Marketers sit on a mountain of valuable consumer data. But not all data at their disposal is useful. Working with poor quality data poisons your marketing efforts, leads to missed opportunities and ultimately hurts your bottom line.
Bad data is data that’s often been corrupted by circumstance. And it’s more common than we realize. Never intentional or malicious, it’s often a result of human error or improper collection. Sometimes it’s as simple as email addresses changing over time. In other instances, it’s something that breaks in your process. Although the cause can be simple, the effect—gaps and inaccuracies in your analytics causing everything you’re measuring to be ineffective—can be disastrous.
Not every organization has data champions on their team, but as companies increasingly embrace a data-first culture, prioritizing data health will become a must.
Bad data isn’t standardized
In your personal life, there’s usually some way to reconcile data. Let’s say you find a discrepancy in your bank account: you know what you earned versus what you spent, and you can check this against historical data in your bank statements. In other words, you have a source of truth. But in marketing, more often than not, there is no baseline. As a marketer, you of course have some idea of what’s right, but all your data is relative to itself.
This problem isn’t new, it just easily flies under the radar. If you’re using Google Analytics, for example, to track the traffic on all of your web pages, and for whatever reason the script wasn’t tracking 10% of your pages, you simply wouldn’t know that you’re missing 10% of your data. Gaps like this can happen in several ways. But one big way it occurs is through a lack of standardization.
For a SaaS business, measuring “visitors to the site” may not mean the same thing as “users in the platform.” When you’re setting up these metrics across varying analytics platforms, and fragmenting it across several departments—from marketing to sales to engineering— it makes a difference. “Clicks” in AdWords doesn’t necessarily translate to overall traffic since there’s a difference between new users, uniques and total sessions. At scale, you’re pulling data from hundreds of sources. Not standardizing what you measure, yet treating it all the same, is a recipe for bad data.
Bad data is expensive
Whether you’re ignoring the problem because you’re not sure how to fix it, or perhaps you’re not aware of it yet, working with poor quality data affects a lot of the business outside of marketing. If your data is all over the place, it puts a halt on valuable initiatives and hurts your bottom line.
To put this into perspective, because data decays at a rate of 70% per year, bad data costs businesses an average of $9.7 million annually. Harvard Business Review concluded that bad data costs so much because decision makers, managers, data scientists and other team members have to accommodate the discrepancies in their everyday work—hunting down inaccuracies and bad sources, correcting mistakes. Doing so is both time-consuming and expensive.
Beyond the dollars, bad data compromises your strategy, leading to squandered opportunities down the road resulting from uninformed business decisions. Dealing with the mass amounts of data provided through multiple sources, in different formats and at different frequencies is a fragmented process. It’s understandable that marketing departments often lack the manpower to analyze, understand and leverage all this data on an ongoing basis.
Good data is clean
Good data results when you take the time to clean up, verify and organize data so that common issues like outdated information, duplicates or inaccuracies no longer plague your system.
Dealing with this complexity requires dedicated resources and well defined processes and policies for standardization, optimization, reporting and an agile approach. This is a departure from the monthly reporting, quarterly forecasting and episodic insight generation most organizations are used to. But this shift is critical for success in an increasingly data-driven world. A world-class marketing organization should seamlessly fuse data, analytics, strategies, people, processes and capabilities to deliver business results.
If your organization is growing, and you’ve just opened the floodgates to sharing data between departments, look for areas where information can be merged so that you have a more complete picture of the customer. Consider forming a task force, where team members own different parts of the pipeline and champion good data in your organization.
If allocating the resources toward a task force to manually clean up your data pipeline is an unrealistic option for you, consider implementing AI tools. Predictive machine learning can learn your data metrics’ baseline behavior and has the ability to rapidly transform vast tracts of data into trusted business information as well as automate the discovery of anomalies.
Dedicated resources to clean the pipeline fixes the problem at hand, but there’s nothing more protective than applying these principles proactively. Take the time your team would spend course-correcting bad data and swap it for time spent building secure and accurate data processes into your efforts from the start.
Pursuit, not perfection
Being realistic is important. And the reality of bad data is that cleaning it up is a never ending process. The goal isn’t an end state where everything is perfect. The goal is to strive toward habits and processes in your workplace that encourage better data.
That said, data quality is ultimately everyone’s business. Whether or not you work directly with the numbers, data affects every output of an organization. A clean, maintained pipeline means you and your team can cut out erroneous costs for good and more easily pursue healthy data strategies.
Moving marketing towards a true data-first culture can be a long journey. But it’s one that proves its worth.
This piece is part of our series on data-driven marketing in which our experts explore the keys to developing a team and strategic approach grounded in data. Read the first article here.
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