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?”
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?
Dive into another Qualytics 8 to learn the importance of Conformity and why organizations should align content to requirements.
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.
How many times have you accidentally stumbled across a massive data quality problem that has gone undetected for months or…
We’re thrilled to announce that we will be attending our first (virtual) conference as a start-up-level sponsor at the 2021 Ai4 Conference. With three days of 200 influential speakers and over 21 industry-specific tracks to discuss the use of AI and ML, it’s an event we can’t miss. If you’re not sure if you should attend, tracks can be customized to personalize your agenda and are built for both technical and non-technical audiences.
Why an AI & ML Conference?
As AI is crucial to the success of Qualytics Data Firewall, we thought we’d take the opportunity to step into the event world and join colleagues, data practitioners, and industry leaders. And as a startup company walking into a relatively new and cutting-edge field, we need to get the word out about not only our product— but also how we are approaching Data Confidence. In today’s world, where data is in line with oil as a resource, we want to share our message: Quality of Data matters, and it matters a lot.
This year, AI usage across businesses is set to create $2.9 trillion of dollars worth of business value. Our product, the Qualytics Data Firewall, similarly uses AI to ensure Data Quality for the industry. It does this through innovative features that take advantage of machine learning and artificial intelligence.
Data Quality is a problem for many. We as company owners and operators make thousands of decisions every day – anywhere from C-Suite to the mailroom – by looking at data that may be in our home-grown or SaaS products, in databases or data warehouses, raw or aggregated to KPIs. As we grow more dependent on data in the modern age, there is a growing need for ensuring that the data we look at is of “some” quality. In this article, we take a 5W1H approach to data quality monitoring.
A century ago, the most valuable resource was oil. Companies rushed to mine the oil, process it, sell it and influence the dependencies on it, ultimately growing the macroeconomy and other industries with the additional mobility gained by consumers. The oil of the 21st century is data.
At its most basic, a firewall is the barrier that sits between a private internal network and the public internet. It was invented in the 1980s and soon became the most important line of defense for organizations against cyber attacks. Its main purpose is to keep dangerous traffic out. We took this concept and applied it to data. This means our Data Firewall’s main purpose is to keep bad data out. We profile and analyze your data, ultimately using our understanding to improve your data’s quality by filtering and quarantining the bad data.