How many times have you accidentally stumbled across a massive data quality problem that has gone undetected for months or…
We spent a lot of time dealing with data quality issues before we decided to take a stance and build Qualytics. Going through our experiences, we have ultimately identified and defined the Qualytics 8 as the key dimensions that must be addressed for a trustworthy data ecosystem.
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.
Bad data. It sounds simple; it’s just inaccurate data or data that goes to the wrong place, right? Not quite. Even true data can be bad data. It may even be correct in every way— but duplicated or in the wrong field or simply not what you’re looking for. This is indeed bad data. Those small glitches in the system are where huge mistakes can arise. In today’s world that relies so heavily on data, bad data needs to be monitored to prevent it from spiraling into countless financial, operational, and reputational damage.