detection

   The process converts raw data into meaningful information and insights. It helps in revealing meaningful patterns that can facilitate the fraud detection process and supports the creation of a model that can detect whether a record is genuine or fraudulent.

7. Intrusion Detection

   Processes applied to mine data facilitate anomaly detection and this way, it can help in the detection of intrusion. An analyst is able to spot a new activity from day-to-day, common network activity. The process promotes the extraction of data that is more appropriate to address certain scenarios.

8. Lie Detection

   Data mining combined with text mining can help in crime investigations as well as in communication-monitoring of suspects. This process can reveal meaningful patterns in unstructured text. A lie detection model can be created using data samples that are obtained from previous investigations. This model can help in the creation of appropriate process to facilitate further investigations.

9. Customer Segmentation

   Data mining gives deeper insights compared to traditional market research. It allows businesses to categorize customers into certain groups and tailor their services as per their needs. The process can help reveal vulnerable customers, thereby allowing businesses to design special offers for them.

10. Financial Banking

    With data mining, analysts can address complex problems in the banking and finance industry. The process enables analysts to identify patterns and correlations in market prices and business information, which are often difficult to be identified due to huge data volume. These patterns help managers in developing appropriate strategies for targeting, segmenting, acquiring, and retaining a loyal customer base.

11. Corporate Surveillance

    Corporate surveillance statistics are basically used for marketing purposes. This data can be used by businesses to customize their products as per the needs of their customers. The data can be applied in an appropriate manner to create targeted ads on Yahoo and Google on the basis of customers’ search history.

12. Research Analysis

    Mining and analysis of data support database integration, data pre-processing and data cleaning. Analysts can identify similar data, which may cause a change in the research. Data visualization may reveal co-occurring sequences, which may allow analysts to find relationships between activities.

13. Criminal Investigation

    The process facilitates crime analysis. It is an appropriate method for crime data analysis owing to the complexity and large volume of data. With this process, it is possible to convert text-based reports into word-processing files. The information supports the crime matching process.

14. Bio Informatics

    Data mining can be used to extract vital knowledge in the fields of medicine, biology, and neuroscience. This process can be used to find important information about disease diagnosis, gene finding, treatment optimization, protein sub-cellular location calculation, gene interaction network, disease prognosis and diagnosis, etc.

Summing it Up

The purpose of data mining is to explain past events and predict future events. With widespread use in diverse industries such as communications, education, retail, banking, Ecommerce, insurance, and life sciences, data mining has emerged as a leading option for businesses to address key market issues and retain their competitive edge in the industry. If you are running a business and looking for data mining services, we, at ProGlobalBusinessSolutions (PGBS), are always ready to deliver world-class support. Our analysts are adept at the use of advanced data mining technologies and can deliver professional data analytics support, thereby helping you to stay ahead of the competition and make an optimal use of the available data.