We live in an information-rich world where the amount of data being produced plays a significant role in ensuring the transformation of businesses for good. Knowing how to make the most out of this data has become an obsession amongst organizations and the sheer volume of the data creates a challenge on how valuable insights can be derived from them.
Data Mining which can be a useful process in finding correlations, patterns, and trends from large datasets using recognition techniques to summarize the results into the logic that is helpful to the data owner has become a way used by data analysts for getting this much-needed insight.
This write-up will discuss what Data Mining is all about, show its component architecture and ultimately highlight the pros and cons of Data Mining.
Table Of Contents
What is Data Mining?
Data is unquestionably valuable. However, analyzing it is not easy. With the exponential expansion of data, a technique to extract relevant information that leads to usable insights is required. This is where Data Mining comes into place. Data Mining acts as the backbone for Business Intelligence and Data Analytics.
Data Mining can be defined as the process of analyzing large volumes of data to derive useful insights from it that can help businesses solve problems, seize new opportunities, and mitigate risks. It can be leveraged to answer business questions that were traditionally considered to be too time-consuming to resolve manually
It is the process of finding patterns in large volumes of data to translate them into valuable information. Data Mining Tools help you get comprehensive Business Intelligence, plan company decisions, and substantially reduce expenses.
By using a range of statistical techniques to analyze data in different ways, businesses can seamlessly identify patterns, relationships, and trends. For example, the world’s most popular streaming platform, Netflix, has approximately 93 million active users per month. The data pipeline of Netflix captures more than 500 billion user events per day. This includes data on various things such as video viewing activities, error logs, performance reports, etc.
The storage of this data requires approximately a storage space of 1.3 Petabytes (1 Petabyte = 1,000,000 Gigabytes) per day. The advantages of having such high volumes of data are as follows:
- It allows Netflix to plan its future releases by analyzing the kind of content viewers like.
- It allows Netflix to understand how they can make the user experience on their website and Android/iOS applications better by analyzing user behavior on these services.
Data Mining results in the extraction of relevant intelligence that are useful to organizations in establishing relationships, solving problems, predicting trends, discovering new opportunities, finding anomalies, showing correlations, and mitigating risks. These patterns are discovered through the use of statistical and mathematical algorithms from using Data Mining techniques and tools. There are pros and cons of Data Mining, based on the application it is used accordingly.
Data Mining processes are applicable in various industries including banks, healthcare, retail, manufacturing, sports, etc.
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Key Benefits of Data Mining
- Pattern Discovery: Automatic pattern discovery is a strategic advantage, and this technique helps in modeling and predicting future behavior.
- Trend Analysis: Understanding trends keeps you up-to-date with current developments in the industry, and helps reduce costs and timeliness to market.
- Fraud Detection: Data Mining techniques help in fraud detection by discovering anomalies in datasets. This is used to detect which insurance claims, credit card purchases, etc., are likely to be fraudulent.
- Forecasting in Financial Markets: Data Mining techniques are extensively used to model financial markets and predict likely outcomes.
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Data Mining Architecture
Data Mining tasks can be classified into two types namely descriptive and predictive, the Data Mining functionalities of any of these types are used to define the type of patterns that will be discovered.
With any of these types used, the major components of Data Mining include:
- Data Sources: This is where data can be gotten from to be worked on, it could be text files, spreadsheets, or even files found on the World Wide Web.
- Databases: This is one or a set of databases, data warehouses, spreadsheets, and other available data repositories where data can be gotten from.
- Data Warehouse Server: This component is used to fetch relevant records from a data warehouse based on a user’s request for onward processing. It is the actual space where the data is contained when it is received from various data sources.
- Data Mining Engine: This component of Data Mining is very essential as it is regarded as the driving force that handles and manages requests. It uses a functional module to perform tasks such as classification, characterization, association, cluster analysis, regression, ensemble methods, etc.
- User Interface: This component architecture is what interacts with the user. It enables users to interact with the system by describing a Data Mining function or a query through the Graphical User Interface (GUI) thereby, establishing a sense of contact between the user and the data mining system.
- Module for Pattern Evaluation: This component involves the measurement of the interestingness of patterns and communicates with data mining structures to make a targeted search for interesting patterns. It is used to measure how interesting the pattern that has been devised is and a threshold value is usually used to achieve this.
- Knowledge Base: This component forms the base of the overall data mining process as it helps guide the search or evaluate the interestingness of the path formed by discovering interesting patterns. It is relevant as it contains users’ views and data from previous user experiences that might be helpful in data mining processes. The data mining engine can also receive inputs from the knowledge base to make the results more accurate and reliable.
There are enterprise data platforms—like Datavid Rover—that bring all of these elements together into a complete “knowledge engine.”
