Precisely

MARCH 14, 2023

Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms. eBook 4 Ways to Measure Data Quality To measure data quality and track the effectiveness of data quality improvement efforts you need data.

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IBM Journey to AI blog

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Alation

MAY 24, 2022

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

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Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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The Data Administration Newsletter

DECEMBER 19, 2023

One of the reasons why there’s always excess production in the textile sector is the stringent requirement of meeting set quality standards. As far as healthcare is concerned, surprisingly, only two out of five health executives believe they receive healthy data through […]

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IBM Journey to AI blog

JUNE 12, 2023

Companies rely heavily on data and analytics to find and retain talent, drive engagement, improve productivity and more across enterprise talent management. However, analytics are only as good as the quality of the data, which must be error-free, trustworthy and transparent. What is data quality?

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Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

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Alation

DECEMBER 7, 2021

Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s data observability platform empowers data teams to discover and collaboratively resolve data issues quickly. Does the quality of this dataset meet user expectations?

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The Data Administration Newsletter

SEPTEMBER 5, 2023

The role of data products has become pivotal, driving organizations towards insightful decision-making and competitive advantage. However, ensuring the achievement of these data products demands the strategic integration of Non-Invasive Data Governance (NIDG).

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Heartbeat

JUNE 12, 2023

The primary goal of model monitoring is to ensure that the model remains effective and reliable in making predictions or decisions, even as the data or environment in which it operates evolves. There are several aspects to model monitoring, including data monitoring, model performance monitoring, and feedback monitoring.

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IBM Data Science in Practice

APRIL 26, 2024

Metadata Enrichment: Empowering Data Governance Data Quality Tab from Metadata Enrichment Metadata enrichment is a crucial aspect of data governance, enabling organizations to enhance the quality and context of their data assets. This dataset spans a wide range of ages, from teenagers to senior citizens.

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Tableau

APRIL 12, 2021

Product Manager, Tableau Prep. Data discovery and trust have been core principles of Tableau Catalog (part of Tableau Data Management ) since its introduction with Tableau 2019.3. With every release, we continue to add features that help users find and use trusted data with confidence. Kate Grinevskaja. Kristin Adderson.

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Flipboard

DECEMBER 27, 2023

Presented by SQream The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how …

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The Data Administration Newsletter

MAY 17, 2022

One of the greatest contributions to the understanding of data quality and data quality management happened in the 1980s when Stuart Madnick and Rich Wang at MIT adapted the concept of Total Quality Management (TQM) from manufacturing to Information Systems reframing it as Total Data Quality Management (TDQM).

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Heartbeat

OCTOBER 30, 2023

Ensuring Long-Term Performance and Adaptability of Deployed Models Source: [link] Introduction When working on any machine learning problem, data scientists and machine learning engineers usually spend a lot of time on data gathering , efficient data preprocessing , and modeling to build the best model for the use case.

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Tableau

APRIL 12, 2021

Product Manager, Tableau Catalog. Data discovery and trust have been core principles of Tableau Catalog (part of Tableau Data Management ) since its introduction with Tableau 2019.3. With every release, we continue to add features that help users find and use trusted data with confidence. Kate Grinevskaja. April 14, 2021.

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Dataversity

SEPTEMBER 14, 2023

In the dynamic landscape of contemporary business, data analytics in product management has become a pivotal driver of success. Data analytics, the systematic exploration of data sets to glean valuable insights, has revolutionized how companies design, develop, and refine their products.

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Alation

DECEMBER 6, 2022

Today I’m thrilled to share that we’ve added two new, seasoned leaders to the Alation Product Team. Diby Malakar – VP Product Management, Cloud & Technical Metadata Platform. Diby joins us from Confluent, where he was one of their product leaders during their hyper-growth phase through IPO.

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Dataversity

MAY 3, 2023

AI ethics are a factor in responsible product development, innovation, company growth, and customer satisfaction. Companies often err on getting their latest AI product in front of customers to get early feedback. However, the review cycles to assess ethical standards in an environment of rapid innovation creates friction among teams.

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Dataversity

FEBRUARY 14, 2023

Based on what we are seeing with our customers, we can expect a surge in the adoption of emerging technologies like generative artificial Intelligence as well as new software architectures that will transform markets, empower consumers, and deliver new personalized customer experiences. […] The post 2023: Generative AI, IoB-Informed Products, (..)

