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The Qualytics 8 – Consistency

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.

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How Not to Make the Cut for Supply Chain Disasters

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

The Qualytics 8 – Timeliness

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.

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Embrace Your Migration to the Data Cloud

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.

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The Qualytics 8 – Accuracy

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.

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AI and ML at Qualytics

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.

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The State of Data Quality

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.