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Robert Seiner and Anthony Algmin faced off – in a virtual sense – at the DATAVERSITY® Enterprise Data World Conference to determine which is more important: Data Governance, Data Leadership, or DataArchitecture. The post Data Governance, Data Leadership or DataArchitecture: What Matters Most?
In this blog, we will take a look at: The impact poor DataQuality has on organizations and practical advice for how to overcome this challenge through the use of feedback loops. Poor DataQuality can cost organizations millions each year. It can lead to incorrect decisions, […].
In fact, it’s been more than three decades of innovation in this market, resulting in the development of thousands of data tools and a global data preparation tools market size that’s set […] The post Why Is DataQuality Still So Hard to Achieve? appeared first on DATAVERSITY.
The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess DataQuality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.
Learn about data strategy pitfalls A few words about data strategy Elements of Strategy A solid strategy outlines how an organization collects, processes, analyzes, and uses data to achieve its goals. You will find my business analysis digest, my articles, and more! Is that your first visit to Passionate BA?
How can your company redesign its dataarchitecture without making the same mistakes all over again? The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all dataarchitecture, and […].
This statistic underscores the urgent need for robust data platforms and governance frameworks. A successful data strategy outlines best practices and establishes a clear vision for dataarchitecture, […] The post Technical and Strategic Best Practices for Building Robust Data Platforms appeared first on DATAVERSITY.
With the accelerating adoption of Snowflake as the cloud data warehouse of choice, the need for autonomously validating data has become critical. While existing DataQuality solutions provide the ability to validate Snowflake data, these solutions rely on a rule-based approach that is […].
If data is the new oil, then high-qualitydata is the new black gold. Just like with oil, if you don’t have good dataquality, you will not get very far. So, what can you do to ensure your data is up to par and […]. You might not even make it out of the starting gate.
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 […]
In the context of a large system integration project, we are talking about awareness of: 1) DataQuality expectations and metrics, 2) Enterprise Data Management plan, 3) Data Governance best practices, 4) data risk factors, 5) Data Governance framework, 6) data owners/data consumers, 7) DataArchitecture principles, 8) […].
Data fabric is redefining enterprise data management by connecting distributed data sources, offering speedy data access, and strengthening dataquality and governance. This article gives an expert outlook on the key ingredients that go into building […].
If you’re trying to determine whether you need a data lake, a data warehouse, or possibly even both, you’ll want to understand the functionality of each tool and their differences. This article will highlight the differences between each and how […].
The ways in which we store and manage data have grown exponentially over recent years – and continue to evolve into new paradigms. For much of IT history, though, enterprise dataarchitecture has existed as monolithic, centralized “data lakes.” The post Data Mesh or Data Mess?
In the fast-evolving data landscape, understanding emerging trends and embracing technological advancements are key to staying ahead. As we approach 2024, this article explores the data trends that will define the strategic landscape for the coming year.
Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where dataquality testing comes in.
A data lake becomes a data swamp in the absence of comprehensive dataquality validation and does not offer a clear link to value creation. Organizations are rapidly adopting the cloud data lake as the data lake of choice, and the need for validating data in real time has become critical.
Today’s data pipelines use transformations to convert raw data into meaningful insights. Yet, ensuring the accuracy and reliability of these transformations is no small feat – tools and methods to test the variety of data and transformation can be daunting.
Ransomware in particular continues to vex enterprises, and unstructured data is a vast, largely unprotected asset. In 2025, preventing risks from both cyber criminals and AI use will be top mandates for most CIOs.
Unexpected (and unwanted) data transformation problems can result from 50 (or more) issues that can be seen in the table thats referenced in this blog post (see below). This post is an introduction to many causes of data transformation defects and how to avoid them.
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.
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? Dataquality: Gone are the days of “data is data, and we just need more.” Data modeling.
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.
Instead of starting data protection strategies by planning backups, organizations should flip their mindset and start by planning recovery: What data needs to be recovered first? What systems […] The post World Backup Day Is So 2023 – How About World Data Resilience Day?
My company’s 2024 Data Protection Trends report revealed that 75% of organizations experience […] The post Understanding the Importance of Data Resilience appeared first on DATAVERSITY. In recent years, the frequency and sophistication of cyberattacks have surged, presenting a formidable challenge to organizations worldwide.
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? Dataquality: Gone are the days of “data is data, and we just need more.” Data modeling.
In todays digital age, managing and minimizing data collection is essential for maintaining business security. Prioritizing data privacy helps organizations ensure they only gather necessary information, reducing the risk of data breaches and misuse.
In todays rapidly evolving global landscape, data sovereignty has emerged as a critical challenge for enterprises. Businesses must adapt to an increasingly complex web of requirements as countries around the world tighten data regulations in an effort to ensure compliance and protect against cyberattacks.
Master data lays the foundation for your supplier and customer relationships. However, teams often fail to reap the full benefits […] The post How to Win the War Against Bad Master Data appeared first on DATAVERSITY.
Organizations learned a valuable lesson in 2023: It isn’t sufficient to rely on securing data once it has landed in a cloud data warehouse or analytical store. As a result, data owners are highly motivated to explore technologies in 2024 that can protect data from the moment it begins its journey in the source systems.
In our increasingly digital world, organizations recognize the importance of securing their data. As cloud-based technologies proliferate, the need for a robust identity and access management (IAM) strategy is more critical than ever.
This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month, we’re talking about data mesh. Data mesh represents a federated model of running your data program. I’m Mark Horseman, and welcome to The Cool Kids Corner.
OpenAI launched generative AI (GenAI) into the mainstream last year, and we haven’t stopped talking about it since – and for good reason. When done right, its benefits are indisputable, saving businesses time, money, and resources. Industries from customer service to technology are experiencing the shift.
Companies are spending a lot of money on data and analytics capabilities, creating more and more data products for people inside and outside the company. These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another.
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.
Technology generates more and more data, regulators need to exercise more and more control, digital transformation is advancing, and traditional firms are changing and need to respond quickly to the new demands of regulators – not only to avoid sanctions but also to guard their processes and avoid security breaches and inconsistencies in their information (..)
Generating actionable insights across growing data volumes and disconnected data silos is becoming increasingly challenging for organizations. Working across data islands leads to siloed thinking and the inability to implement critical business initiatives such as Customer, Product, or Asset 360.
Maintaining high-quality, error-free data. Many business teams do not have a clear understanding of who is responsible for maintaining dataquality. And should duplicate data or errors be found, many do not know where to report quality issues. Managing permissions, access, and governance at scale.
According to Gartner, data fabric is an architecture and set of data services that provides consistent functionality across a variety of environments, from on-premises to the cloud. Data fabric simplifies and integrates on-premises and cloud Data Management by accelerating digital transformation.
The goal of digital transformation remains the same as ever – to become more data-driven. We have learned how to gain a competitive advantage by capturing business events in data. Events are data snap-shots of complex activity sourced from the web, customer systems, ERP transactions, social media, […].
Here’s an important truth: There is no data privacy without data protection. Consumers and companies place their trust in the organizations they do business with and trust that their sensitive data will be kept private.
DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. Click to learn more about author Wayne Yaddow. ETL projects are increasingly based on agile processes and automated testing.
Click to learn more about author Balaji Ganesan. Sources indicate 40% more Americans will travel in 2021 than those in 2020, meaning travel companies will collect an enormous amount of personally identifiable information (PII) from passengers engaging in “revenge” travel.
We are living in turbulent times. Online security has always been an area of concern; however, with recent global events, the world we now live in has become increasingly cloud-centric.
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