Every company today is data-driven or at least claims to be. Business decisions are no longer made based on hunches or anecdotal trends as they were in the past. Concrete data and analytics now power businesses’ most critical decisions.
Copyright: venturebeat.com – “Top 5 data quality and accuracy challenges and how to overcome them”
As more companies leverage the power of machine learning and artificial intelligence to make critical choices, there must be a conversation around the quality—the completeness, consistency, validity, timeliness and uniqueness—of the data used by these tools. The insights companies expect to be delivered by machine learning (ML) or artificial intelligence (AI)-based technologies are only as good as the data used to power them. The adage, “garbage in, garbage out,” comes to mind when it comes to data-based decisions.
Statistically, poor data quality leads to increased complexity of data ecosystems and poor decision-making over the long term. In fact, roughly $12.9 million is lost every year due to poor data quality. As data volumes continue to increase, so will the challenges that businesses face with validating and their data. To overcome issues related to data quality and accuracy, it’s critical to first know the context in which the data elements will be used, as well as best practices to guide the initiatives along.
1. Data quality is not a one-size-fits-all endeavor
Data initiatives are not specific to a single business driver. In other words, determining data quality will always depend on what a business is trying to achieve with that data. The same data can impact more than one business unit, function or project in very different ways. Furthermore, the list of data elements that require strict governance may vary according to different data users. For example, marketing teams are going to need a highly accurate and validated email list while R&D would be invested in quality user feedback data.
The best team to discern a data element’s quality, then, would be the one closest to the data. Only they will be able to recognize data as it supports business processes and ultimately assess accuracy based on what the data is used for and how.[…]
Read more: www.venturebeat.com
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Every company today is data-driven or at least claims to be. Business decisions are no longer made based on hunches or anecdotal trends as they were in the past. Concrete data and analytics now power businesses’ most critical decisions.
Copyright: venturebeat.com – “Top 5 data quality and accuracy challenges and how to overcome them”
Statistically, poor data quality leads to increased complexity of data ecosystems and poor decision-making over the long term. In fact, roughly $12.9 million is lost every year due to poor data quality. As data volumes continue to increase, so will the challenges that businesses face with validating and their data. To overcome issues related to data quality and accuracy, it’s critical to first know the context in which the data elements will be used, as well as best practices to guide the initiatives along.
1. Data quality is not a one-size-fits-all endeavor
Data initiatives are not specific to a single business driver. In other words, determining data quality will always depend on what a business is trying to achieve with that data. The same data can impact more than one business unit, function or project in very different ways. Furthermore, the list of data elements that require strict governance may vary according to different data users. For example, marketing teams are going to need a highly accurate and validated email list while R&D would be invested in quality user feedback data.
The best team to discern a data element’s quality, then, would be the one closest to the data. Only they will be able to recognize data as it supports business processes and ultimately assess accuracy based on what the data is used for and how.[…]
Read more: www.venturebeat.com
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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