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I published an article a few months back that was titled Where Does Data Governance Fit in a DataStrategy (and other important questions). In the article, I quickly outlined seven primary elements of a datastrategy as an answer to one of the “other important questions.”
They use data better. How does Spotify win against a competitor like Apple? Using machine learning and AI, Spotify creates value for their users by providing a more personalized experience.
Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.
The main goal of creating an enterprise data fabric is not new. It is the ability to deliver the right data at the right time, in the right shape, and to the right data consumer, irrespective of how and where it is stored. Data fabric is the common “net” that stitches integrated data from multiple data […].
In my last article I suggested that many organizations have approached Data Governance incorrectly using only centralize data governance teams and that approach is not working for many.
And do you have the transparency and data observability built into your datastrategy to adequately support the AI teams building them? Will the new creative, diverse and scalable data pipelines you are building also incorporate the AI governance guardrails needed to manage and limit your organizational risk?
A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.
Ensuring data quality is an important aspect of data management and these days, DBAs are increasingly being called upon to deal with the quality of the data in their database systems more than ever before. The importance of quality data cannot be overstated.
At the recent InfoGovWorld conference, I had the opportunity to participate in a panel discussion about the future of Data Governance. Common themes were the growing importance of governance metadata, especially in the areas of business value, success measurement and reduction in operational and data risk.
I delivered this series of questions focused on relating their need for an over-arching datastrategy with the […]. The purpose of the Q&A was to assist her with determining the most appropriate messaging to share across the company.
Reading Time: 11 minutes The post DataStrategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
There is… but one… Data Governance. Maybe you are one who believes that there is something called Master Data Governance, Information Governance, Metadata Governance, Big Data Governance, Customer [or insert domain name here] Data Governance, Data Governance 1.0 – 2.0 – 3.0, or maybe even that […].
I said I thought it affected all of them pretty profoundly, but perhaps the Metadata wedge the most. Recently, I was giving a presentation and someone asked me which segment of “the DAMA wheel” did I think semantics most affected. I thought I’d spend a bit of time to reflect on the question and answer […].
In this age, data management 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. Understandably, Data Catalogs […].
The particular episode we recommend looks at how WeWork struggled with understanding their data lineage so they created a metadata repository to increase visibility. Agile Data. Another podcast we think is worth a listen is Agile Data. Techcopedia follows the latest trends in data and provides comprehensive tutorials.
Is your organization struggling to succeed with your Data Governance program? Data Governance occurs best when done in conjunction with the business processes and not as a “bolt on”/additional activity. Many organizations have attempted to implement Data Governance and their business glossary with a very limited […].
Getting to great data quality need not be a blood sport! This article aims to provide some practical insights gained from enterprise master data quality projects undertaken within the past […].
By creating visual representations of data flows, organizations can gain a clear understanding of the lifecycle of personal data and identify potential vulnerabilities or compliance gaps. Note that putting a comprehensive datastrategy in place is not in scope for this post.
The purpose of this article is to provide a model to conduct a self-assessment of your organization’s data environment when preparing to build your Data Governance program. Take the […].
We took this a step further by creating a blueprint to create smart recommendations by linking similar data products using graph technology and ML. In this post, we showed how an organization can augment a data catalog with additional metadata by using ML and Neptune with an automated process. His Amazon author page
I believe that my strongest articles and columns come from opportunities to work with great companies and organization. A long-time client recently told me that, for their data and […]. Of course, I cannot mention their names. But there is a strong possibility that you may have some of the same opportunities in front of you.
I vividly remember reading this passage from Bob Seiner’s TDAN.com article “Things I Think I Think about Data Governance”, from August 1, 2015: If we were going to remove two words from the Data Governance vocabulary, I would choose the words “assign” and “owner.
There is … but one … Data Governance. Maybe you are one of those that believe that there is something called Master Data Governance, Information Governance, Metadata Governance, Big Data Governance, Customer [or insert domain name here] Data Governance, Data Governance 1.0 – 2.0 – 3.0, […].
This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form. Allows metadata repositories to share and exchange.
