Search for:
DGIQ + AIGov Conference: Takeaways and Trending Topics in Data Quality


In this series of blog posts, I aim to share some key takeaways from the DGIQ + AIGov Conference 2024 held by DATAVERSITY. These takeaways include my overall professional impressions and a high-level review of the most prominent topics discussed in the conference’s core subject areas: data governance, data quality, and AI governance.  In the first blog post of […]

The post DGIQ + AIGov Conference: Takeaways and Trending Topics in Data Quality appeared first on DATAVERSITY.


Read More
Author: Irina Steenbeck

Unlocking AI Success: Creating a Winning Data Strategy


AI has the power to transform industries by analyzing massive datasets and automating complex processes. However, AI’s effectiveness is directly tied to the integrity of the data fueling it. Data governance is required to drive accountability around privacy and ethics, while poor quality data results in inaccurate AI outcomes, leading to customer dissatisfaction, delayed decisions, […]

The post Unlocking AI Success: Creating a Winning Data Strategy appeared first on DATAVERSITY.


Read More
Author: Cameron Ogden

Data Quality Metrics Best Practices


The amount of data we deal with has increased rapidly (close to 50TB, even for a small company), whereas 75% of leaders don’t trust their data for business decision-making. Though these are two different stats, the common denominator playing a role could be data quality. With new data flowing from almost every direction, there must be a yardstick or […]

The post Data Quality Metrics Best Practices appeared first on DATAVERSITY.


Read More
Author: Suresh P

The Art of Lean Governance: Moving Beyond Governance Buzzwords and Bling
This column will expand on a Systems Thinking approach to Data Governance and focus on process control. The vendors of myriad governance tools focus on metadata, dictionaries, and quality metrics. Their marketing is a sea of buzzwords and bling — bells and whistles. Yet, where is the evidence of adding actual business value, defined as […]


Read More
Author: Steve Zagoudis

Data Speaks for Itself: Data Quality Management in the Age of Language Models
Unsurprisingly, my last two columns discussed artificial intelligence (AI), specifically the impact of language models (LMs) on data curation. My August 2024 column, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality,” and my November 2024 column, “Data Validation, the Data Accuracy Imposter or Assistant?” addressed some of the […]


Read More
Author: Dr. John Talburt

Empowering Organizations Through Data Literacy, Governance, and Business Literacy
In my journey as a data management professional, I’ve come to believe that the road to becoming a truly data-centric organization is paved with more than just tools and policies — it’s about creating a culture where data literacy and business literacy thrive.  Data governance, long regarded as a compliance-driven function, is now the backbone […]


Read More
Author: Gopi Maren

Identifying and Addressing Data Overload
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. Without the right tools, frameworks, and strategies, even established companies risk being overwhelmed by data overload.  Let’s take a closer look at data overload and […]


Read More
Author: Irfan Gowani

The Challenges of Data Migration: Ensuring Smooth Transitions Between Systems
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.   Duplicate or missing data, system compatibility issues, data security problems, […]


Read More
Author: Ainsley Lawrence

From Input to Insight: How Quality Data Drives AI and Automation


More and more enterprises are looking to automation and AI to deliver new efficiencies and give their organizations an edge in the market. Data is the engine that powers both automation and AI. But data must be clean and user-friendly for these systems to work effectively and deliver on their promise.  Lots of organizations are […]

The post From Input to Insight: How Quality Data Drives AI and Automation appeared first on DATAVERSITY.


Read More
Author: Amol Dalvi

Beyond Ownership: Scaling AI with Optimized First-Party Data


Brands, publishers, MarTech vendors, and beyond recently gathered in NYC for Advertising Week and swapped ideas on the future of marketing and advertising. The overarching message from many brands was one we’ve heard before: First-party data is like gold, especially for personalization. But it takes more than “owning” the data to make it valuable. Scale and accuracy […]

The post Beyond Ownership: Scaling AI with Optimized First-Party Data appeared first on DATAVERSITY.


Read More
Author: Tara DeZao

Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database


You never know what’s going to happen when you click on a LinkedIn job posting button. I’m always on the lookout for interesting and impactful projects, and one in particular caught my attention: “Far North Enterprises, a global fabrication and distribution establishment, is looking to modernize a very old data environment.” I clicked the button […]

The post Mind the Gap: Architecting Santa’s List – The Naughty-Nice Database appeared first on DATAVERSITY.


