Fascinated by the world of data and working with numbers? Today, businesses rely heavily on insights extracted from big data to detect patterns, discover trends, and make better-informed decisions. As a result, many companies looking to set themselves apart in a competitive market collaborate with experienced and knowledgeable data mining professionals to help achieve their long-term objectives.
For those considering a career in the realm of data analytics, having a solid understanding of what data mining entails along with its benefits and practical applications in the workforce could help you plan for the future.
How Data Mining Works
The data mining process is a lengthy and complex one, beginning with the collection of raw data and culminating in the actionable insights companies leverage to drive growth. What is data mining more specifically?
Data Collection and Preparation
In the first stage of data mining, large volumes of data are collected from a wide range of predetermined sources. Some common sources of data include internal sources (such as spreadsheets and databases) and external sources — including social media, third-party providers, and application programming interfaces (APIs).
However, not all data is immediately usable. In order for it to be explored and analyzed, it often needs to be prepared through a process of data cleaning and/or scrubbing. This may entail taking the time to find and delete duplicate data, remove unnecessary noise, and handle missing datasets.
Data Exploration and Transformation
In the data exploration phase, data scientists and analysts may take a precursory look at the dataset they’re working with to gain a high-level understanding of its characteristics and potential relationships. This can also be an effective way of pinpointing potential outliers or other issues (such as missing data) that will need to be addressed before the next stage.
During this step, data transformation may also be carried out, which involves converting all the data into a consistent format. This will make it easier to process and analyze the data based on the chosen model.
Modeling and Pattern Discovery
Speaking of data models, choosing an appropriate model is another critical part of the data mining process. Data models refer to the algorithms used to process data — and the model chosen will have a major impact on the quality of the analysis. With this in mind, data analysts make careful selections in this regard and may even make changes to existing models to tailor the process.
Once data has been run through an algorithm, analysts can begin to see patterns and other trends that could inform business decision-making.
Evaluation and Interpretation
Even after data is processed through a predictive or prescriptive model, data analysts still need to review, evaluate, and interpret the findings themselves. In some cases, this may mean applying a separate dataset to the algorithm to validate the results — or simply assessing and interpreting the information in a way that can be easily communicated to stakeholders and decision-makers.
Deployment and Monitoring
Once a data analysis model has been extensively tested for accuracy and reliability, it may be officially deployed to be used on a regular basis within a company. From there, the model can be used consistently for predictive analytics, reporting, or any other number of applications. Meanwhile, data analysts will continue to monitor data processing models and make changes as needed to improve accessibility and accuracy.
Common Data Mining Techniques
As part of their everyday work, data analysts depend on the data mining process as well as the following data mining techniques:
- Classification is the process of assigning classes or categories to data based on unique features or characteristics. This is done to teach algorithms how to identify different types of data.
- Clustering is a different approach to teaching algorithms, where data is grouped based on similarities.
- Association rule mining is a technique used to visualize relationships and patterns between data points, often using “if-then” rules.
- Regression analysis is a data analysis method used to demonstrate the relationships between variables, which can then be used for predictive analytics purposes.
- Anomaly detection is a process that involves carefully assessing datasets to look for data that deviates substantially from the norm and may indicate the need for additional review.
- Sequential pattern mining is a technique commonly used by data analysts to discover patterns based on patterns or sequences of events that occur consistently.
Applications of Data Mining
No matter where your interests in data mining lie, this is a highly sought-after skill with widespread applications across various industries:
- Business and marketing – Data mining can help personalize marketing campaigns, segment customers, and optimize campaigns for better return on investment (ROI).
- Healthcare – In healthcare, data mining strategies can be used to predict potential disease outbreaks, uncover harmful drug interactions, streamline facility operations, and even improve patient outcomes.
- Finance – Banks and other financial institutions use data mining to inform investment strategies based on market trends, detect potential signs of fraud in real time, and perform risk assessments for credit or loan applicants.
- Retail – Businesses in retail can use data mining to better manage their customer relationships and predict increases/decreases in inventory demand.
- Manufacturing – Data mining can help prevent machine downtime using predictive maintenance as well as optimize different aspects of the supply chain.
- Education – Schools can use data mining techniques to make predictions about student performance and personalize learning experiences to suit different styles of learning.
- Government – Some government agencies rely on data mining for crime prevention, intelligence, and even resource management applications.
Benefits and Challenges of Data Mining
As with many data analysis techniques, there exist some inherent advantages and potential drawbacks of data mining.
For one, many organizations use data mining as a means of improving their decision-making processes. With the insights gained from data mining, they can make more informed decisions that may help them achieve long-term goals while maintaining a competitive advantage over businesses not leveraging data to their benefit.
On the other hand, data mining accompanies certain concerns as it relates to data privacy and ethical use (namely, the potential unethical misuse of its insights). Likewise, data mining requires a great deal of technological resources that can be costly to implement and maintain. In addition, if data mining processes aren’t followed correctly, the results can be unreliable or inaccurate.
Tools and Technologies Used in Data Mining
Data analysts across industries (such as healthcare) leverage a number of tools and technologies to perform data mining, especially as rapid advancements in artificial intelligence (AI) and machine learning capabilities take shape. Other examples include:
- R and Python – Programming languages commonly used for analysis and modeling
- RapidMiner and KNIME – Visual workflow tools for non-coders
- SQL and NoSQL databases – Structured and unstructured data storage solutions
- Weka and Orange – Open-source tools with machine learning libraries
- Big data platforms (Hadoop, Spark) – For large-scale and real-time analytics
The Future of Data Mining
Those preparing to enter the field of data analysis and data mining will need to adapt to emerging trends and technologies. Most notably and as mentioned above, the use of AI and machine learning in data mining is already making modeling and analysis more efficient than ever before. It may even be possible for these technologies to automate much of the data mining process, empowering data analysts to focus on other important areas of their work.
What Is Data Mining? Learn More at JWU Online
Executed ethically and backed by the proper techniques, data mining can be a highly effective way to help businesses find meaning in even the largest, most complex datasets. From there, organizations may capture valuable information and insights to make informed decisions, streamline operations, and gain a competitive advantage in their respective industries.
If a career in data mining and analytics appeals to you, then advancing your education with a master’s degree in Data Analytics from Johnson & Wales University (JWU) could be a practical next step. Or, taking a more business-centered approach to data analytics, JWU Online also offers a Master of Business Administration (MBA) in Data Analytics program.
Both programs are offered entirely online and designed to be completed in about two years, with plenty of faculty support and resources guiding your way. Reach out to learn more about any of our programs, or start your online application for admission today!
For more information about completing your degree online, complete the Request Info form, call 855-JWU-1881, or email [email protected].
