Dataversity

FEBRUARY 13, 2023

The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess Data Quality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.

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Dataversity

DECEMBER 3, 2021

Three big shifts came this year, namely in the realms of consumer data privacy, the use of third-party cookies vs. first-party data, and the regulations and expectations […]. The post What to Expect in 2022: Data Privacy, Data Quality, and More appeared first on DATAVERSITY.

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Dataversity

MARCH 14, 2022

The post When It Comes to Data Quality, Businesses Get Out What They Put In appeared first on DATAVERSITY. The stakes are high, so you search the web and find the most revered chicken parmesan recipe around. At the grocery store, it is immediately clear that some ingredients are much more […].

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Dataversity

MARCH 3, 2021

The post Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance appeared first on DATAVERSITY. They want to improve their decision making, shifting the process to be more quantitative and less based on gut and experience.

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Dataversity

APRIL 1, 2024

This reliance has spurred a significant shift across industries, driven by advancements in artificial intelligence (AI) and machine learning (ML), which thrive on comprehensive, high-quality data.

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Dataversity

JULY 12, 2024

At their core, LLMs are trained on large amounts of content and data, and the architecture […] The post RAG (Retrieval Augmented Generation) Architecture for Data Quality Assessment appeared first on DATAVERSITY. It is estimated that by 2025, 50% of digital work will be automated through these LLM models.

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Analysts Corner

OCTOBER 30, 2024

This section explores four main challenges: data quality, interpretability, generalizability, and ethical considerations, and discusses strategies for addressing each issue. Download end-to-end articles with codes 1.

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Dataversity

AUGUST 1, 2024

Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the rise of the data product. This month, we’ll look at data quality vs. data fitness.

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Dataversity

NOVEMBER 1, 2024

In a recent conversation with one of our customers, my team uncovered a troubling reality: Poor data quality wasn’t just impacting their bottom line but also causing friction between departments.

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Dataversity

MAY 31, 2024

Data: Data is number, characters, images, audio, video, symbols, or any digital repository on which operations can be performed by a computer. Algorithm: An algorithm […] The post 12 Key AI Patterns for Improving Data Quality (DQ) appeared first on DATAVERSITY.

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Passionate BA

DECEMBER 5, 2024

Learn about data strategy pitfalls A few words about data strategy Elements of Strategy A solid strategy outlines how an organization collects, processes, analyzes, and uses data to achieve its goals. You will find my business analysis digest, my articles, and more! Is that your first visit to Passionate BA?

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Dataversity

AUGUST 10, 2023

We’ve all generally heard that data quality issues can be catastrophic. But what does that look like for data teams, in terms of dollars and cents? And who is responsible for dealing with data quality issues?

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Dataversity

SEPTEMBER 16, 2024

Public sector agencies increasingly see artificial intelligence as a way to reshape their operations and services, but first, they must have confidence in their data. Accurate information is crucial to delivering essential services, while poor data quality can have far-reaching and sometimes catastrophic consequences.

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Analysts Corner

DECEMBER 6, 2022

It is important to assess the data to understand their fitness for use and shortcomings, before using the same to derive insights and make decisions. Hope you found this article useful! Future articles on data will focus on data quality dimensions, data quality assessment, and other aspects of data quality and data governance.

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Dataversity

JULY 3, 2023

What are the most common causes of Data Quality issues? The conventional answer to that question includes problems like inaccurate data, duplicate data, or data containing missing values.

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Smart Data Collective

APRIL 5, 2022

Drone surveyors must also know how to gather and use data properly. They will need to be aware of the potential that data can bring to entities using drones. Indiana Lee discussed these benefits in an article for Drone Blog. You will also want to know how to harvest the data that you get.

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The Data Administration Newsletter

FEBRUARY 5, 2025

Unsurprisingly, my last two columns discussed artificial intelligence (AI), specifically the impact of language models (LMs) on data curation. My August 2024 column, The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality, and my November 2024 column, Data Validation, the Data Accuracy Imposter or Assistant?

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Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. Both approaches aim to improve data quality and enable accurate analysis.

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Analysts Corner

DECEMBER 21, 2022

A lack of awareness has led to less competition in the data science field. In this article, we present some interesting statistics around the following, that should help you decide for yourself as to where you are headed for with job in data science : Big Data, Data Science, and Analytics Market; Data Scientist — Job and Salary.

