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.

Data Governance 52

Data Governance Data Quality Data Modeling Data Models 52

phData

MARCH 16, 2023

It’s a good thing you found this article, as we’ll tackle how to build a simple validation layer for your XML/JSON that allows you to only process valid XML/JSON while building out monitoring and reporting measures to help you troubleshoot and monitor for problematic XML/JSON. How do you address this?

Data Quality 105

Data Quality Data Modeling Data Models 105

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. million by 2028.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Dataversity

JUNE 19, 2023

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

Data Pipeline 52

Data Pipeline Database Data Modeling Data Models 52

phData

MARCH 22, 2023

Sigma Computing’s Metrics are a powerful tool for simplifying this complexity and making it easier for business users to access and understand data. In this blog, we will explore what Metrics are, how they work, and why they should be used in data modeling. What Are Metrics From Sigma?

Data Modeling 52

Data Modeling Data Models Data Quality Database 52

Tableau

SEPTEMBER 23, 2021

Editor’s note: This article originally appeared on CIO.com. If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? Data quality: Gone are the days of “data is data, and we just need more.” Data modeling.

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Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

Tableau

SEPTEMBER 23, 2021

Editor’s note: This article originally appeared on CIO.com. If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? Data quality: Gone are the days of “data is data, and we just need more.” Data modeling.

Data Governance 97

Data Governance Analytics Analytics Tableau 97

ODSC - Open Data Science

SEPTEMBER 27, 2023

As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident. She is excited to use her writing skills to help businesses grow and succeed online in the competitive market. You can connect with her on Linkedin.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

The Data Administration Newsletter

AUGUST 7, 2024

My column today is a follow-up to my article “The Challenge of Data Consistency,” published in the May 2023 issue of this newsletter. In that article, I discussed how semantic encoding (also called concept encoding) is the go-to solution for consistently representing master data entities such as customers and products.

Natural Language Processing 52

Natural Language Processing Data Quality Data Governance Data Modeling 52

Heartbeat

JUNE 12, 2023

Many tools and techniques are available for ML model monitoring in production, such as automated monitoring systems, dashboarding and visualization, and alerts and notifications. Organizations can ensure that their machine-learning models remain robust and trustworthy over time by implementing effective model monitoring practices.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Precisely

MARCH 27, 2023

Staffed by experienced enterprise professionals with an average of nearly 25 tenure years, Precisely Strategic Services is proud to have earned a reputation as a top-tier data-centric management consulting organization. This article offers a few examples that illustrate some of the most popular use cases for data-driven strategic services.

Data Quality 52

Data Quality Data Lakes Data Governance Data Modeling 52

The Data Administration Newsletter

JANUARY 17, 2024

Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations.

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AI AI Data Quality Data Governance 122

The Data Administration Newsletter

AUGUST 3, 2021

Rigidly adhering to a standard, any standard, without being reasonable and using your ability to think through changing situations and circumstances is itself a bad standard. I guess I should quickly define what I mean by a “database standard” for those who are not aware.

Database 116

Database Data Quality Data Modeling Data Models 116

Pickl AI

JUNE 23, 2023

And to effectively harness the power of data, organizations are adopting data-centric architectures in AI. But what exactly is data-centric architecture in AI? In this article, we will delve deep into this topic, exploring its significance, benefits, and implementation strategies. So, let’s get started!

AI 52

AI AI Data Governance Data Quality 52

Dataversity

MAY 1, 2023

Many organizations have mapped out the systems and applications of their data landscape. Many have modeled their data domains and key attributes. The remainder of this point of view will explain why connecting […] The post Connecting the Three Spheres of Data Management to Unlock Value appeared first on DATAVERSITY.

Data Quality 97

Data Quality Data Governance Data Modeling Data Models 97

Pickl AI

JULY 25, 2023

Two prominent roles that play a crucial part in this data-driven landscape are Data Scientists and Data Engineers. Data Quality and Governance Ensuring data quality is a critical aspect of a Data Engineer’s role. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. Precisely helps enterprises manage the integrity of their data.

Data Lakes 58

Data Lakes Data Warehouse Hadoop Big Data 58

Precisely

JUNE 4, 2024

The journey to SAP S/4HANA , the next generation of SAP’s ERP system, promises improved performance, real-time analytics, and a simplified data model. But for many companies, the voyage is fraught with challenges, particularly regarding user interfaces (UIs). If you want to try this, you can sign up here for a free Studio trial.

