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.
Qualytics, the leading platform for data quality enterprise solutions, announced the addition of technology maven and business strategist Bill Murphy to its Board of Directors. Bill’s expertise will enable Qualytics to further enhance its product strategy and grow its market share. Bill’s experience in board service includes technology and growth-driven advisory for numerous companies including Accurics, Cherre, Phantom Cyber, iLevel, and Carbon Black.
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.
The Data Quality Platform for the Enterprise, announces Eric Simmerman as Chief Technology Officer (CTO). Simmerman brings nearly 25 years of experience building software products and software teams as the CTO or VP of Engineering at Interos, HealthPrize, Social Tables, FolderGrid and Pascal Metrics. Simmerman has a passion for applying machine learning and data science to risk management which is fundamental to the Qualytics Platform strategy.
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.
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.
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.