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Part 1 of this article considered the key takeaways in data governance, discussed at Enterprise Data World 2024. Part […] The post Enterprise Data World 2024 Takeaways: Trending Topics in DataArchitecture and Modeling appeared first on DATAVERSITY.
Datamodels play an integral role in the development of effective dataarchitecture for modern businesses. They are key to the conceptualization, planning, and building of an integrated data repository that drives advanced analytics and BI.
Through big datamodeling, data-driven organizations can better understand and manage the complexities of big data, improve business intelligence (BI), and enable organizations to benefit from actionable insight.
They deliver a single access point for all data regardless of location — whether it’s at rest or in motion. Experts agree that data fabrics are the future of data analytics and […]. The post Maximizing Your Data Fabric’s ROI via Entity DataModeling appeared first on DATAVERSITY.
In the contemporary business environment, the integration of datamodeling and business structure is not only advantageous but crucial. This dynamic pair of documents serves as the foundation for strategic decision-making, providing organizations with a distinct pathway toward success.
And we have short delivery cycles, sprints, and a lot of peers to share datamodels with. The post Quick, Easy, and Flexible DataModel Diagrams appeared first on DATAVERSITY. Many of us have a lot to do. In search of something lightweight, which is quick and easy, and may be produced (or consumed) by other programs?
In the buzzing world of dataarchitectures, one term seems to unite some previously contending buzzy paradigms. Knowledge graph” is not a new term; see for yourself […] The post Modeling Modern Knowledge Graphs appeared first on DATAVERSITY. That term is “knowledge graphs.” First, let us look back.
Many software developers distrust dataarchitecture practices such as datamodeling. They associate these practices with rigid and bureaucratic processes causing significant upfront planning and delays.
An integrated solution provides single sign-on access to data sources and data warehouses.’ You can create common datamodels and BI object templates to publish across tenants with a single click. ‘Integrating augmented analytics within your existing software solutions is simple.
An integrated solution provides single sign-on access to data sources and data warehouses.’ You can create common datamodels and BI object templates to publish across tenants with a single click. ‘Integrating augmented analytics within your existing software solutions is simple.
An integrated solution provides single sign-on access to data sources and data warehouses.’. The integrated augmented analytics approach includes simple tenant management to deploy with a shared datamodel for single-tenant mode or an isolated datamodel for multi-tenant mode and software as a service (SaaS) applications.
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.
However, as a business grows, the way the organization interacts with its data can change, making processes less efficient and impairing progress toward business goals. Businesses need to think critically about their dataarchitecture to […]
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. And outdated datamodels no longer […].
Bounded Contexts / Ubiquitous Language My new book, DataModel Storytelling,[i] contains a section describing some of the most significant challenges datamodelers and other Data professionals face. Like most of its predecessors, including Agile development and […].
In today’s data-driven world, technologies are changing very rapidly, and databases are no exception to this. The current database market offers hundreds of databases, all of them varying in datamodels, usage, performance, concurrency, scalability, security, and the amount of supplier support provided.
Whether you’re sitting on a ton of untapped data or you’re not extracting value from your data because of organizational restrictions, you may be aware by now of the endless possibilities of a mature datamodel. The post 3 Signs That Your Data Is Trapped in Silos appeared first on DATAVERSITY.
One of the ideas we promote is elegance in the core datamodel in a Data-Centric enterprise. Look at most application-centric datamodels: 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.
Canonical DataModels and Overlapping Connections In the previous article, I introduced and explained the approach to application development called ‘Domain-Driven Development’ (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed datamodel can add value to a DDD project by helping to create (..)
Cloud giants like Google and Snowflake, unicorns like dbt Labs, and a host of venture-backed startups are now talking about a critical new layer in the data and analytics stack. Some call it a “metrics layer,” or a “metrics hub” or “headless BI,” but most call it a “semantic layer.”
In the first article, I introduced and explained the approach to application development called Domain-Driven Development (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed datamodel can add value to a DDD project by helping to create the Ubiquitous Language that defines the Bounded Context (..)
