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What is Data Mining?
Businesses are drowning in data – here’s how to make sense of it all.
The modern business enterprise is awash in ever-increasing amounts of information. Customer profiles, pricing reports, market insights, sales figures and more – it’s easy to understand how corporate decision-makers become overwhelmed by the sheer volume of data available to them. Data mining offers a solution to this urgent challenge.
Data mining is the process by which insights are uncovered in large data sets, often from disparate sources. Advances in data management and enterprise analytics applications have made it easier than ever to sift through both structured and unstructured data in search of meaningful patterns and data points.
While it’s crucial to helping executives make sense of a complex business environment, the data mining process itself is usually managed by skilled IT professionals, statisticians and data scientists. Leadership doesn’t need to run data mining software from the C-suite, but they should be able to describe its value to the business’s bottom line.
From Raw Data to Business Decision
Many corporations are adept at collecting data. It’s relatively easy to assemble customer contact information, transactions, and other records. However, companies often struggle to put countless data points into a meaningful context.
Whether you’re reviewing thousands of transactions per day or sales figures spanning multiple quarters, insightful findings can become buried beneath layers of noise and distraction.
The Retail Perspective
Let’s consider a retailer with millions of customer transactions on file. Presented with nothing more than raw data, most executives would struggle to create a coherent strategy from this valuable information.
Data mining algorithms could assist leadership by viewing transactions through various lenses, including frequency or purchase value. It might also uncover subtle correlations or outlying trends that may be cause for concern. What began as a spreadsheet becomes much more useful, helping your business upsell customers or outflank the competition.
Data Mining Algorithms Uncover Trends & Opportunities
Fraud Detection & Risk Management
Data mining models are also employed to help businesses identify fraudulent activity and manage risk.
The financial sector has long used data mining to uncover suspicious transactions or behavior that fits a pattern of abuse. If these activities are identified early on, measures can be taken to limit damage.
Many lenders rely upon data mining algorithms to assess and manage risk when making lending decisions. In short, there are a multitude of possible applications for data mining software.
The Business Case for Data Mining
While few business leaders would dispute the importance of understanding their data and anticipating threats, new software does represent an additional cost. When making the case for improving your data mining and analysis operation, there are several things to keep in mind:
- Know Your Audience: When crafting a proposal, be sure to speak to the concerns of the people in the room. Aside from the Chief Information Officer, it’s unlikely that most executives will have much experience with data mining. Remember to cover the foundational knowledge necessary to help your audience make an informed decision.
- Highlight the Problem: Simply stating that you need more clarity around data may not impress the C-suite. Instead, identify specific challenges that can only be addressed with data mining.
- Be Honest About Results: Portraying data mining as a black box that takes in numbers and prints out solutions may set you up for failure. While algorithms can arrange information in more useful ways, human beings will still be responsible for making strategic business decisions. It’s also important that business analysts are available to help end-users make the most of the data.
Learn more about how Dun & Bradstreet can help with your customer data mining in Our Data section.
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