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When I occasionally re-read articles I penned back in 2009 or 210, I’m often struck that – no matter how many things have undeniably changed over the intervening years in the data arena – there are some seemingly eternal verities. These articles have a certain timeless quality to them. True then, true now.
This increase was driven in part by the launch of my new Maths & Science section , articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. This is my selection of the articles that I enjoyed writing most, which does not always overlap with the most popular ones. May onwards.
However, embedding ESG into an enterprise datastrategy doesnt have to start as a C-suite directive. Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams.
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.
Data is commonly referred to as the new oil, a resource so immensely powerful that its true potential is yet to be discovered. We haven’t achieved enough with data research and other statistical modeling techniques to be able to see data for what it truly is and even our methods of accruing data are rudimentary […].
After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current dataarchitecture and technology stack. It isn’t easy.
A sea of complexity For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives. Layering technology on the overall dataarchitecture introduces more complexity. Data Management
The phrase “dataarchitecture” often has different connotations across an organization depending on where their job role is. For instance, most of my earlier career roles were within IT, though throughout the last decade or so, has been primarily working with business line staff.
Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern dataarchitecture is critical in order to become a data-driven organization. Mike is the author of two books and numerous articles.
I’ll let you know what the coin was toward the end of this article, but first I need to give you my own […] I read “How Big Things Get Done” when it first came out about six months ago.[1] 1] I liked it then. But recently, I read another review of it, and another coin dropped.
Donna Burbank is a Data Management Consultant and acts as the Managing Director at Global DataStrategy, Ltd. Her Twitter page is filled with interesting articles, webinars, reports, and current news surrounding data management. TDAN stands for The Data Administration Newsletter. IRM UK Connects.
In the thirteen years that have passed since the beginning of 2007, I have helped ten organisations to develop commercially-focused DataStrategies [1]. However, in this initial article, I wanted to to focus on one tool that I have used as part of my DataStrategy engagements; a Data Maturity Model.
In 2022, we saw that DataStrategy played key role in the success of top performing companies globally. is being “data driven”. This article is continuation of the proposed enterprise data management block […].
Many software developers distrust dataarchitecture practices such as data modeling. They associate these practices with rigid and bureaucratic processes causing significant upfront planning and delays.
DataArchitecture / Infrastructure. When I first started focussing on the data arena, Data Warehouses were state of the art. More recently Big Dataarchitectures, including things like Data Lakes , have appeared and – at least in some cases – begun to add significant value. DataStrategy.
This article is not about Marketing professionals, it is about poorly researched journalism. Prelude… I recently came across an article in Marketing Week with the clickbait-worthy headline of Why the rise of the chief data officer will be short-lived (their choice of capitalisation).
The recently launched DataStrategy Review Service is just one example. White Papers can be based on themes arising from articles published here, they can feature findings from de novo research commissioned in the data arena, or they can be on a topic specifically requested by the client. Follow @peterjthomas.
In today’s world, access to data is no longer a problem. There are such huge volumes of data generated in real-time that several businesses don’t know what to do with all of it. Unless big data is converted to actionable insights, there is nothing much an enterprise can do.
Top-quality data currently represents one of the most important resources for any company. Startups that lack familiarity with important tendencies and trends in their industry need to have this crucial data […].
Part 3 completes this article series by discussing some important topics beyond the critical differentiators in the terminology and capabilities of Property Graphs and Knowledge Graphs covered in Parts 1 and 2.
When you’ve been involved in data management for as long as I have, things are definitely bound to change. And things have changed, quite a lot, in fact. Back when I started in IT, IMS was the primary database system used at most big enterprises and most of the computing was done on mainframe systems. […].
For anyone who is unaware, the title of the article echoes a 1953 Nature paper [1] , which was instead “of considerable biological interest” [2]. I have been very much focussing on the start of a data journey in a series of recent articles about DataStrategy [3]. Introduction. Follow @peterjthomas.
