Top 15 Most Common Data Quality Issues (and how to fix them)

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Top 15 Most Common Data Quality Issues (And How to Fix Them)

Tips and Tricks in Dealing with Common Data Quality Issues

Imagine having clean, good-quality data for all your analytics, machine learning and decision-making.

The inherent characteristic of data is its quality, which will deteriorate even with the most robust controls. 100% accuracy and completeness don’t exist, which is also not the point. Instead, the point is to pick your battles and improve quality to an acceptable threshold.

Let’s look at 15 common Data Quality (DQ) issues in this article from Hanzala Qureshi

Decrease your operational costs by blending the Artificial Intelligence (AI) & Human Intelligence (HI)

Tighter collaboration of AI-HI decreases the operational costs and time to handle your customers.

Conversational AI is a branch of machine learning that understands the user’s query and provides responses to resolve their query. However, this AI has not reached a state where it can solve the complex questions that require the skill, intuition, and empathy of a human to resolve. So, Most companies now realize to provide a great customer experience, it’s essential to augment conversational AI based chat or voice bots with the human agents.

Having AI at the forefront and Human agents as fallback decreases the costs by 30%1 more than just having individual agents.

Read more in this article from Raghu Suram

Why Create a More Data-Conscious Company Culture

The drive to greater transparency in data requires efforts beyond breaking down data silos.

Data has risen to the level of being a key corporate asset, with the speed, confidence, and effectiveness of business decisions increasingly rooted in data transparency and trust. To improve data transparency, chief data officers (CDOs) and other stakeholders must focus on automated harvesting of data assets, data search and discovery and crowdsourced curation for data classification and description.

These capabilities represent a lifecycle of collection, improvement, and reuse that supports enterprise-wide data awareness and transparency. They can be stitched and integrated together using individual technologies, however most organizations prefer to use a SaaS-based catalog that serves as a platform to provide these capabilities and support various user types, from the non-technical to very technical.

Here’s how and why to focus on cultivating a more data-literate workforce by Nathan Eddy

By Exploring Virtual Worlds, AI Learns in New Ways

Intelligent beings learn by interacting with the world. Artificial intelligence researchers have adopted a similar strategy to teach their virtual agents new skills.

n 2009, a computer scientist then at Princeton University named Fei-Fei Li invented a data set that would change the history of artificial intelligence. Known as ImageNet, the data set included millions of labeled images that could train sophisticated machine-learning models to recognize something in a picture. The machines surpassed human recognition abilities in 2015.

Soon after, Li began looking for what she called another of the “North Stars” that would give AI a different push toward true intelligence.She found inspiration by looking back in time over 530 million years to the Cambrian explosion, when numerous land-dwelling animal species appeared for the first time. An influential theory posits that the burst of new species was driven in part by the emergence of eyes that could see the world around them for the first time. Li realized that vision in animals never occurs by itself but instead is “deeply embedded in a holistic body that needs to move, navigate, survive, manipulate and change in the rapidly changing environment,” she said. “That’s why it was very natural for me to pivot towards a more active vision [for AI].”

Find out more in this article by Allison Whitten

---------------------------------------------------------------------------------------------------------------About Leon Gordon?

Leon Gordon, is a leader in data analytics. A current Microsoft Data Platform MVP based in the UK and partner at Pomerol Partners. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data.?

Leon is a Thought Leader at the Forbes Tech Council and also an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor.