Not all data is created equal. Understand the different levels of data quality and how they can influence your organization.
In the race to leverage data as a valuable asset to the enterprise, we are constantly balancing the need for immediate access with the need to refine the information as much as possible. Ideally, we would like to extract the real nuggets of gold from our data, the ones that give us important strategic insights. However, it is also natural (and necessary!) to handle data at various stages of refinement on our way there.
So what does data look like at different levels of quality?
A useful way of thinking about data quality is by splitting it into three distinct levels:
While many organizations have data at both ends of the spectrum, moving as much data as possible to the gold level (i.e. improving its quality) is a goal that will pay dividends for years to come. To do so, we rely on a number of strategies:
Once data transitions to a gold-level status, it offers some additional advantages to the organization. For instance, gold data has a high level of accuracy, which means that it can be used as a trusted input for training machine learning models and business intelligence tools. These techniques allow businesses to better understand their current state and to create segments for current behavior or forecasts that help inform decisions about the future.
As we can see, the data across the organization is handled and accessed by multiple user groups with varied needs. The question then becomes: how can we effectively store these heterogeneous data categories into a single, unified repository?
A potential answer is to employ a Data Lakehouse, which is a flexible repository that combines the scalability of a Data Lake with the solid integration power of a Data Warehouse. A Data Lakehouse has the advantage of allowing data to be organized in layers, which can be directly mapped to the bronze, silver, and gold quality categories above.