Cory is the Chief Operating Officer at BlueConic, the world’s leading customer data platform.
The new digital currency is no longer bitcoin. It’s data. The frequency and breadth of activities for which consumers use the internet today have produced vast amounts of it. So much so that many of the world’s most popular and disruptive brands have built their business models around this modern commodity.
Leading brands understand that data isn’t something they have a right to own. Rather, it’s something that consumers are willing to give in exchange for value. In fact, one of the biggest misconceptions today is that the internet is free, when in reality, data is the currency that consumers are willing to “pay for” in exchange for the promise of rewards, experiences and products that are tailored to their unique needs and preferences.
For this marketplace to succeed, however, it’s essential that brands deliver on that promise. The more data consumers provide, the more value-filled the experiences should become. But before businesses can analyze and act on data, they need to actually have it. And data collection is rife with challenges and tradeoffs that must be considered and addressed.
Key Data Collection Considerations
While there is any number of challenges associated with data collection, here are six common hurdles:
1. Ubiquity and volume: Rapid digitalization has contributed to an ever-growing volume of data. With so much data available, it should be easy for marketers to pinpoint who their ideal customer is, what they are interested in and how best to deliver value to them. In fact, 53% of marketers say that “you can never have too much data.” But in reality, too much data can lead to analysis paralysis. Furthermore, marketers need to balance how much data they need to be effective with the security and compliance risks that collecting and storing large amounts of data can impose on their business.
2. Accuracy and quality: Decisions are only as good as the quality of the data upon which they are based. That means data must be constantly cleaned and kept up-to-date. Data cleansing can be hard enough when a company manages data collection in-house, but it’s even harder when second- and third-party data sources are added to the mix. Marketers must have an agreed-upon definition of what quality end data looks like, as well as a rigorous plan and process in place to keep it accurate and up-to-date. If not, their resulting decisions will be flawed.
3. Organizational priorities: How data governance is designed can have a significant impact on what can then be done with that data. Who does data go to first? What are the steps in the workflow? Organizational priorities will likely determine how data cascades throughout the organization. If marketing isn’t where it needs to be in the order of operations, effectively using that data to improve engagement and outcomes becomes much more challenging.
4. Privacy and consent: Up until recently, many brands were collecting data without consumers really understanding how they were using it. This all changed with the rise of GDPR in Europe, CCPA in California, and other laws designed to manage privacy and consent. Mitigating risk means complying with privacy regulations and federating consent across marketing technology systems. Since retrofitting privacy and consent is virtually impossible, marketers must build it into their data collection plans from the ground up.
5. Application: Before collecting data, marketers need to think about what they need the data for. Uncertainty about exactly how the data will be used not only leads to a lack of clarity about what’s important but also makes it impossible to effectively use data to improve marketing strategies and business outcomes.
6. The “Monet” effect: The Monet effect refers to anything that looks one way up close but very different from far away. Applied to data, it means that what’s gleaned from it very much depends on one’s perspective. Effective data utilization means marketers need to understand and accept which insights can be extracted from it and which ones can’t.
Deciding How Much Data Is Enough
The majority of brands today collect some amount of data. But the aforementioned challenges associated with data collection can leave many marketers wondering how much data is enough. While the answer is relative, there are several questions marketers can ask themselves to help guide these decisions:
• What answers do you need from your data? While it may sound rudimentary, this question may evoke eight different responses from eight different people. Gaining alignment and agreement on the questions you’re looking to answer upfront can help dictate the amount of data you ultimately need.
• What use cases are driving the need? So many data projects end up in the graveyard of business priorities because no one ever formally established what it was going to be used for. Developing clearly defined use cases can help uncover what data you actually need in order to accomplish your goals.
• What capacity do your data resources have? With data collection often comes a starry-eyed optimism that it will automatically put an organization on a path to better outcomes. To manage it effectively, data collection must be right-sized to the number of people, processes and skillsets an organization has available to it.
• Can you manage it in a risk-mitigating way? Data can’t just be scraped and left to sit on the shelf (or stored in a data warehouse). Ensuring your data collection doesn’t become a liability means determining if having that data is really necessary, and if so, why.
Data collection can introduce new information, but it won’t result in smarter engagement or better outcomes if it’s not managed correctly. By starting with the fundamental questions, you can spot the gaps in the data you have and what data you need to get to those answers. Putting data collection in the context of the words from tennis player Arthur Ashe, “Start where you are, use what you have, do what you can.”