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The Data Mining Process: How Does It Work?
Data mining is the process of extracting meaningful information from large databases. Here, we’ll explain the six primary stages of the process.
The use of big data has become critical in the modern world. Organizations analyze data to learn about different markets, improve the customer experience, predict sales trends, evaluate performance, and complete other functions. One way of gathering this vital information is through data mining.
Data mining is the process of collecting, cleaning, and processing huge amounts of information for business intelligence or knowledge discovery in databases (KDD). People and businesses can extract information from data sources including relational databases, information repositories, websites, and data warehouses like those owned by Amazon and Microsoft.
Each day, users across the world create images, documents, videos, and other content to post on the internet or save to their devices. Automated systems also generate a lot of data, such as those used in financial interactions, the Internet of Things (IoT), and other sensor technologies. The number of records Google has indexed is currently in the billions and is expected to continue increasing in the foreseeable future.
Despite the abundance of data on the internet, it is often unstructured or in formats that aren't suitable for processing. These elements of data may also be stored in different databases or websites, making analysis difficult.
The data mining process:
1. Research
Before engaging in a data mining process, you must first understand the problem you seek to address or solve. Some business questions to ask include:
- What goal are you seeking to achieve?
- What type of data will you require to address the problem?
Identifying your business and data mining goals will ensure you stay focused rather than divert your attention to other issues. A perfect example of a business goal is to study consumers’ online purchasing decisions. With this objective, you know you need to collect data from various e-commerce businesses. You can look at how long customers stay on e-commerce websites or view popular products and analyze cart fulfillment or abandonment rates.
Here is a visual representation of the Cross Industry Standard Process for Data Mining model (CRISP-DM):
2. Data collection
In this phase, you need to evaluate and collect data that is relevant to your business objectives. You may need to use software tools (e.g., web crawlers) or specialized hardware (e.g., sensors).
Alternatively, you can use traditional data collection techniques like questionnaires and user surveys. However, such traditional methods are discouraged since they are time-consuming compared to modern technological approaches.
While collecting data, you should also determine where to store it. For instance, you can save data in Excel, CSV files, or a database. Whichever storage medium you choose, ensure you can easily retrieve the data when required.
3. Data preprocessing
The raw data you collect is usually not ready to be processed directly. It may be in unsupported formats or structures or contain missing values, which can affect the overall data mining results. The dataset may also contain mixed information. For example, you may find data on politics though your focus is on e-commerce businesses.
The preprocess or data preparation phase ensuring that you have the right data consists of the following steps.
- Data cleaning. In this stage, you need to deal with outliers, which are basically missing, abnormal, or incorrect entries. You may need to drop certain attributes or values and estimate missing entries for consistency.
- Data integration. Data integration or aggregation involves combining data from different sources into a single file for easier analysis.
- Feature extraction. In certain cases, you may collect and end up with large volumes of data. It's your responsibility to sort through this data to identify features that are relevant to your objective. To do this successfully, you must have a solid understanding of your business and data mining objectives.
- Data transformation. Data mining algorithms perform well when you have consistent data. However, the final result of the data collection stage is usually high-dimensional data, which may need to be clustered or otherwise transformed to make it better suited for data analysis.
4. Model estimation
A data mining model analyzes raw data, allowing the identification and understanding of different trends and patterns. To illustrate, imagine you have raw data or real-time information about customer reviews on different e-commerce products. You can use a data mining model to determine how customers react to different items. Data analysts can also use an association rule to find products that are purchased together.
Each data mining process is unique; therefore, each requires a specialized model to be evaluated well. The two major modeling techniques used in data science are descriptive and predictive.
Descriptive models are quite effective when evaluating different patterns, causal relationships, and consistency in a dataset. You can use statistical methods like time series analysis, regression, and correlations to analyze data to explain what has happened in the past.
On the other hand, predictive models use existing data to forecast what might happen in the future. A perfect time to adopt descriptive models is when evaluating how different factors affect organizational performance. Predictive models are effective for machine learning, artificial intelligence, neural networks, and forecasting. Learn more about descriptive and predictive models from this overview of data mining models.
5. Results analysis
One of the final stages of a data mining project involves interpreting the model’s results. A good data mining model should allow you to make quality decisions quickly. Unless required, avoid complex models that are difficult to interpret; these often need a lot of time and expertise to understand.
As a data mining expert, you can use reports, data visualization tools, and other software to share your findings with other professionals. These experts can ensure that the results are relevant to business objectives or a business problem.
6. Drawing conclusions
Drawing conclusions is an essential part of data mining. This phase is highly dependent on your ability to understand results from the use of data mining techniques. You also need to summarize what you’ve learned from the data mining process and assess the strengths of the models.
Use the results for personal decision-making or present them to the management team to determine how to use the information discovered from the data.
Here are some common data mining applications:
- Marketing campaigns. Data mining can help marketers understand customers’ purchasing decisions, tastes, and preferences. Use this information to improve the customer experience.
- Health care. Medical practitioners can make better diagnoses using information retrieved through data mining. For example, doctors can understand how a disease spreads, what its prevalence rate is, and which medications are effective.
- Retail. Data mining lets retailers identify popular products and stock shelves accordingly to maximize sales.
Connect with a data mining expert
Data mining allows you to extract, collect, and analyze large datasets to identify various trends. Data mining experts can collect and evaluate this data using predictive or descriptive data mining models to make conclusions.
As the volume of data increases, the demand for data mining experts and related services is also rising. Upwork is a great place to meet potential clients or engage independent talent.
If you’re looking for a data mining expert or you’re an independent professional looking for work, get started by hiring a data mining specialist or selling your services on Upwork today.
Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
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