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We have lots of data conferences here. I’ve taken to asking a question at these conferences: What does dataquality mean for unstructured data? Over the years, I’ve seen a trend — more and more emphasis on AI. This is my version of […]
But while state and local governments seek to improve policies, decision making, and the services constituents rely upon, data silos create accessibility and sharing challenges that hinder public sector agencies from transforming their data into a strategic asset and leveraging it for the common good. . Modern dataarchitectures.
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.
A few years ago, Gartner found that “organizations estimate the average cost of poor dataquality at $12.8 million per year.’” Beyond lost revenue, dataquality issues can also result in wasted resources and a damaged reputation. Learn more about dataarchitectures in my article here.
The first step to fixing any problem is to understand that problem—this is a significant point of failure when it comes to data. Most organizations agree that they have data issues, categorized as dataquality. However, this definition is […].
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.
However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. In this article, we’ll dig into the core aspects of data integrity, what processes ensure it, and how to deal with data that doesn’t meet your standards.
The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.
DataArchitecture – Definition (2). Data Catalogue. Data Community. Data Domain (contributor: Taru Väre ). Data Enrichment. Data Federation. Data Function. Data Model. Data Operating Model. The Data & Analytics Dictionary will continue to be expanded in coming months.
Donna Burbank is a Data Management Consultant and acts as the Managing Director at Global Data Strategy, Ltd. Her Twitter page is filled with interesting articles, webinars, reports, and current news surrounding data management. TDAN stands for The Data Administration Newsletter. It is published by Robert S.
The goal of a data product is to solve the long-standing issue of data silos and dataquality. 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. His Amazon author page
There are many perennial issues with data: dataquality, data access, data provenance, and data meaning. I will contend in this article that the central issue around which these others revolve is data complexity. It’s the complexity of data that creates and perpetuates these other problems.
Control of Data to ensure it is Fit-for-Purpose. This refers to a wide range of activities from Data Governance to Data Management to DataQuality improvement and indeed related concepts such as Master Data Management. DataArchitecture / Infrastructure. Best practice has evolved in this area.
One of the greatest contributions to the understanding of dataquality and dataquality management happened in the 1980s when Stuart Madnick and Rich Wang at MIT adapted the concept of Total Quality Management (TQM) from manufacturing to Information Systems reframing it as Total DataQuality Management (TDQM).
In this article, we are bringing science fiction to the semantic technology (and data management) talk to shed some light on three common data challenges: the storage, retrieval and security of information. We will talk through these from the perspective of Linked Data (and cyberpunk).
Business has a fundamental problem with dataquality. In some places it’s merely painful, in others it’s nearly catastrophic. Why is the problem so pervasive? Why does it never seem to get fixed? I believe we’ve been thinking about the problem wrong. It’s time for a fresh look.
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). …and Fugue.
No this article has not escaped from my Maths & Science section , it is actually about data matters. The image at the start of this article is of an Ichthyosaur (top) and Dolphin. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation.
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.
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. Another article from peterjamesthomas.com. Another service we provide is writing White Papers for clients.
Data migration the process of transferring data from one system to another is a critical undertaking for organizations striving to upgrade infrastructure, consolidate systems, or adopt new technologies. However, data migration challenges can be very complex, especially when doing large-scale data migration projects.
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.
Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
“Technical debt” refers to the implied cost of future refactoring or rework to improve the quality of an asset to make it easy to understand, work with, maintain, and extend.
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. […].
The third and final part of the Non-Invasive Data Governance Framework details the breakdown of components by level, providing considerations for what must be included at the intersections. The squares are completed with nouns and verbs that provide direction for meaningful discussions about how the program will be set up and operate.
Gartner is explicit: Data catalogs play a foundational role in the data fabric. And leaders are recognizing the value of a strong data foundation. Indeed, the foundation of your dataarchitecture and strategy – and thus your business strategy – begins with choosing the best data catalog to support your business.
“…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
In today’s data-driven world, efficiently extracting data from large datasets is crucial for businesses to gain valuable insights and make informed decisions. As data volume, variety, and velocity grow, traditional extraction methods often need to catch up in handling massive and complex datasets.
Insightful and accurate data is the lifeblood of any successful business. There are many ways to ensure high-quality information in Salesforce Sales Cloud with regular cleansing routines that keep up-to date records for each […].
How many times, when you were a kid, did a thunderstorm cause an immediate mix of emotions: the fear of the continuous boom of the thunder as the clouds rolled in, the calming sensation of the smell of freshwater in the breeze and wind, and the awe and wonder as the lighting streaked through the […].
In an increasingly interconnected world, cybersecurity is of the utmost importance for many businesses. In fact, poor security isn’t just a hit to your reputation, it can also be expensive. Businesses of all sizes are looking for ways to mitigate these costs and prepare for cyberattacks.
Recently, I attended the CDIO Conference in Boston where I had the pleasure of hearing the two Toms (Tom Redman and Tom Davenport) — gurus of data — introduce the concept of tweeners to the data management world. As I listened to their explanation of a tweener (someone who sits with one foot in data […]
Welcome to DAMA Corner, a source of information for data management professionals here in TDAN.com, an industry-leading publication for people interested in learning about data administration, data management disciplines, and best practices.
We hope your Data Management career and programs are progressing well. If you have issues, please refer to DAMA.org for references, as well as the DAMA Data Management Body of Knowledge (DMBok). Good day from DAMA International. You can purchase the DMBoK at your favorite book source or via website link.
Cross-Agency Priority (CAP) Goal #2 is “leverage data as a strategic asset to grow the economy, increase the effectiveness of the Federal Government, facilitate oversight, and promote transparency.” The President’s Management Agenda (PMA) lays out a long-term vision for modernizing the Federal Government.
To succeed in todays landscape, every company small, mid-sized or large must embrace a data-centric mindset. This article proposes a methodology for organizations to implement a modern data management function that can be tailored to meet their unique needs. Implementing ML capabilities can help find the right thresholds.
When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” ” through a truly data literate organization. What is data democratization?
Increased data generation requires modern businesses to manage vast volumes of information. All this data holds immense potential for insights and informed decision-making, but its value depends on effective utilization. Lets take a closer look at data overload and […]
On 20 July 2023, Gartner released the article “ Innovation Insight: Data Observability Enables Proactive DataQuality ” by Melody Chien. It alerts data and analytics leaders to issues with their data before they multiply. It alerts data and analytics leaders to issues with their data before they multiply.
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