Data Mining
Quick definition: Data mining means preparing data for insights by finding anomalies, patterns, and correlations.
Key takeaways:
- Data mining – a subset of data analysis – is the first step in preparing raw data for insights and consumption.
- Data mining involves compiling historical data, processing it, and preparing it for future analysis.
- Organizations can avoid potential risks associated with data mining by putting privacy first and having effective value exchanges with customers around first-party data.
- Your data mining process should always align with your data governance strategy and organizational goals.
- Machine learning and artificial intelligence take basic data mining methods and constantly refine them, finding patterns and answers that humans can’t.
The following information was provided during an interview with Nate Smith, group manager of product marketing for Adobe Analytics Cloud.
What is data mining?
Why is data mining important?
How does data mining work?
What types of insights can businesses gain from data mining?
What are the benefits of data mining?
What are the risks of data mining?
What are some best practices for data mining?
How has data mining changed over time?
How will data mining continue to evolve in the future?
What is data mining?
Data mining — a subset of data analysis — is the first step in preparing raw data for insights and consumption. During the data mining process, analysts examine large data sets to identify anomalies, patterns, and correlations.
Data professionals and marketers can employ this useful information for predictive purposes such as forecasting outcomes, cutting business costs, improving customer relationships, reducing risk, and increasing business intelligence (BI) across their organization.
Though there are many different data mining techniques, industry leaders use the term “data mining” interchangeably with other terms like “data analysis” and “data analytics.”
Why is data mining important?
The data mining process takes unstructured data and creates an organized, understandable visualization that is ready for use. Modern business processes that use data visualization, like business intelligence and marketing analytics, require a large amount of data — and not just any data. A set of data must be structured so that it yields useful information.
Data collection is useless if all the information just sits in a data warehouse. Data preparation and data management are essential.
The data mining process helps teams throughout an organization make better decisions. They can more clearly and quickly identify the most valuable data patterns. For example, a business intelligence team uses data insights to identify areas for optimization. An analytics team can use data insights to create predictive models. And a marketing team uses data insights to inform marketing campaigns for better targeting and customer engagement.
How does data mining work?
Data scientists use a variety of data mining techniques. These approaches use different analytical functions, ask different questions, and use different levels of human input or machine-learning algorithms to arrive at decisions. Generally, the most common techniques fall into three main categories:
Descriptive modeling. Descriptive modeling reveals shared similarities in data sets to identify the reasons behind an event or outcome. Some examples of descriptive modeling methods are:
- Clustering – Grouping similar records together to detect anomalies or outliers.
- Association rule learning – Identifying relationships between data points and other records.
- Principal component analysis – Discovering relationships between variables.
- Affinity grouping – Segmenting groups of people with similar goals and interests to analyze behavior.
Predictive modeling. Predictive modeling classifies future events or estimates for unknown outcomes. Some real-world examples of predictive modeling include using someone’s credit score to assess how likely they are to repay a loan or using a person’s past spending behaviors to identify outliers for credit card fraud detection. Examples of predictive modeling methods include:
- Regression – Measuring the strength of relationships between a dependent variable and a series of independent variables.
- Neural networks – Using computer programs and learning algorithms to detect patterns and make predictions.
- Decision trees – Tree-shaped diagrams where each branch represents an event that is likely to occur.
- Support vector machines – Supervised learning models with associated learning algorithms.
Prescriptive Modeling. Prescriptive modeling filters and transforms unstructured data through a process called text mining so that it is ready to be included in predictive models. Prescriptive modeling looks at both internal and external variables to recommend a course of action. Some examples of prescriptive modeling methods include:
- Predictive analytics with rules – Predicting outcomes by developing if/then rules from patterns.
- Marketing optimization – Simulating different types of media in real-time to determine the right combination for the highest possible return on investment (ROI).
What types of insights can businesses gain from data mining?
In data science, there are always risks associated with sensitive data. If an organization puts privacy first and has an effective value exchange with customers around first-party data, it can avoid these risks.
What are the benefits of data mining?
The term data mining can sound invasive, but in reality, it simply means finding patterns and using insights to improve customer experiences. Effective data mining can add significant value for customers because their interactions will go more smoothly and could even become more tailored to meet their needs.
What are the risks of data mining?
The best part of machine learning is how it automates more tedious tasks and enhances your productivity. Additionally, companies use machine learning to optimize their products to make their customer’s jobs easier. For example, Adobe creates machine learning-based features that allow you to spend less time on endless activities, like sifting through massive amounts of data to figure out who your best-performing customers are.
Machine learning can also uncover insights that humans don’t have the brain power to consider.
What are some best practices for data mining?
Whenever there is big data, there needs to be data mining and data preparation. Your data mining process should always align with your data governance strategy and organizational goals. The data scientists doing the actual data preparation should understand how the results from their outputs feed into business intelligence (BI), data analysis, and marketing.
How has data mining changed over time?
The term “data mining” first appeared in the late 1980s and early 1990s, but at that time, it only meant querying databases. There was rudimentary statistics software that could help perform certain tasks like cluster analysis. Now, automation does much of that work. Machine learning and artificial intelligence take these basic methods and constantly refine them, finding patterns and answers that humans can’t.
How will data mining continue to evolve in the future?
In the future, artificial intelligence will take data mining even further. Today, most data mining is done on flat files and structured data. In the future, data mining will incorporate all types of interaction data, whether it is relational or not. It will also be possible to data-mine non-traditional data sets that the industry hasn’t thought about before.
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