Basic Data Mining Definitions

I am often asked to define some of the commonly used analytics terms. I have heard "data mining" used to refer to things from screening, to sorting names in excel, to querying from data marts. Below are a handful of some very common terms and how I define them.

Analytics
A broad set of mathematical tools used to reveal trends and patterns and harvest additional value from existing data. Analytics departments generally have the following services:

• Descriptive Analytics: Analyzing constituencies to understand core segments according to behaviors and demographics. Also, analyzing programs to understand performance and the key factors and metrics impacting this performance.
• Predictive Analytics: Using internal and/or external data to predict behaviors and segment constituents according to probabilities.
• Decision logic / Decision Support: Metrics-based forecasting and simulation studies to determine database potential, capacity or philanthropic potential of constituent segments, and investment priorities.

Data Mining
Finding useful information by identifying patterns and trends within data--typically in large databases. Often this statistical pattern recognition is married with predictive analytics to produce predictive models.

Predictive Modeling
An outcome of predictive analytics, predictive models are formulas producing probability scores predicting future behaviors. Typically, these are built using statistical tools such as regression analysis, decision trees, and neural networks.

Competing on Analytics

Here is a book I am eagerly anticipating. Thomas Davenport wrote an article of the same name for Harvard Business Review in January of 2006. I found the article enormously helpful for equipping researchers to make the case for building internal analytics programs. I will circle back to write a review in upcoming months.

The New York Police Department does it. The Harrah's casinos in Las Vegas do it. And businesses like Netflix are built entirely on the basis of it. It, in this case, is using the sophisticated analysis of data -- or "analytics" -- to drive decisions. As a concept, analytics is neither new nor complicated. Any dieter standing on the bathroom scale can attest that numbers are a more reliable source of information than intuition or a spouse's kind opinion. You might feel fit, but the numbers don't lie.

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Does Data Mining = Privacy Concerns?

Data mining has many people concerned about privacy considerations. However, data mining as a technique should not be the concern. Instead, the data used for data mining should be held to the highest legal and ethical standards. Nonprofits are governed by legislation preventing use of sensitive information such as private credit data. Prospect researchers generally follow strict ethical guidelines about using data only where relevant to the relationship.

The majority of information in a nonprofit database is transactional giving data, relationship history, organizational activities, and contact information. By using this internal data, most organizations can very powerfully identify closer constituents and those most likely to respond positively to engagement activities. Additionally, it is possible to identify constituents preferring not to receive our communications.

Although the use of data mining in fundraising is less of a privacy concern from my perspective, it is important to stay current on the debates. Here is an article discussing this privacy debate.

The future of computing will feature devices that monitor you, anticipate your actions and chronicle your life. The problem: Privacy concerns.

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Viral Marketing Ideas

With the increase of innovations in nonprofit communications, many of you might be interested in this "Hall of Fame" of viral marketing ideas.

Includes creative samples and results data for viral efforts targeting organic moms, Hong Kong’s Gen Y, America’s Gen X, and tight-focus biz professionals. Plus, a nifty way to get celebrities more involved in fundraising.

Read the article

Good Data and Experienced Analysts

Custom-engineered models, created internally or by data mining professionals with thorough understanding of the specific context, are superior because of two factors:

• Knowledge of the data
• The human element

Analytics requires a good deal of art. To say a data miner is a "technical person," overlooks a much of their value. These professionals use statistics as a means. What drives them is, "Why do people give to us?" and "Who else fits this profile?" They are fundraising strategists with a unique perspective. Soon, they will be irreplaceable to your organization.

This article discusses the value of good data and the human element.

"One extravagant claim is that experienced human analysts will no longer be required," Wheaton says. "The problem is that it is easy to write software to identify statistical patterns in the data. But, it is a lot more difficult to figure out which of these patterns makes business sense and will hold up over time."

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"Anonymizing" Technology

I find many non-profits all to comfortable in the transmission of sensitive data. When I work with clients on analytics projects, I ask for anonymous records and use secure transmission methods. As you build your data mining programs in fundraising, it is important to take donor privacy very seriously.

This article describes an advance in "anonymizing" technology. I expect these advances will show up in screening and data enhancement services for fundraising in the near future.

Banks typically send customer information to data aggregators, which match it against demographic and lifestyle data about those customers, such as which magazines they subscribe to. The information is sent back to the banks, which use it to profile their customers to help with product up-selling and retention. Banks that use Anonymous Resolution are protecting their customers from hackers and unscrupulous employees of data aggregators, Jonas says.

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Why Can't A Computer Be More Like A Brain?

I think most data miners would find this an interesting read. I did.

For 50 years, computer scientists have been trying to make computers intelligent while mostly ignoring the one thing that is intelligent: the human brain. Even so-called neural network programming techniques take as their starting point a highly simplistic view of how the brain operates.

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Opportunity Intelligence

A challenge for most prospectors in fundraising is the transfer of prospects and knowledge to the front lines. There have been many advances using data mining, screening, integrated surveys, efficient prospect research qualification, and profiling to filter and identify new names. However, we still face inefficiencies in realigning portfolios and engaging new prospects.

Umberto Milletti observes similar inefficiencies despite substantial advances in identification technologies. He has developed an approach called "Opportunity Intelligence." I believe it translates well to prospecting.

Opportunity intelligence solutions filter through large quantities of company, market and personnel data, business news, financial filings and other sources, employing techniques, such as natural language processing and semantic analysis to extract meaning from the data. They, then, assess relevance, applying algorithms tuned by expert industry knowledge, and present highly selective results that precisely identify top selling opportunities.

Check it out