In today’s fast-paced tech world, data quality is more important than ever. With artificial intelligence (AI), machine learning, big data analytics and the Internet of Things (IoT) advancing, data is the backbone of innovation. But these technologies are only as good as the data they use. Poor data quality can mess up even the best algorithms, leading to bad insights, wrong decisions and big financial and reputational hits. TechNative brought this interesting topic to us in their article, “Desirable Data: How To Fall Back In Love With Data Quality.”
Emerging tech relies on data to work, learn and get better. AI and machine learning need tons of data to train models that spot patterns, make predictions and automate decisions. Big data analytics needs accurate data to give useful insights. IoT devices collect and send data all the time, which is then analyzed to make operations smoother, more efficient and better for users.
Bad data quality can have serious consequences. Inaccurate data can lead to wrong decisions, causing financial losses, inefficiencies and missed opportunities.
At the end of the day, data needs to be findable, and that happens with a strong, standards-based taxonomy. Data Harmony is our patented, award-winning AI suite that uses explainable AI for efficient, innovative and precise semantic discovery of new and emerging concepts, helping you find the info you need when you need it.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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