Read More
Author: Mark Cooper

5 Data Management Tool and Technology Trends to Watch in 2025


The market surrounding data management tools and technologies is quite mature. After all, the typical business has been making extensive use of data to help streamline its operations and decision-making for years, and many companies have long had data management tools in place. But that doesn’t mean that little is happening in the world of […]

The post 5 Data Management Tool and Technology Trends to Watch in 2025 appeared first on DATAVERSITY.


Read More
Author: Matheus Dellagnelo

Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?
I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, I’ve seen a trend — more and more emphasis on AI.   I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]


Read More
Author: Randall Gordon

Data Insights Ensure Quality Data and Confident Decisions
Every business (large or small) creates and depends upon data. One hundred years ago, businesses looked to leaders and experts to strategize and to create operational goals. Decisions were based on opinion, guesswork, and a complicated mixture of notes and records reflecting historical results that may or may not be relevant to the future.  Today, […]


Read More
Author: Kartik Patel

Technical and Strategic Best Practices for Building Robust Data Platforms


In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. 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 data architecture, […]

The post Technical and Strategic Best Practices for Building Robust Data Platforms appeared first on DATAVERSITY.


Read More
Author: Alok Abhishek

Chatbot Quality Control: Why Data Hygiene Is a Necessity


The rush is on to deploy chatbots. Chatbots rely on data to power their outputs; however, companies that prioritize data quantity over quality risk creating systems that produce unreliable, inappropriate, and simply incorrect responses. Success in this field depends on rigorous data standards and ongoing quality control rather than simply accumulating more training data. When […]

The post Chatbot Quality Control: Why Data Hygiene Is a Necessity appeared first on DATAVERSITY.


Read More
Author: Todd Fisher

Data Speaks for Itself: Data Validation – Data Accuracy Imposter or Assistant?
In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM). Because data quality is measured in the degree […]


Read More
Author: Dr. John Talburt

The Art of Lean Governance: A Systems Thinking Approach to Data Governance
A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data quality approaches not utilizing these loops will fail to achieve the desired results, often worsening the problem.  Data Governance is about gaining trust and […]


Read More
Author: Steve Zagoudis

Data Errors in Financial Services: Addressing the Real Cost of Poor Data Quality
Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Even industry leaders like Charles Schwab and Citibank have been severely impacted by poor data management, revealing the urgent need for more effective data quality processes across the sector.  Key Examples of Data Quality Failures  — […]


Read More
Author: Angsuman Dutta

New Tools, New Tech, Same Roadblocks: Data Governance in the Age of AI


Organizations are racing to adopt AI for its promise of efficiency and insights, yet the path to successful AI integration remains fraught with obstacles. Despite advancements in tools like ChatGPT and Google’s Gemini, fundamental issues with data governance – such as high costs, poor data quality, and security concerns – continue to hinder progress. Stop me […]

The post New Tools, New Tech, Same Roadblocks: Data Governance in the Age of AI appeared first on DATAVERSITY.


Read More
Author: Bryan Eckle

Data Crime: Cartoon Signatures
I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of  data management to someone.   The Story  The state of Rhode […]


Read More
Author: Merrill Albert

Key Insights From the ISG Buyers Guide for Data Intelligence 2024

Modern data management requires a variety of technologies and tools to support the people responsible for ensuring that data is trustworthy and secure. Conquering the data challenge has led to a massive number of vendors offering solutions that promise to solve data issues.  

With the evolving vendor landscape, it can be difficult to know where to start. It can also be difficult to understand how to determine the best way to evaluate vendors to be sure you’re seeing a true representation of their capabilities—not just sales speak. When it comes to data intelligence, it can be difficult to even define what that means to your business.

With budgets continuously stretched even thinner and new demands placed on data, you need data technologies that meet your needs for performance, reliability, manageability, and validation. Likewise, you want to know that the product has a strong roadmap for your future and a reputation for service you can count on, giving you the confidence to meet current and future needs.

Independent Assessments Are Key to Informing Buying Decisions

Independent analyst reports and buying guides can help you make informed decisions when evaluating and ultimately purchasing software that aligns with your workloads and use cases. The reports offer unbiased, critical insights into the advantages and drawbacks of vendors’ products. The information cuts through marketing jargon to help you understand how technologies truly perform, helping you choose a solution with confidence.

These reports are typically based on thorough research and analysis, considering various factors such as product capabilities, customer satisfaction, and market performance. This objectivity helps you avoid the pitfalls of biased or incomplete information.

For example, the 2024 Buyers Guide for Data Intelligence by ISG Research, which provides authoritative market research and coverage on the business and IT aspects of the software industry, offers insights into several vendors’ products. The guide offers overall scoring of software providers across key categories, such as product experience, capabilities, usability, ROI, and more.

