As data is used in more intensive ways for more complex use cases, data quality grows increasingly important. It also becomes more difficult to collect as the number of data sources increases, and barriers to complete data collection appear, such as browser-based tracking prevention measures.
Data quality underpins initiatives to achieve personalization, proper marketing attribution, predictive analytics, machine learning and AI applications. How can you break through data quality myths and start to build confidence in your data quality?
Data quality guide
How to break through data quality myths and get high-quality data for your analytics use cases
Data quality truths: myths busted
One of the first steps toward adopting a data-quality mindset in your organization and getting high-quality data for answering your pressing business questions is to break down misconceptions about data quality.
Here are some of data quality truths that you don’t need to learn the hard way:
No such thing as one and done
Achieving data quality is an ongoing effort to measure how accurate and complete your data is - and how accurate and complete you need it to be. Don’t be fooled by the idea that this is something you can clean up once and forget about. Your analytics results are only going to be as good as the data you derive insight from, and this is a process.
Data quality is not easy
Data teams will agree that achieving accurate and complete your data can be a challenge. The “how” behind your data collection matters at least as much as the “what” you collect.
The more structured your data, the better
The more structured you make your data collection, the better. You should not just vacuum data up indiscriminately without defining structures or schemas for it first.
Quality data does not mean perfect data
When we talk about “data quality” we do not mean that you end up with “perfect data”. What it can mean is that you strive to get “the most accurate and complete data possible”, which will depend on what data you are able to collect.
Data quality is not a standalone problem
A lack of data quality may exist for a number of reasons. Maybe the organization as a whole does not recognize its importance. Maybe there are technical gaps, such as not having the right data team or even enough control over how you collect data. Maybe the organization suffers from overconfidence in the data. Perhaps, like many organizations, data is siloed and/or messy and cannot easily be accessed by the right people.
Data quality, on a surface level, is intertwined with a number of both data-related and organizational culture problems. More fundamentally, data quality is not a standalone problem because bad data leads to misleading and expensive conclusions. And these relate to the overall culture because misleading conclusions can lead to mistrust or loss of confidence in the data team.
Why is data quality important?
Snowplow Co-Founder, Yali Sassoon, has talked and written about data quality for years, telling the awkward truth that no one’s data is perfect, but that even from imperfections we can learn and improve. Our conversations with real-world Snowplow users and data teams have surfaced data-quality challenges along the lines of what we’ve shared above:
- Inadequate data culture and willingness to ignore bad or inconsistent data
- Organizational erosion of confidence in data and data team
- Operational issues, such as the difficulty of working with bad data, the need for complete data, difficulties around missing or bad data.
These challenges are important because they show what barriers exist to developing a data-quality mindset, and highlight why data quality is important:
- Working with poor quality data is time consuming and resource intensive
- Bad data hinders getting desired insights, leads to incomplete or erroneous insights, and erodes confidence in what the data team is doing (both within the team and the wider company).
- Companies’ investments in data analytics do not yield a good return on investment.
How do you get to better data quality?
Is your data complete and accurate enough to deliver on your data strategies? After you have given some serious thought to what data quality is and what it means to your business and decision making, actually getting better data quality is your next step. This comes down to focusing on your data collection strategy to gather the most consistent, accurate, and complete datasets for your use cases. Data collection has become less about moving data from one place to another to being able to collect data from anywhere you want to use in your own data analytics use cases.
We’d like to share more about how you can take the next step and optimize your pipeline for high-quality data collection. Take a look at our new data quality guide to dive deeper into data collection and processing with data quality in mind.