Data fuels many things: product innovation, artificial intelligence, machine learning, that third Starbucks in your neighborhood, the fifth Dunkin Donuts franchise within a tiny radius. It’s also a well-known fact that marketers at large tech companies such as Google and Amazon – the blueprint for how almost all startups operate - are required to bring data to prove a point or get sign-off on a marketing campaign. But what happens when you unknowingly have poor data, aka data pollution, that is unknowingly costing you millions?
The truth is that if your data is contaminated, your marketing campaigns won’t see the full return on investment you were betting on in order to anticipate your customers’ needs, create a competitive edge and drive business forward, quickly. In fact, per IBM via the Harvard Business Review, dirty data costs U.S. companies $3 trillion dollars per year.
A recent Forrester study, commissioned by Infogroup, found that 90% of marketers surveyed reported that their campaigns were negatively affected by polluted data. That was an unexpectedly higher number than we had thought of prior to the study, particularly as the study also found that marketers had spent nearly twice as much on custom analytics. However, as any data scientist or AI creator is happy to tell you, the underlying data needs to be as perfect as possible.
Here are the 5 easiest ways you can identify polluted data:
Organizations that don’t value quality data and/or do not provide the right strategic and operational tools, are more likely to have data pollution. Companies with analytics-driven cultures, on the other hand, are more likely to sink the time and resources into developing a data infrastructure.
The reason marketers aren’t seeing the ROI they would like too from their technology investments might be due to a lack of a solid data governance initiative. Data governance is comprised of the set of policies that make-up how an organization manages and organizes its data. Ideally, a good data governance policy will set up a sustainable data ecosystem, where data is being constantly updated and enriched.
The internet has provided us with more cat videos than we ever thought possible but it has also given us more sources of data than ever before. The proliferation of vendors in this space means that it has become more important than ever to be sure of where your data is coming from. Data should come from sources that are reliable, and possess clearly defined end-to-end data acquisition, processing, validation and verification processes. Try following a multi-sourced model of data acquisition to help maximize your ROI on data investments.
You can have ‘the best’ or the ‘most accurate’ data, but that is irrelevant if your company is unable to ingest, analyze and/or formulate clear conclusions from the data. ‘Data fluency’ is what makes data worth the investment. Using data to understand and engage your consumer is impossible if you can’t translate your data from numbers into insights.
Inaccurate data is the most easily discoverable sign that your data is polluted. If your email campaigns are repeatedly filled ‘undeliverables’ and bounce-backs, or your direct mail campaigns are “returned-to-sender” you know you have a problem.
As we progress through the age of data to the age of the customer, the need for data that is accurate is more important than ever. So be on the alert for polluted data, ensure that the data quality is as pristine as possible, and advocate for the tools that will allow you to succeed.