In addition to the overall guide, ISG Research offers multiple buyers guides that focus on specific areas of data intelligence, including data quality and data integration.

ISG Research Market View on Data Intelligence

Data intelligence is a comprehensive approach to managing and leveraging data across your organization. It combines several key components working seamlessly together to provide a holistic view of data assets and facilitate their effective use. 

The goal of data intelligence is to empower all users to access and make use of organizational data while ensuring its quality. As ISG Research noted in its Data Quality Buyers Guide, the data quality product category has traditionally been dominated by standalone products focused on assessing quality. 

“However, data quality functionality is also an essential component of data intelligence platforms that provide a holistic view of data production and consumption, as well as products that address other aspects of data intelligence, including data governance and master data management,” according to the guide.

Similarly, ISG Research’s Data Integration Buyers Guide notes the importance of bringing together data from all required sources. “Data integration is a fundamental enabler of a data intelligence strategy,” the guide points out.   

Companies across all industries are looking for ways to remove barriers to easily access data and enable it to be treated as an important asset that can be consumed across the organization and shared with external partners. To do this effectively and securely, you must consider various capabilities, including data integration, data quality, data catalogs, data lineage, and metadata management solutions.

These capabilities serve as the foundation of data intelligence. They streamline data access and make it easier for teams to consume trusted data for analytics and business intelligence that inform decision making.

ISG Research Criteria for Choosing Data Intelligence Vendors

ISG Research notes that software buying decisions should be based on research. “We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data integration technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential,” according to the company.  

In the 2024 Data Intelligence Buyers Guide, ISG​​ Research evaluated software and presented findings in key categories that are important to modern businesses. The evaluation offers a framework that allows you to shorten the cycle time when considering and purchasing software.

isg report 2024

For example, ISG Research encourages you to follow a process to ensure the best possible outcomes by:

  • Defining the business case and goals. Understand what you are trying to accomplish to justify the investment. This should include defining the specific needs of people, processes, and technology. Ventana Research, which is part of ISG Research, predicts that through 2026, three-quarters of enterprises will be engaged in data integrity initiatives to increase trust in their data.
  • Assessing technologies that align with business needs. Based on your business goals, you should determine the technological capabilities needed for success. This will ensure you maximize your technology investments and avoid paying for tools that you may not require. ISG Research notes that “too many capabilities may be a negative if they introduce unnecessary complexity.”
  • Including people and defining processes. While choosing the right software will help enforce data quality and facilitate getting data to more people across your organization, it’s important to consider the people who need to be involved in defining and maintaining data quality processes.
  • Evaluating and selecting technology properly. Determine the business and technology approach that best aligns with your requirements. This allows you to create criteria for meeting your needs, which can be used for evaluating technologies.

As ISG Research points out in its buyers guide, all the products it evaluated are feature-rich. However, not all the capabilities offered by a software provider are equally valuable to all types of users or support all business requirements needed to manage products on a continuous basis. That’s why it’s important to choose software based on your specific and unique needs.

Buy With Confidence

It can be difficult to keep up with the fast-changing landscape of data products. Independent analyst reports help by enabling you to make informed decisions with confidence.

Actian is providing complimentary access to the ISG Research Data Quality Buyers Guide that offers a detailed software provider and product assessment. Get your copy to find out why Actian is ranked in the “Exemplary” category.

If you’re looking for a single, unified data platform that offers data integration, data warehousing, data quality, and more at unmatched price-performance, Actian can help. Let’s talk. 

 

The post Key Insights From the ISG Buyers Guide for Data Intelligence 2024 appeared first on Actian.


Read More
Author: Actian Corporation

Charting a Course Through the Data Mapping Maze in Three Parts


Companies are dealing with more data sources than ever – sales figures, customer profiles, inventory updates, you name it. Data professionals say, on average, data volumes are growing by 63% per month in their organizations. Data teams are struggling to ensure all that data hangs together across systems and is accurate and consistent.  Bad data is bad […]

The post Charting a Course Through the Data Mapping Maze in Three Parts appeared first on DATAVERSITY.


Read More
Author: Eric Crane

Data Quality: The Hidden Cornerstone of Digital Transformation Success
As organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality. In the race to implement cutting-edge technologies and overhaul business processes, many companies fail to recognize that the success of these efforts hinges on the accuracy, completeness, and reliability of their underlying data. This oversight can lead to disastrous […]


Read More
Author: Christine Haskell

RSS
YouTube
LinkedIn
Share