Achieving Enterprise Data Awareness is a natural maturity progression within the governance domain. At this stage, companies rightly see Data Governance and Information Governance as a large metadata puzzle.
I debated over whether to title this articleData Governance as a Puzzle … or Data Governance is a Puzzle. I selected the first option and decided to use this article to provide a comparison of data governance and good puzzles; rather than […].
Reading Time: 3 minutes Last month, IDC announced that LeasePlan, a car-as-a-service company, was the winner of IDC’s European DataStrategy and Innovation awards, in the category of Data Management Excellence, for LeasePlan’s logical data fabric. This is a testament to the maturity of.
Data wellness obviously means a lot to me. This article starts with something that is “going on” with […]. David Crosby, of super group fame with Stills, Nash and Young, once said that you have to write about something that goes on in your life if you want to write something that means something to you.
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.
You may already have a formal Data Governance program in place. Or … you are presently going through the process of trying to convince your Senior Leadership or stakeholders that a formal Data Governance program is necessary. Maybe you are going through the process of convincing the stakeholders that Data […].
No, this is not a mistyping of data literacy. Yes, like everyone, I am aware of and fully on-board with the growing movement to improve data literacy in the enterprise. What I want to talk about is Data Littering, which is something else entirely.
Imagine what it would be like if your data was perfect. By perfect I mean that the people in your organization have confidence in the data to use it for effective decision making and to focus on building efficiency and effectiveness through data into your […]. By perfect I mean fit for use and high quality.
Back in 2017, I wrote an article titled There are No Facts … Without Data. The overwhelmingly positive response to that article validated for me that most people believed my premise to be true. It is time to revisit that topic. I was very thankful to see that. In this anti-fact world (watch cable news […].
The three of us talked migration strategy and the best way to move to the Snowflake Data Cloud. As Vice President of Data Governance at TMIC, Anthony has robust experience leading cloud migration as part of a larger datastrategy. This underscores the importance of having a plan that fits your datastrategy.
A common misconception among c-level executives is that governance and management of data is the same thing other than in capital letters. Below, we will explore the main differences between Data Management […].
In the September issue of TDAN.com, Anthony Algmin denounced Data Catalogs as a “1980’s solution to a 2020’s problem.” What is the state of data science today? As I state in my book, The Data Catalog: Sherlock Holmes Sleuthing for Data Analytics and in many articles, 80% (or more) of a data analyst’s […].
A recent experience brought home to me the critical importance of good quality data in even the simplest of processes, particularly as processes become more automated and data driven. Before I went on vacation last month, a new team member joined Castlebridge.
SCD2 metadata – rec_eff_dt and rec_exp_dt indicate the state of the record. Register source tables in the AWS Glue Data Catalog We use an AWS Glue crawler to infer metadata from delimited data files like the CSV files used in this post. These two columns together define the validity of the record.
What is Data Governance and How Do You Measure Success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Answers will differ widely depending upon a business’ industry and growth strategy.
Can the responsibilities for vocabulary ownership and data ownership by business stakeholders be separate? I have listened to many presentations and read many articles about data governance (or data stewardship if you prefer), but I have never come across anyone saying they can and should be. Should they be?
The post Navigating the New Data Landscape: Trends and Opportunities appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. At TDWI, we see companies collecting traditional structured.
As you can probably tell from my previous columns, my thinking lately has been very focused on the issue of data curation and data governance automation. I have always been troubled by the amount of manual effort required in almost all phases of the data life cycle. In my November 2023 article, Waldo Where Are […]
Several years ago, I wrote an article called the Data Governance Bill of “Rights.” I also speak often about my Bill of “Rights” in many of my webinars and presentations. Please notice that I put the word “rights” in quotations. By rights, I do not mean human rights, or the freedoms to claim equality based […].
I last published my Data Governance Bill of “Rights” in a TDAN.com article circa 2017. I mentioned in the earlier piece that Data Governance is all about doing the “right” thing when it comes to managing your data. It’s all in the data. That seems like a long time ago.
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