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Master Data Management

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 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 your 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

Data mining is powered by algorithms that bring interesting insights to the surface. These complex models search for patterns and correlations that might otherwise be missed. Different algorithms are deployed depending upon the desired output. Predictive models are among the most valuable for businesses, and seek to forecast future behavior based upon the information at hand. You can familiarize yourself with several types of data mining algorithms below.

Classification Algorithms:
Seek to identify predetermined attributes that suggest a certain outcome. For example, an ecommerce retailer may use a classification algorithm to predict which customers are most likely to make large purchases in the future.

Regression Algorithms:
Predict a result based on continuous variables, such as sales figures. Commonly used to create financial forecasts underpinned by daily, weekly, or monthly figures.

Sequential Pattern Algorithms:
Uncover actions that tend to happen in a certain sequence. Often employed to identify purchase patterns in a retail setting.

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:

  1. 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.
  2. 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.
  3. 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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