Those who want to design universal data pipelines and ETL testing tools face a tough challenge because of the vastness and variety of technologies: Each data pipeline platform embodies a unique philosophy, architectural design, and set of operations. Some platforms are centered around batch processing, while others are centered around real-time streaming. While the nuances […]
Maximizing IT Investments and Enhancing End-User Experience with Data
In an age defined by data-driven decision-making, where 91.9% of organizations have already leveraged analytics to enhance their operations, a question remains: What if there were even more sources of untapped data, capable of helping businesses increase the quality of the end-user experience and elevating the functionality of systems, applications, and cloud investments within their business? This is […]
Building a Strong Community for Women in Data Management and Governance
In September, I had the privilege of co-hosting a new special interest group (SIG), Women in Data Management and Governance, alongside DATAVERSITY’s Shannon Kempe, at a pre-conference Enterprise Data World (EDW) event. I’m so honored to be part of building this community and to better serve a critical and growing constituency in our industry. Supporting the growth of […]
Managing Missing Data in Analytics
Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data […]
Data Governors, First Govern Yourselves
Data Governance, as currently practiced, is failing. There have been some successes, but by and large, even these efforts have fallen short. Worse, many of those tasked with contributing to Data Governance find the effort painful. We have enormous sympathy for data governors. (We use the term “data governors” – DGs – as the most […]
Why Are Companies Demanding DLP Functionality?
In an age where data breaches, cyber threats, and privacy violations are commonplace, companies are placing greater emphasis on safeguarding their digital assets. Data Loss Prevention (DLP) functionality has emerged as a critical tool in this endeavor. Although we all understand the consequences and the benefits of protecting data, it is interesting to delve into what’s […]
Transforming Data Management with AI-Driven Data Catalogs
In today’s data-driven world, where every byte of information holds untapped potential, effective Data Management has become a central component of successful businesses. The ability to collect and analyze data to gain valuable insights is the basis of informed decision-making, innovation, and competitive advantage. According to recent research by Accenture, only 25% of organizations are […]
How Low-Code FileOps Ensures a Seamless Digital Transformation
In an era where data stands as the driving force behind the sweeping wave of digital transformation and GenAI initiatives, FileOps is emerging as a true game-changer. Defined as a low-code/no-code methodology for performing and streamlining file operations, FileOps enables organizations to expedite their digital transformation and GenAI initiatives by empowering them to effectively manage […]
AI at the Edge: Creating a Successful Strategy
The recent hype surrounding AI makes every organization feel like they must rethink their strategy to ensure they are aligned with the market expectations and not let the competition gain an advantage. AI has been in the news for a while, but when ChatGPT was introduced, people outside of business started to explore the technology […]
The Cool Kids Corner: Data Quality Is Not a Fish You Can Catch
Hello! I’m Mark Horseman, and welcome to The Cool Kids Corner. This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. (Read last month’s column here.) This month we’re talking about Data Quality (DQ). Data Quality, the phrase “garbage in, garbage out,” dirty data, data […]