This article attempts to analyze and make sense of a harmonization between Information Architecture and SAFe, and will address how their cooperation will contribute to the development of an Agile Business. SAFe is a very modern Agile Framework and has replaced TOGAF in many organizations.
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.
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.
My book “The Data-Centric Revolution” will be out this summer. I will also be presenting at Dataversity’s DataArchitecture Summit coming up in a few months. Both exercises reminded me that Data-Centric is not a simple technology upgrade. It’s going to take a great deal more to shift the status quo.
We have learned from our recent articles : Demystifying Edge Computing and Types of and How to use Edge Computing – how Edge Computing architecture, with its capabilities of distributed computing, is addressing the rising scale and ubiquity of data.
In a world of exponential data growth and ever-increasing silo-ization, the FAIR principles are needed more than ever. In this article, we will first summarize the FAIR principles and describe the […]. FAIR stands for: Findable, Accessible, Interoperable and Reusable.
The example of the myriad of COVID-19 challenges shows that coordinated, data-driven action across boundaries has helped fast-track solutions such as testing, vaccines, and non-pharmaceutical interventions. In this article, we will explore how organizations can […].
Success stories of implementing a data driven culture or creating successful “Data Products” is still new. We will continue to look at my proposed enterprise data management and high level lego framework in this article and focus on “Data Mission” Level 2. The recommended Lego blocks […].
The data world continues to change rapidly and you may want to consider these predictions when planning for the new year. Special thank you to Altair for providing the following set of bold predictions for 2023. The rise of generative AI startups: Generative artificial intelligence exploded in 2022.
The increasing speed and pace of business certainly contributes to several data challenges (quality, timeliness, availability and, most important, usability of the data).
It has been an incredible run. I hope it is just “see you soon” rather than “goodbye.” With this column, DAMA International’s streak of quarterly columns since mid-2001 is coming to an end. The columns have featured the activities and incredible work of DAMA International over the past two decades. Thank you, DAMA, and I […].
Given this, I have used The Dictionary entries as a basis for this slightly expanded article on the subject of chart types. A Chart is a way to organise and Visualise Data with the general objective of making it easier to understand and – in particular – to discern trends and relationships. Radar Charts / Spider Charts.
Data catalogs also seek to be the. The post Choosing a Data Catalog: Data Map or Data Delivery App? appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information.
Blockchain is a distributed, shared, permissioned ledger for recording transactions with consensus, provenance, immutability, and finality. It is the technology that drives virtual currencies like Bitcoin. But its potential spans many more industries and use cases than just virtual currencies. But let’s back up for a minute.
In the cloud-era, should you store your corporate data in Cosmos DB on Azure, Cloud Spanner on the Google Cloud Platform, or in the Amazon Quantum Ledger? The overwhelming number of options today for storing and managing data in the cloud makes it tough for database experts and architects to design adequate solutions.
“The digital world is here but our old companies are simply not yet designed for digital.” Jeanne Ross (Designed for Digital) Your business may need to develop a new digital platform to replace your existing application-centric IT solution.
As data programs accelerate their capabilities to tap into insights, the rights of the consumer and their privacy are racing counter. We’ve long had to contend with the balance of how to best use data throughout its lifecycle and build processes. The more recent innovation? The ability to rapidly pivot, experiment, and learn.
One of the ideas we promote is elegance in the core data model in a Data-Centric enterprise. Look at most application-centric data models: you would think they would be simpler than the enterprise model, after all, they are a small subset of it. This is harder than it sounds.
Government systems produce and store a large amount of data daily. Government leaders want to utilize this data to make decisions faster and more efficiently. It is nearly impossible to make well-informed decisions if that data is not visible, accessible, organized, and cannot be seamlessly discovered across the enterprise.
Data Governance is defined as the execution and enforcement of authority over the management of data and data-related assets.1 1 The terms “Data Mesh” and “Data Fabric” are the most recent examples of names being given to something that describes techniques to help organizations manage their data.
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