Data mining refers to the process of identifying within a data set patterns, trends, or anomalies. Organizations use a variety of tools and approaches to mine data and extract information that they can use to improve their business.
For modern businesses, data is gold. It's the key to unlocking insights and improving operations. However, just as mining gold is hard work, extracting value from a sprawling data set is no easy task. Looking at surface-level information is unlikely to produce insights. You must employ tools and processes to extract patterns or identify trends.
Data mining doesn't involve removing data from a data set in the way that you might mine minerals from the earth. Instead, it's about examining a data set’s structure and content — as well as the relationships between the data within it — to determine what data to extract and analyze for business insights.
The data mining process
Most data mining operations follow a basic process similar to this:
- Business understanding: Businesses managers identify specific insights they hope to gain from data sets.
- Data understanding: Data engineers define the types of data they plan to work with, and their sources of origin.
- Data ingestion: A data engineer uses an ETL solution to ingest data from assorted sources to a repository from which it can be analyzed. The destination is typically a data warehouse, data mart, database, document store, or data lake.
- Data selection: A data scientist determines which data inside the warehouse is relevant to the problem they want to solve or the question they want to answer.
- Data preparation: The data team cleans, structures, and organizes data to make it more suitable for data analytics and business intelligence.
- Data transformation: Data transformation is the process of changing the format, structure, or values of data.
- Data modeling: Data modeling is the process of describing the structure, associations, and constraints relevant to available data, and encoding these rules into a reusable standard.
- Evaluation and analysis: Data scientists use analytics and machine learning tools to identify patterns or trends in the data.
- Reporting: Data analysts report and share the results with stakeholders, often in the form of data visualizations.
Data mining business applications
Data mining can turn data into value by unlocking information hidden within complex data sets. Among its many applications, here are three of the most common.
Anticipating trends or customers' behavior is a common goal of data mining operations. For example, retailers can examine connections among customers' age, gender, and previous purchases to predict their future behavior and implement personalized loyalty or marketing campaigns. Universities can use data mining to predict which prospective students will graduate or drop out.
Deliver personalized services
In a health care system, data mining can help identify risks, predict illnesses in segments of the population, or predict how long a patient will be in the hospital. Doctors can do a better job of identifying patterns and prescribing treatments when they can analyze the entirety of a patient's medical records, physical examinations, and treatment patterns.
For large companies with complex product development and sales operations, determining the profitability of a given offering is not always clear-cut. Tasks like this require sorting through complex information, such as how many staff hours were spent developing and marketing a product, how long the product will remain on the market, and how much the company spends to support customers who use the product.
Data mining can help businesses identify relevant trends in all of these areas to determine the profitability of a given product. While staff members might be able to make this type of assessment manually, data mining allows businesses to draw profitability conclusions quickly; for example, they could track profitability in real time, as sales and expense data changes.
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Data mining helps organizations to predict trends, provide personalized services and products, and measure profitability. But your data team should anticipate a few common data mining challenges, and also be prepared to implement some best practices.
Data mining challenges
Several challenges can sometimes make it difficult to achieve the desired results using data mining. Some of the most common challenges are:
- Noisy data: A data set is said to be "noisy" if it contains information that's not relevant for a given purpose mixed in with information that is, or if it contains corrupt or poorly structured data. In this type of situation, a data analyst must either extract relevant data from the data set before mining it, or find ways of ignoring the irrelevant data.
- Scalability: The bigger the data set, the more resources required to mine data. Scalability isn't a problem for organizations that host their data infrastructure on a cloud platform that can scale up or down as needs change, but large data sets can put heavy demands on on-premise data warehouses with fixed hardware configurations.
- Incomplete data: Not every data set is complete. For example, a data set that is supposed to contain sales data from the entire business might be missing information from some departments. Ideally, data engineers and analysts would complete the data set by adding this missing information before mining the data. But if that's not possible, they could minimize the impact of incomplete data by noting its absence in their reports, or using the results from the available data to extrapolate on how trends might apply to the missing data.
Data mining best practices
To get the most valuable insights, and avoid the pitfalls described above, organizations should follow a few data mining best practices:
- Preserve data: Include all raw data in a data warehouse or data lake so that it remains accessible for data mining operations. Data that seems irrelevant today may be important for data mining in the future.
- Have a good idea of the insights you seek: Mining data without knowing which types of information are relevant to your business is unlikely to yield actionable insights. Define the types of insights your data mining operations might produce.
- Strive for data quality: You're likely to run into difficulties if you have to work with incomplete data or data with other quality problems. Although some data quality issues may be unavoidable, focus on data quality out of the gate by designing processes that eliminate or prevent duplicate data entries or minimize the chances of inaccurate data entry.
- Recognize outliers: Trends and patterns are valuable, but outliers also are a crucial source of insight. Design a data mining process that reports on the most common features within a data set, and also identifies anomalies within the data, especially when those anomalies are relevant to the business goals.
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When you want to start mining your data, you can simplify the process by using a cloud-based data warehouse and an ETL solution like Stitch, which can load data to your data warehouse or data lake from more than 100 data sources. Stitch provides a secure, easy-to-use ETL solution and a bridge to data mining, data analytics, and business intelligence. Sign up for a free trial and start mining your data now.
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