Cleaner Data vs. More Data — Which is Better?

The era of “big data” is upon us. Emerging technologies give us access to wide range of data that was unavailable even a few years ago. Integrated data sources, migration to the cloud, mobile apps and an assortment of external data providers have all contributed to a “data glut.” Sometimes, less is more — but sometimes, “more” can be useful, even if the data is not squeaky clean.


The challenge is to recognize when clean data is essential and when more data is beneficial. There is no simple answer to which is better — different industries and even different businesses within each industry must set their own standards, based on the desired results. However, the following three scenarios might help you determine whether you need cleaner data or more data.

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If the Data is Inaccurate, Your Company’s Reputation Can Suffer

There are times when your data must be completely up to date and accurate. Suppose you want to be proactive at averting inconvenience to your customers. One excellent example would be an airline that wants to help customers avoid being stranded at an airport during a blizzard. The airline could use technology to monitor weather conditions and send alerts to customers. Perhaps the airline would like to offer customers the opportunity to take an earlier flight. Data has to be completely accurate. Imagine the repercussions if the airline sent an impending blizzard notice to a passenger who is scheduled to fly out of Miami or offered to book them on an earlier flight that was already sold out. In addition, the weather data must be reliable — perhaps the storm’s path shifted, and the bad weather will not make it to within 500 miles of the airport. In situations like this, more data is not the answer — your data must be impeccably clean.

You Need General Information for Your Marketing Efforts

At the other end of the spectrum, you might need to amass all of the data you can to help you define your customers. What are their ages, education levels, marital status, and income? How many pets do they own, how many children are in the home and what type of vehicles do they own? How often do they make online purchases, and do they make these purchases from a mobile device or desktop computer? In this scenario, you are trying to build a silhouette of your average customer. Perhaps you are trying to increase sales by a modest amount, such as 3 or 4 percent. Relatively “dirty” data is typically better for such situations. You can access a wide variety of attributes to find commonalities that can help you tailor campaigns.

You Need to Target Specific Customers

At times, you may need data that is both plentiful and relatively clean. These situations call for data that falls in between the first and second scenarios. You need it to be as accurate as possible, but no great harm will be done if a bit of dirty data is included in the mix. Suppose you are a magazine publisher who wants to offer former subscribers a discounted rate for returning to your publication. Obviously, you do not want to annoy current subscribers by making it apparent that their business has been overlooked. At the same time, if you offer a special rate for renewing a subscription to someone who has never subscribed to your magazine, no great harm is caused.

So — Which is Better?

The best answer — truthfully, the only answer — is that it depends. You must analyze your goals and the impact that choosing one over the other could have. However, it is always wise to take advantage of opportunities to scrub your data. For example, if you are planning a Salesforce integration, cleaning your data can make the process easier and faster — and you will have the security of knowing that the accuracy of your data has been enhanced.