Data Quality 52

Data Quality Data Modeling Data Models Analytics 52

Dataversity

MARCH 22, 2021

This announcement is interesting and causes some of us in the tech industry to step back and consider many of the factors involved in providing data technology […]. The post Where Is the Data Technology Industry Headed? Click here to learn more about Heine Krog Iversen.

Data Lakes 98

Data Lakes Data Warehouse Data Quality Data Modeling 98

Heartbeat

DECEMBER 7, 2023

Data should be designed to be easily accessed, discovered, and consumed by other teams or users without requiring significant support or intervention from the team that created it. Data should be created using standardized data models, definitions, and quality requirements. So think about your clothes!

Data Governance 52

Data Governance Deep Learning Deep Learning Artificial Intelligence 52

The Data Administration Newsletter

JUNE 21, 2022

Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].

Data Quality 78

Data Quality Data Governance Data Modeling Data Models 78

Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Mlearning.ai

MAY 16, 2023

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. First, the data is extracted from the various sources and brought into a staging area.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. This ensures that the data is accurate, consistent, and reliable.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Dataversity

MAY 3, 2024

Commerce today runs on data – guiding product development, improving operational efficiency, and personalizing the customer experience. However, many organizations fall into the trap of thinking that more data means more sales, when these two factors aren’t directly correlated.

Data Modeling 75

Data Modeling Data Models Data Quality Analytics 75

Pickl AI

OCTOBER 17, 2023

ChatGPT’s ability to interpret natural language improves Data Science processes from analysis to model building, underscoring its significance in enabling Data Scientists to fully use data. We will also explore the opportunities and factors to be taken into account while using ChatGPT for Data Science.

Data Scientist 52

Data Scientist Machine Learning Machine Learning Data Science 52

The MLOps Blog

JANUARY 25, 2023

The context of a word was previously only taken into account in the words immediately preceding it, but the BERT model also takes into account the words immediately after it. In this article, you will see some of the things that I learned while working on a sentiment classification model.

Natural Language Processing 52

Natural Language Processing ML ML Deep Learning 52

The Data Administration Newsletter

FEBRUARY 17, 2021

“…quite simply, the better and more accessible the data is, the better the decisions you will make.” – “When Bad Data Happens to Good Companies,” (environmentalleader.com) The Business Impact of an organization’s Bad Data can cost up to 25% of the company’s Revenue (Ovum Research) Bad Data Costs the US healthcare $314 Billion. (IT

Data Quality 59

Data Quality Data Modeling Data Models 59

ODSC - Open Data Science

OCTOBER 15, 2023

The platform is used by businesses of all sizes to build and deploy machine learning models to improve their operations. ArangoDB ArangoDB is a company that provides a database platform for graph and document data. It is a NoSQL database that uses a flexible data model that can be used to store and manage both graphs and documents.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. And since you are reading this article, the data scientists you support have probably reached out for help.

Data Scientist 52

Data Scientist ML ML Machine Learning 52

Dataversity

FEBRUARY 19, 2021

Enterprises are modernizing their data platforms and associated tool-sets to serve the fast needs of data practitioners, including data scientists, data analysts, business intelligence and reporting analysts, and self-service-embracing business and technology personnel. Click to learn more about author Tejasvi Addagada.

Data Analyst 59

Data Analyst Data Scientist Business Intelligence Business Intelligence 59

The Data Administration Newsletter

AUGUST 2, 2022

In part 1 of this series, I shared how our reactions to data can cause a great deal of suffering and discussed the following 3 principles to identify how this suffering can occur as well as ways to alleviate suffering: Attachment is a root cause of suffering ‘Not knowing’ has unlimited possibilities Always being ‘right’ […].

Data Quality 52

Data Quality Data Governance Data Modeling Data Models 52

The Data Administration Newsletter

JUNE 15, 2021

billion annually due to improperly organized testing – despite the fact that 25-40% of budget funds are allocated to methods and tools for Quality Assurance (QA) organization. According to research work done by the National Institute of Standards and Technology, the US economy loses from $22.5 billion to $59.5 What does this mean?

Data Quality 52

Data Quality Data Modeling Data Models 52

Dataversity

FEBRUARY 17, 2021

Click to learn more about author Steve Zagoudis. Successful problem solving requires finding the right solution to the right problem. We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” – Russell L.

Data Warehouse 52

Data Warehouse Data Governance Data Quality Data Modeling 52

Dataversity

JULY 12, 2021

In my eight years as a Gartner analyst covering Master Data Management (MDM) and two years advising clients and prospects at a leading vendor, I have seen first-hand the importance of taking a multidomain approach to MDM. Click to learn more about author Bill O’Kane.

Data Quality 52

Data Quality Data Governance Data Modeling Data Models 52