The Donation Maximization Blog

5 Tips To Improve Data Quality Before Using Predictive Analytics

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As defined by quality management pioneer Joseph M. Juran, data is considered high quality when it is fit for its intended use with respect to: decision making, planning and operations. And were he alive today, there is little doubt that Juran would add “predictive analytics” to the list.

This is because the efficacy, impact and overall value of predictive analytics is fundamentally driven by the quality of data – or in many cases, undermined by the lack of quality. Indeed, the addage GIGO, or “garbage in, garbage out” applies to predictive analytics as directly and meaningfully as it does in all other endeavors, whether we are talking about a poll to identify the front-runner in an upcoming election, medical research in support of a clinical breakthrough, and the list goes on.

Of course, even if you do not yet leverage predictive analytics in your nonprofit, this is not to suggest your data is “garbage”. Most likely, you are leveraging a certain degree of data quality to steer your executive, marketing and sales teams in the right direction.

However, as noted above, to fully exploit the power of predictive analytics – and ultimately get more donors, revenues and profits – it takes more than just basic quality data. It requires high quality data, which is the kind that reliably and efficiently converts raw information into accurate, actionable intelligence.

Below, we highlight 5 tips that will significantly -- and in some cases dramatically -- improve the quality of your data, and therefore help your nonprofit get the most out of its predictive analytics program and investment

1. Use Drop Down Menus

 Whenever possible, use drop-down menus on your forms instead of open text. This will prevent customers from typing (for example) “y” when they mean “yes” or “n” when they mean “no”. This will help improve consistency, which is a major component of high quality data.

2. Standardize Data Input

 When drop-down menus are not suitable, ensure that donors (or potential donors) can only input data one way, such as “MM/DD/YYYY” rather than the dozens of potential variations. Capturing and storing standardized data ensures validity, which is also essential.

3. Use 3rd Party Validation

Inaccurate data will directly lead to flawed analysis – and poor decisions. Avoid this by using a third party to validate data (e.g. use an address validator).

4. Use Required Fields

Make “must-have” data points mandatory, so that donors cannot skip them (accidentally or otherwise). Generally speaking, crucial data points are those that your teams need in order to do their jobs. For example, if your marketing team needs to know a donor's job title/role, then make this a required field. Or if your development team needs to know how soon a donor intends to make a gift decision, then capture this as well. The goal here is to ensure that you don’t just glean data, but that you glean complete data

5. Put "Best Before" Date on Data

Data has a shelf life, and information that was accurate a few months or years ago may no longer be reliable today. Put a time stamp on data so that you know when fields have been last updated. And when the data becomes “stale” (i.e. out of date and of questionable validity), then archive it immediately and focus on generating new, high quality data.

Before leaving this tip on ensuring that your data is timely, it is necessary to point out that based on prevailing laws and regulations, your organization may be required to keep data for a certain period of time. Or your industry may have best practices related to data retention policies. Obviously, these requirements trump development and marketing considerations, and should not be compromised in any way.

The Bottom Line

Cleaning and enhancing data for marketing and development purposes is a MUST for any organization. Nonprofits need to play a role in ensuring that their data is high quality vs. low quality, and the above tips will measurably and rapidly advance you towards that goal. Simply put, the more organized and reliable your data, the more accurate your predictions – and the better your results! Click here to find out how data quality can impact your bottom-line.

 

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