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Smart Data Collective

SEPTEMBER 28, 2022

Big data management increases the reliability of your data. Big data management has many benefits. One of the most important is that it helps to increase the reliability of your data. Data quality issues can arise from a variety of sources, including: Duplicate records Missing records Incorrect data.

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The Data Administration Newsletter

DECEMBER 4, 2024

We have lots of data conferences here. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? Over the years, I’ve seen a trend — more and more emphasis on AI. This is my version of […]

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Dataversity

NOVEMBER 7, 2022

Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best. Gartner reports that companies lose an average of $14 million per year due to poor data quality.

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Dataversity

JANUARY 31, 2025

Data Sips is a new video miniseries presented by Ippon Technologies and DATAVERSITY that showcases quick conversations with industry experts from last months Data Governance & Information Quality (DGIQ) Conference in Washington, D.C.

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Dataversity

SEPTEMBER 25, 2023

In an era where large language models (LLMs) are redefining AI digital interactions, the criticality of accurate, high-quality, and pertinent data labeling emerges as paramount. That means data labelers and the vendors overseeing them must seamlessly blend data quality with human expertise and ethical work practices.

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Dataversity

OCTOBER 25, 2021

It’s common for enterprises to run into challenges such as lack of data visibility, problems with data security, and low Data Quality. But despite the dangers of poor data ethics and management, many enterprises are failing to take the steps they need to ensure quality Data Governance.

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Dataversity

DECEMBER 24, 2020

.” The series covers some of the most prominent questions in Data Management such as Master Data, the difference between Master Data and MDM, “truth” versus “meaning” in data, Data Quality, and so much […].

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Dataversity

JUNE 21, 2021

Building an accurate, fast, and performant model founded upon strong Data Quality standards is no easy task. Click to learn more about author Scott Reed. Taking the model into production with governance workflows and monitoring for sustainability is even more challenging.

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Smart Data Collective

JUNE 22, 2023

One such field is data labeling, where AI tools have emerged as indispensable assets. This process is important if you want to improve data quality especially for artificial intelligence purposes. This article will discuss the influence of artificial intelligence and machine learning in data labeling.

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Dataversity

JANUARY 19, 2024

Do you know the costs of poor data quality? Below, I explore the significance of data observability, how it can mitigate the risks of bad data, and ways to measure its ROI. Data has become […] The post Putting a Number on Bad Data appeared first on DATAVERSITY.

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Dataversity

SEPTEMBER 21, 2021

In my first business intelligence endeavors, there were data normalization issues; in my Data Governance period, Data Quality and proactive Metadata Management were the critical points. The post The Declarative Approach in a Data Playground appeared first on DATAVERSITY. It is something so simple and so powerful.

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Dataversity

JANUARY 8, 2024

However, the sheer volume and complexity of data generated by an ever-growing network of connected devices presents unprecedented challenges. The Internet of Things (IoT) has rapidly redefined many aspects of our lives, permeating everywhere from our jobs to our homes and every space in between.

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Analysts Corner

JUNE 21, 2023

There is still much work to be done to improve data quality and ensure that AI has access to all relevant information without compromising the privacy and security of those involved. If you haven’t watched it yet, why not check out the video that accompanies this article at the beginning?

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Analysts Corner

OCTOBER 31, 2023

Integrated Knowledge Bases — AI Midjourney generated image In this second article in the series, I will explore a possible path for AI advancement that must have a profound impact on our society: access to integrated knowledge bases. Management : monitoring transactional data from business operations to generate indicators at various levels.

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Dataversity

FEBRUARY 15, 2021

This article introduces Force-Field Analysis (FFA) [1], a tool that one of us (Tom) has used for many years to help understand and summarize the impacts of multiple factors in the data space. The post Increasing the Business Impact of Data Management Using Force-Field Analysis appeared first on DATAVERSITY.

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Dataversity

MARCH 21, 2022

If you’re trying to determine whether you need a data lake, a data warehouse, or possibly even both, you’ll want to understand the functionality of each tool and their differences. This article will highlight the differences between each and how […].

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Dataversity

AUGUST 7, 2023

Businesses increasingly rely on real-time data to make informed decisions, improve customer experiences, and gain a competitive edge. However, managing and handling real-time data can be challenging due to its volume, velocity, and variety.

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Dataversity

MAY 7, 2024

Most, if not all, organizations need help utilizing the data collected from various sources efficiently, thanks to the ever-evolving enterprise data management landscape. Data is collected and stored in siloed systems 2. Different verticals or departments own different types of data 3. Often, the reasons include: 1.

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