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Welcome to our new series, “Book of the Month.” In this series, we will explore new books in the datamanagement space, highlighting how thought leaders are driving innovation and shaping the future.
Lean GovernanceTM is the next machine to change the world of DataGovernance and Enterprise DataManagement. As proponents of lean thinking, we view corporations as data factories that produce information for operations, reporting, and financial modeling.
Four years ago, in a fit of naivete, I decided to write a book about DataGovernance. I wasn’t naïve about DataGovernance – I was naïve about what that book would bring about. After I left my corporate gig, I did a state-of-the-state in the data industry to get a broader understanding […].
As a frequent reviewer of data and strategy books, I am always interested in understanding authors’ perspectives on datagovernance. Two recent books have ideas that are worthy of datagovernance professionals: “Rewired” by Eric Lamarre, Kate Smaje, and Rodney W. Wixom, Cynthia M.
As I’ve been working to challenge the status quo on DataGovernance – I get a lot of questions about how it will “really” work. In 2019, I wrote the book “Disrupting DataGovernance” because I firmly believe that […]. The post Dear Laura: How Can I Build Traction for DataGovernance in a Start-Up?
As some of you already know, I am dedicating these summer days to the writing of my new book, “99 Questions About DataManagement,” which follows in some way the book “20 Things You Have to Know About DataManagement.”
It has been eight years plus since the first edition of my book, Non-Invasive DataGovernance: The Path of Least Resistance and Greatest Success, was published by long-time TDAN.com contributor, Steve Hoberman, and his publishing company Technics Publications. That seems like a long time ago.
From Bob Seiner’s first book, Non-Invasive DataGovernance, we learned how to get the benefits of datagovernance without making major changes to our job roles or functions. That’s probably why Non-Invasive Data […]
What makes a databook great? Our time is valuable, so a good databook should be concise and practical. It should show us how to do something, step by step, so we can apply the techniques to reinforce and always remember. The experiences of the author should shine through in every chapter. It should […]
Sometimes I like to read a book purely for pleasure, like a good Dan Brown or Stephen King novel, and sometimes I like to read a book to learn something new. There are not many books that I read for both pleasure and to learn new things. One exception is Telling Your Data Story: Data […].
Steve Hoberman has been a long-time contributor to The Data Administration Newsletter (TDAN.com), including his The Book Look column since 2016, and his The Data Modeling Addict column years before that.
Datagovernance and data quality are closely related, but different concepts. The major difference lies in their respective objectives within an organization’s datamanagement framework. Data quality is primarily concerned with the data’s condition.
What is a DataGovernance Framework? A datagovernance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.
Technics Publications has started publishing a line of Data-Driven AI books, and one of the first books in this series is “AI Governance” by Dr. Darryl J Carlton. The goal of the book in one sentence is to enable the reader to gain the knowledge and tools to effectively govern and oversee the use of […]
Succeed As a Business Analyst was originally published in Analyst’s corner on Medium, where people are continuing the conversation by highlighting and responding to this story.
I never realized how complex data privacy rules can be for multinational companies until I read “Data Privacy Across Borders” by Lambert Hogenhout and Amanda Wang.
I love books that make you think. We The People, by Kathy Rondon, leads us down the path of rethinking what we do with data today and also, for most of us, makes us realize that we often do not consider ethics when using data today to make business decisions. To quote Kathy, “Just because […].
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.
To talk with Bob Seiner is to talk with a friend and a personal reference in the field of datamanagement. The fact that I have collaborated with him in the translation of his first book, Non-Invasive DataGovernance: The Path of Least Resistance and Greatest Success, into Spanish is a source of pride.
Astera Astera is an all-in-one, no-code platform that simplifies datamanagement with the power of AI. With Asteras visual UI, users automate workflows, connect diverse data sources, and build and managedata pipelines without writing a single line of code. Book a demo today to see what Astera is all about.
Cataloging items has been a process used since the early 1900s to manage large inventories, whether it be books or antics. In this age, datamanagement has become a necessary routine. Organizations have started to uncover large sets of data in the form of Assets typically used for analysis and decision making.
For modern organizations, data is a commodity almost always in flux, which exposes it to risk-related challenges. This makes proper data access management even more critical. What Is Data Access Management? Data access management is crucial to an organization’s overall datamanagement strategy.
