Quality Data

Data is the fuel that powers business. This page is a high level overview of Quality Data where I explain my Mount Rushmore of quality data, which features four things. Quality Data is Intentional, Accurate, Available and Valuable.

Intentional

Quality Data is Intentional. Your organization does not just happen to hit the lottery of quality data. You are highly intentional about collecting data, processing data and putting it to use. You need some standards about how you deal with data and perhaps have some data-specific positions for dealing with data at the organizational level because many people look at data with a focus on their highly specific use. These roles include Data Architects, Data Engineers, Data Scientist and Data Analysts. These people help data get used by others in the organization.

You should also be aware of relavant data regulations and security standards that apply to your organization based on your size, industry or location. These include GDPR, CCPA, HIPPA, PCI, SOX and others.

Accurate

Quality Data is Accurate. Accurate data is essential to getting value from it because people don't trust data that is inaccurate. You have good quality control processes that you established to ensure data is captured, cleaned, purged of duplicates, uses consistent terminology, correct formatting, etc.

The level of accuracy is dependend upon your organization's standards. However, your data does need to be consistent across your systems and many systems will hold similar data. So when an email address gets changed on the e-commerce system, it should also get automatically updated on the CRM and customer support systems as well. I can't tell you how much time I deal with this very thing.

Available

Quality Data is Available. Data that isn't available, isn't useful. Data that is isolated and unavailable is called a data silo. This happens when data is stuck in a specific app or database of a single department that is never shared, but should be. You need the capacity to get that data out of those systems so it can be used by your team for the benefit of your customers and that is quite often done through a data pipeline or API built by a data engineer.

Valuable

Quality Data is Valuable. At the end of the day the data needs to be providing business value. You understand your customers, your products or services, your team and the business is growing. You are able to personalize offers that people want to buy, spot opportunities and prevent customer churn. If you aren't getting these results, you are likely doing something wrong. Try some of the concepts I explored above.