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Mlearning.ai

MAY 23, 2023

Product First versus Model First mindset is a important concept as you mature in data science. Models can perform well in production, but ultimately fail to answer the business’s question. This is the result of two different mindsets: product first vs. model first mindsets. When is Product First Used?

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DagsHub

AUGUST 2, 2023

This process as a whole can be executed in many different ways like developing dedicated internal tools, product integrations, tons of scripting, or just manual work - all of which require a tremendous amount of time and effort, and mainly reduce productivity. Let’s explain how the Data Engine helps teams do just that.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization. Axfood has been using Amazon SageMaker to cultivate their data using ML and has had models in production for many years.

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IBM Data Science in Practice

DECEMBER 7, 2022

IBM Multicloud Data Integration helps organizations connect data from disparate sources, build data pipelines, remediate data issues, enrich data, and deliver integrated data to multicloud platforms where it can easily accessed by data consumers or built into a data product.

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Data Science Dojo

MAY 14, 2024

Hence, an AI-powered CRM can create a hyper-personalized customer experience, ranging from tailored marketing campaigns and product recommendations to enhanced customer service responses. Moreover, a combination of CRM with AI results in dynamic content creation, like tailoring product descriptions to individual customer preferences.

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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. Taking the model into production with governance workflows and monitoring for sustainability is even more challenging. Click to learn more about author Scott Reed.

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Precisely

MARCH 11, 2024

It’s followed by master data management, which is consistent with the findings that data quality and master data related concerns are highly-ranked issues standing in the way of automation. Automation can deliver higher data quality and auditable governance to processes, thus making it a natural fit for finance processes.

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Precisely

MARCH 12, 2024

When you have a data-informed vision of your customers , you’re better positioned to develop the right products and services to serve their unmet needs. Increased productivity. Better data quality. Customer data quality decays quickly. What is the difference between data enrichment and data quality?

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Dataversity

OCTOBER 21, 2022

Even today, big data is key to better decision-making and operational excellence; however, two phenomena challenge the notion of “collect as much […]. The post Why Just Collecting More and More Data Is No Longer Productive appeared first on DATAVERSITY.

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Precisely

JANUARY 17, 2023

Poor data quality and information silos tend to emerge as early challenges. Customer data quality, for example, tends to erode very quickly as consumers experience various life changes. Moreover, they tend to understand data quality improvement as a one-off exercise.

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IBM Data Science in Practice

NOVEMBER 28, 2022

IBM Multicloud Data Integration helps organizations connect data from disparate sources, build data pipelines, remediate data issues, enrich data, and deliver integrated data to multicloud platforms where it can easily accessed by data consumers or built into a data product.

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Precisely

DECEMBER 23, 2022

Let’s explore the impact of data in this industry as we count down the top 5 supply chain blog posts of 2022. #5 5 2 Tips for Data-Driven CPG Customer Satisfaction Over time, CPG customers have become savvier and increasingly invested in the processes behind the products that they consider spending their hard-earned dollars on.

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Data Science Dojo

OCTOBER 10, 2023

A manufacturing company can discover how AI can enhance production efficiency and quality control by attending an AI conference. Data Observability : It emphasizes the concept of data observability, which involves monitoring and managing data systems to ensure reliability and optimal performance.

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Tableau

MARCH 21, 2023

Srikant Subramaniam Director, Product Management, Tableau Bronwen Boyd March 21, 2023 - 8:28pm March 21, 2023 The increase in data volume and formats over the years has led to complex environments where it can be difficult to track and access the right data.

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Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

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Precisely

MAY 18, 2023

Customer Voices from Trust ’23: the Precisely Data Integrity Summit Jean-Paul Otte from Degroof Petercam shares why data governance is essential to linking data to business value – and why improving data quality is the first step of any governance journey.

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Towards AI

AUGUST 25, 2023

In today's business landscape, relying on accurate data is more important than ever. The phrase "garbage in, garbage out" perfectly captures the importance of data quality in achieving successful data-driven solutions. Join thousands of data leaders on the AI newsletter. Published via Towards AI

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