My new book, Data Model Storytelling[i], describes how data models can be used to tell the story of an organization’s relationships with its Stakeholders (Customers, Suppliers, Dealers, Regulators, etc.), and how data models can be used to help organizations get from where they currently are to where they would like to be.
As a result, your data becomes quick to discover, easier to understand, and more accessible by humans and machines. A library wouldn’t just store books on random shelves; it would categorize them, label them, and have entries in a catalog system. Metadata management does the same thing for your data.
Once the migration is complete, you can verify data integrity by conducting a data validation check. Role of DataGovernance in Cloud Migration When migrating your data from on-prem to the cloud, you may overlook the documentation and design of datagovernance processes.
This quarter’s column draws on my keynote for DAMA Calgary’s contribution to DAMA Days Canada last month, which in turn drew on some of the content in the second edition of the “Data Ethics” book I wrote with my colleague Katherine O’Keefe (particularly, Chapter 3 and Chapter 11). My keynote looked at the thorny question […]
Once upon a time (the way every good fairy tale begins), a book or a paper by a Ted Codd or Bill Inmon would set in motion a sea change that swept us all in a new direction. We no longer live in that world. We now live in a world in which everyone talks, […]
So, whether you’re checking the weather on your phone, making an online purchase, or even reading this blog, you’re accessing data stored in a database, highlighting their importance in modern datamanagement. This allows data to be retrieved from various tables when needed, based on the established relationships.
For instance, while creating a data model for a library database, instead of typing out the author’s name on every book, give each author a unique number, like an author ID. Then, link this ID to each book to indicate the author. The resulting web-like structure is not only sophisticated but also highly flexible.
Navigating the Data Maze: Challenges in the SAP Landscape For SAP users, datamanagement can feel like a labyrinth, fraught with obstacles and frustrating dead ends. The burden of manual data entry looms large, with endless spreadsheets consuming valuable time and resources.
Automating DataManagement to Transform Reporting Processes. The combination of a lack of datagovernance and control, coupled with insufficient automation has a negative impact on the productivity and timeliness of the group reporting process. Automation and datamanagement go hand-in-hand. Enable cookies.
From cloud-based platforms to on-premises databases, Simbas connectors make the data accessible, reliable, and ready for analysis. With Logi Symphony, you get: DataGovernance and Security: Layered protections ensure that data is accessed securely, respecting user and tenant-level permissions.
few key ways to reduce skills gaps are streamlining processes and improving datamanagement. While many finance leaders plan to address the skills gap through hiring and employee training and development, a significant percentage of leaders are also looking to data automation to bridge the gap.
In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on Master DataManagement (MDM), the creation of a single, reliable source of master data, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.
Mastering Data: Effectively Manage Your Data Download Now How Jet Analytics Enhances Microsoft Fabric Jet Analytics from insightsoftware is a complete data preparation, automation and modeling solution that enables Microsoft Dynamics customers to accelerate Dynamics ERP-ready BI projects without requiring specialist skills.
AI can also be used for master datamanagement by finding master data, onboarding it, finding anomalies, automating master data modeling, and improving datagovernance efficiency. From Chaos to Control: Navigating Your Supply Chain With Actionable Insights Download Now Is Your Data AI-Ready?
Here are some key steps towards achieving this goal: Adopt Integrated Budgeting Software : Investing in a modern budgeting and planning application with centralized datamanagement, real-time collaboration, and robust controls can significantly enhance the efficiency and effectiveness of the budgeting process.
Maintaining robust datagovernance and security standards within the embedded analytics solution is vital, particularly in organizations with varying datagovernance policies across varied applications.
This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any datamanagement initiative, such as data integration, data migration, data transformation, data warehousing, or automation.
Data Quality and Consistency Maintaining data quality and consistency across diverse sources is a challenge, even when integrating legacy data from within the Microsoft ecosystem. Get a Demo See how companies are getting live data from their ERP into Excel, and closing their books 4 days faster every month.
In the second edition of DataManagement at Scale [1] author Piethein Strengholt reacts to the privilege — and problem — of formulating one’s thoughts at a certain point in time. This is merely an example of how Strengholt expands and details the language of data mesh.
Insufficient functionality and dashboards – ISVs face demands from their users to uplevel their reporting (e.g., better drill down, more filtering options, real-time, self-service capabilities, exporting etc.).
Addressing these challenges requires a combination of technical solutions, datagovernance practices, and a clear reporting strategy. Reporting on large datasets can impact performance, leading to slower query response times and lags in real-time reporting.
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