Pros and Cons of Data Mining
Data mining is a crucial component of a successful analytics initiative as the information generated can be used in real-time analytics applications, advanced analytics applications that involve the analysis of historical data, and Business Intelligence (BI).
Effective data mining also aids in different aspects of planning for your business operations and strategies including the day-to-day runnings, finance, marketing, advertising, risk management, etc but then, it also has its limitations too. There are both pros and cons of Data Mining.
This piece is written to highlight the pros and cons of data mining and this section will bring to your knowledge these disparities.
Pros and Cons of Data Mining: Pros of Data Mining
The following are the pros of data mining:
- Gathering of Reliable Information: It helps companies, governments, and associations in the gathering of reliable information that can be used in marketing, to evaluate policies, and secure procedures for effective campaigns. As more data is collected, the accuracy of data mining is increased thereby providing insights that would have been difficult to get from merely looking at records.
- Used for Effective Marketing and Sales: Data Mining gives marketers the leverage of using its deductions to make better decisions as they will understand their target audience better. This can be done from patterns that discover customer behaviors and preferences, to enable marketers to create targeted marketing and sales campaigns. The sales unit can also use data mining results to increase lead conversion rates and sell other products to their already existing customers.
- Supply Chain Management: With data mining in place, it can be used to make correlations between products, consumers, suppliers, and other aspects of the business as companies can easily spot market trends, thereby, an accurate forecast for the demand of products can be made which in turn will enable the management of inventories of goods and supplies, warehousing distribution, and other operations.
- Better Customer Service: Data mining can be used to discover patterns and trends in user behavior by looking for anything that is repeated in the data. Up-to-date information about clients can be obtained from the data mining process and if a company understands its customers, targeted campaigns can be made specifically for them to increase its sales over time. Customer service issues can also be easily identified with the aid of data mining to meet the needs of the customer.
- Handling of Risk and Fraud: Data mining can help in identifying risks and fraud that may not be detected through traditional means of data analysis as it uncovers difficult patterns which may not be easily noticed. Financial, legal, and cybersecurity risks can be handled properly through data mining and steps/measures to handle such risks can be developed from the results gotten.
- Lower Costs: Data mining can result in cost savings as business procedures and needless spending is reduced.
- Analyzing Very Large Quantity of Data Quickly: Data mining is used in the analysis of data that was once deemed as difficult and veritable outcomes can be gotten from it to move businesses forward. It has become a routine process for companies today to be heavily involved in data mining.
- Increased Production Uptime: Data mining helps businesses make production and operational adjustments that will improve uptime. Predictive maintenance applications can be used to detect potential problems that might occur when using data mining and this can be helpful in the reduction of unscheduled downtime.
Pros and Cons of Data Mining: Cons of Data Mining
The following are the cons of data mining:
- Complex Data Mining Tools: Data mining tools can be very complex and requires specialized skills and training to be able to use them effectively. This discourages small-scale businesses from venturing into data mining technologies as they may not be able to cope with such demands. Also, different data mining tools work with various data mining techniques depending on the algorithms deployed so, the data analyst has to know the correct tool to use for a particular purpose.
- Data Mining Not Infallible: Data mining does not always provide accurate results all the time as it may rely on assumptions before certain patterns may be found. Sometimes, they may be an incorrect data point or a piece of missing information on a database that needs to be accounted for to make a complete analysis, therefore, pre-processing errors may lead to inconclusive and incorrect results. Data mining also analyses data without knowing the meaning of the data, presenting it in various visualizations but needing a user to access and interpret them.
- Security of Data: Data mining does not require a lot of security set up and since companies hold a lot of critical information about customers and employees, there is always the risk of being hacked and information stolen as a lot of data is stored in the data mining systems.
- Privacy Concerns: Data privacy is a major issue with data mining as people are more concerned nowadays that their personal information can be traded or leaked to third parties without their consent. People are afraid that if this information is sold, it can be used to target them into buying specific products, personal information used to create unethical scenarios, or even governments tracking information about its citizens and how they use their devices.
- It Requires Large Datasets to Be Effective: One of the drawbacks of data mining is that it requires large datasets to be effective. Patterns and trends can be obtained from a larger dataset than from a small one since information can be gleaned better when provided with enough data.
- Cost of Data Mining: Data mining can be very expensive as it requires specialized hands to handle the process and, advanced software which can be costly, to make it effective. Depending on the type of data to be mined, the initial investment to obtain such technologies can be on the high side and this is usually a turn-off for small-scale businesses who do not see the need for data mining as they may not also possess large datasets in the first place.
In this write-up, you have seen the Pros and Cons of Data Mining in detail. You have also seen that data mining is an excellent way to optimize the process of analyzing your data to obtain relevant information that can be used to grow your business. Though it has its setbacks, it is a good method to discover behavioral patterns of your customers and when used correctly its pros can outweigh the cons.
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