Many people confuse data precision with accuracy, but it’s important to understand each and the differences, especially when applied to data quality. Precision is defined as the exactness of the measurement. A highly precise television would reflect minute differences in colors with incredibly high pixel resolution. In data quality, precision assesses the depth of detail that is encoded in the data. To strengthen the definition, one may ask themself, “how tightly can my data be defined?”
Data coverage means that all the right data is available and included. Having full data coverage doesn’t necessarily mean that the entire data set is fully exhaustive or that every value is accessible, but rather that the data is available for a necessary purpose.
Eric Simmerman set out to fix a problem plaguing data science teams while building products at software startups over the last 25 years. Data Quality and Machine Learning brought him to joining the team at Qualytics to help build the solution that he wished he’d had during his career.
According to DataCadamia, a definition of consistency is, “It specifies that two data values drawn from separate data sets must not conflict with each other, although consistency does not necessarily imply correctness.”
Data consistency means that the value is the same across all datastores within the organization. This data belongs together and describes a specific process at a specific time, meaning that the data is not changed during processing or transfers. Without consistency, there is no way to guarantee that when a piece of data is moved it is correct and the same across all places data is stored.
What is the mentality of your data quality team? Are they passive, reactive, or proactive? Are they building a fragile data quality pipeline, or are they building it to be antifragile?
The term big data is thrown around a lot these days, but one of the main areas where this term truly applies is large industrial units (manufacturing facilities, refineries, vehicle assembly plants etc.). With the advent of digital technologies and advanced sensors, the amount of data being collected every day is astounding. This poses several challenges: these datasets are prone to numerous errors and issues.
Timeliness is a measure of how often data is available when it’s expected. It can be calculated as the time difference of when information should be available and when it is actually available. Informed business decisions depend upon consistent and timely information. Therefore, critical measures of data quality include tests specifying how quickly data must be propagated and compliance with other timeliness constraints such as periodic availability.
As the CEO of Red Pill Analytics, I led our company through a journey similar to the one we now lead customers through. We founded the company in 2014 with a focus on building on-prem analytics stacks, which was still all the rage then, with the individual components of those stacks being primarily Oracle products. Although our name was inspired by the revolutionary Matrix film (and exactly one of the sequels) and the metaphor that data can free our mind and offer us the truth, with a nod and a wink we were also acknowledging the color most associated with Oracle.
As mentioned, with Qualytics Compare, you can ensure consistency throughout your data. Our product works for you to identify incorrect data and the root cause for the error. Additionally, with Qualytics Protect, you can capture anomalies in data pipelines and quarantine records; or identify and alert on anomalies in your historical data. With our products, businesses are alerted of problems within their data, so the problems can be solved.
How many times have you accidentally stumbled across a massive data quality problem that has gone undetected for months or…