Data Mesh and Data as a Product In the first article, I introduced and explained the approach to application development called Domain-Driven Development (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed datamodel can add value to a DDD project by helping to create the Ubiquitous […]. (..)
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? Datamodeling. Data migration . Dataarchitecture. Nathan Cho. Nirav Kamdar.
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.
In an industry as competitive as eCommerce retail, the ability to turn data into actionable insights presents the opportunity to make business decisions that drive more revenue and control costs. Collecting and then analyzing retail data like customer visits, logistic fulfillment, pricing, and customer satisfaction presents a […].
However, most enterprises are hampered by data strategies that leave teams flat-footed when […]. The post Why the Next Generation of Data Management Begins with Data Fabrics appeared first on DATAVERSITY. Click to learn more about author Kendall Clark. The mandate for IT to deliver business value has never been stronger.
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? Datamodeling. Data migration . Dataarchitecture. Nathan Cho. Nirav Kamdar.
Why would Technics Publications publish a book outside its specialty of data management? First, Graham is a world-renowned datamodeler and the author of DataModeling for Quality, and therefore many of his examples are in the field of data management. Second, and more […]
The desire to leverage data as a strategic asset has led to the development of sophisticated systems and methodologies that go beyond basic data storage and retrieval. Among these advancements is modern data warehousing, a comprehensive approach that provides access to vast and disparate datasets.
Data engineering is a fascinating and fulfilling career – you are at the helm of every business operation that requires data, and as long as users generate data, businesses will always need data engineers. The journey to becoming a successful data engineer […]. In other words, job security is guaranteed.
A collection of facts from which inferences can be made is called data. Data is the cornerstone of contemporary society and is crucial to many facets of people’s lives. In order to gain knowledge and make wise decisions, […] The post Data Provisioning: Ingest, Curate, and Publish appeared first on DATAVERSITY.
Are you planning on strategically using data to improve the efficiencies of your value chains? This can happen with artificial intelligence models that can make a journey interesting for […]. Click to learn more about author Tejasvi Addagada.
Data Science is a diverse field with an array of career and job options out there to pursue. The modern economy is dependent on data and data analysis so, naturally, data scientists are in high demand and enjoy good salary and job security prospects. With that in mind, below are 11 intriguing roles for data […].
Are you in a work environment where streaming architecture is not yet implemented across all IT systems? Click to learn more about author Aditi Raiter. Have you ever been in a situation when you had to represent the ETL team by being up late for L3 support only to find out that one of your […].
Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Click to learn more about author Wayne Yaddow.
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.
NoSQL database systems continue to gain traction, but they are still not widely understood. There is more than one type of NoSQL database and a large number of individual NoSQL DBMSs. There are more than 225 NoSQL DBMSs listed on the NoSQL Database website alone and it just is not possible to review and understand every option. […].
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. […].
“…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
They are interesting to an extent, but mostly, they feel like a late-night re-run and remind me that data work is hard. If you haven’t heard about metrics stores yet, they’re “newish,” so you likely will. So, what is a metrics store? Most of the young vendors trying to create this category will tell you that […]
Many developers pooh-pooh OWL (the dyslexic acronym for the Web Ontology Language). Many decry it as “too hard,” which seems bizarre, given that most developers I know pride themselves on their cleverness (and, as anyone who takes the time to learn OWL knows, it isn’t very hard at all). It does require you to think […].
“First thing we do, let’s banish all of the CIOs.” Shakespeare was much harder on lawyers. But the reality is, IT is a mess. Yes, it is. Judged by any performance measure we would apply to other groups within any enterprise, it is. I am reminded of old black and white movies: the […].
The foundation of a business’s digital transformation is effective data management. Master data management services allow you to effectively utilise the new currency of data and effectively collaborate between different functional verticals, departments, and stakeholders for better productivity, efficiency, and […]
In my September 2020 Data is Risky Business column for TDAN.com, I wrote about the strategy lessons for data that we can learn from the writings of a 16thcentury Japanese swordsman. To summarize: – We need to study different disciplines to understand how they relate to each other.
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