Data Science Blog

JULY 23, 2023

Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. It advocates decentralizing data ownership to domain-oriented teams.

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The Data Administration Newsletter

MARCH 15, 2022

1 In this article, I will apply it to the topic of data quality. I will do so by comparing two butterflies, each that represent a common use of data quality: firstly and most commonly in situ for existing systems, and secondly for use […]. We know the phrase, “Beauty is in the eye of the beholder.”1

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Dataversity

MAY 7, 2025

Business intelligence (BI) ensures organizations and enterprises make measured decisions. However, many analytics teams in businesses struggle with slow, fragmented, or downright counterproductive BI systems. The main culprit […] The post How Data Accessibility Shapes Business Intelligence appeared first on DATAVERSITY.

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Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

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Dataversity

DECEMBER 7, 2021

Robert Seiner and Anthony Algmin faced off – in a virtual sense – at the DATAVERSITY® Enterprise Data World Conference to determine which is more important: Data Governance, Data Leadership, or Data Architecture. The post Data Governance, Data Leadership or Data Architecture: What Matters Most?

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IBM Journey to AI blog

SEPTEMBER 5, 2024

The best way to build a strong foundation for data success is through effective data governance. Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success.

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Dataconomy

JUNE 10, 2025

Applications of data analytics Data analytics finds applications across various fields, driving innovation and efficiency. Business intelligence and reporting Through dashboards and reports, data analytics provides actionable insights into performance metrics, allowing for better decision-making.

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IBM Journey to AI blog

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

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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. But […].

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AWS Machine Learning Blog

APRIL 3, 2025

Data governance challenges Maintaining consistent data governance across different systems is crucial but complex. Embracing generative AI with Amazon Bedrock The company has identified several use cases where generative AI can significantly impact operations, particularly in analytics and business intelligence (BI).

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Dataconomy

MARCH 20, 2025

This process extracts data from various sources, transforms it into a desired format, and loads it into the data mart. With efficient ETL practices, organizations can maintain high data quality and relevant structures. It relies on the data warehouse for its information and ensures consistency across the organization.

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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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DagsHub

AUGUST 23, 2024

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

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Alation

AUGUST 5, 2021

What is Data Quality? Data quality is defined as: the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. By tracking data quality , a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

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Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. So why are organizations not able to scale governance? Meet Governance Requirements.

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Dataconomy

MARCH 28, 2025

Definition and scope Understanding decision intelligence requires recognizing its multi-faceted nature. At its core, it draws from AI and data science while connecting to broader concepts like business intelligence. Data enrichment and AI processing Enhancing data quality is crucial in this phase.

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Dataversity

AUGUST 12, 2024

We live in a data-driven culture, which means that as a business leader, you probably have more data than you know what to do with. To gain control over your data, it is essential to implement a data governance strategy that considers the business needs of every level, from basement to boardroom.

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Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms. Cloud-native data execution is just the beginning.

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The Data Administration Newsletter

JANUARY 15, 2025

In my journey as a data management professional, Ive come to believe that the road to becoming a truly data-centric organization is paved with more than just tools and policies its about creating a culture where data literacy and business literacy thrive.

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Alation

DECEMBER 15, 2021

Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information. Data governance requires a system.

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Alation

MAY 31, 2023

The Data Governance & Information Quality Conference (DGIQ) is happening soon — and we’ll be onsite in San Diego from June 5-9. If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. The best part?

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Alation

JANUARY 26, 2023

These data requirements could be satisfied with a strong data governance strategy. Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. Low quality In many scenarios, there is no one responsible for data administration.

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Dataconomy

MAY 26, 2025

Big data management refers to the strategies and processes involved in handling extensive volumes of structured and unstructured data to ensure high data quality and accessibility for analytics and business intelligence applications.

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Dataconomy

SEPTEMBER 4, 2023

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence. Understand what insights you need to gain from your data to drive business growth and strategy.

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Smart Data Collective

DECEMBER 21, 2022

Big data technology has helped businesses make more informed decisions. A growing number of companies are developing sophisticated business intelligence models, which wouldn’t be possible without intricate data storage infrastructures. One of the biggest issues pertains to data quality.

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Defined.ai blog

AUGUST 10, 2023

For example, if your AI model were designed to predict future sales based on past data, the output would likely be a predictive score. This score represents the predicted sales, and its accuracy would depend on the data quality and the AI model’s efficiency. Maintaining data quality.

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Precisely

APRIL 25, 2024

Jean-Paul sat down for an interview where we discussed his background as a former CDO, the challenges he faced, and how he developed his unique perspective and data governance expertise. After starting my career in banking IT, I turned to consulting, and more specifically to Business Intelligence (BI) in 2004.

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Alation

JULY 19, 2021

This requires a metadata management solution to enable data search & discovery and data governance, both of which empower access to both the metadata and the underlying data to those who need it. In today’s world, metadata management best practices call for a data catalog. Administrative information.

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Data Science Dojo

JANUARY 12, 2023

Analytics Data lakes give various positions in your company, such as data scientists, data developers, and business analysts, access to data using the analytical tools and frameworks of their choice. You can perform analytics with Data Lakes without moving your data to a different analytics system. 4.

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IBM Journey to AI blog

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

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Dataversity

JULY 21, 2022

Despite its many benefits, the emergence of high-performance machine learning systems for augmented analytics over the last 10 years has led to a growing “plug-and-play” analytical culture, where high volumes of opaque data are thrown arbitrarily at an algorithm until it yields useful business intelligence.

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The Data Administration Newsletter

OCTOBER 3, 2023

My recent columns have focused on actionable initiatives that can both deliver business value, providing a tangible achievement, and raise the profile of the data management organization data management organization (DMO).(For In that light, let’s […]

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The Data Administration Newsletter

DECEMBER 20, 2022

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning.

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Dataversity

AUGUST 16, 2023

Various factors have moved along this evolution, ranging from widespread use of cloud services to the availability of more accessible (and affordable) data analytics and business intelligence tools.

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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Precisely

OCTOBER 10, 2024

Data democratization has become a hot topic lately with advances in technology such as AI and machine learning, cloud storage and scalable server capacity, and improved integration. Then add self-service business intelligence tools that are accessible to virtually anyone. Attention to data quality.

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