Unexpected (and unwanted) data transformation problems can result from 50 (or more) issues that can be seen in the table that’s referenced in this blog post (see below). This post is an introduction to many causes of data transformation defects and how to avoid them. Data transformations are a process of altering data and data-related […]
2025 Predictions in Machine Learning and AI
Is 2025 the year that the AI advances of recent years finally deliver significant results to corporate bottom lines worldwide? I believe the answer is yes. The technology has been ready for this shift for at least 18 months, but most users have yet to fully grasp the details needed to harness its full potential. […]
Thinking Like a Data Person: Strategies for Better Decision-Making
Today, data is no longer used for just operations and compliance; it is the driving force behind a company’s strategy and performance. To leverage data for improved business performance, employees must think like a “data person” – a data scientist or a decision scientist. But what exactly is a data person, and how can a […]
AI Data Retention Creates Environmental Stumbling Block
As artificial intelligence reshapes our world, an environmental crisis is building in its digital wake. Data center power demand is projected to surge 160% by 2030, potentially generating up to $149 billion in social costs, including resource depletion, environmental impact, and public health. While most of the conversations around this focus on the energy processing demands […]
Ask a Data Ethicist: How Can We Ethically Assess the Influence of AI Systems on Humans?
From entertainment to online shopping to chatbots, Al systems are exerting influence across many aspects of our lives. The impacts are widespread, shaping our belief systems, our voting decisions, and our well-being. Yet, not all influence is unethical, which leads to this question … How can we ethically assess the influence of AI systems on […]
Data Sips: Interview with Bob Seiner
Data Sips is a new video miniseries presented by Ippon Technologies and DATAVERSITY that showcases quick conversations with industry experts from last month’s Data Governance & Information Quality (DGIQ) Conference in Washington, D.C. The eighth episode of the Data Sips series features Bob Seiner, president and principal of KIK Consulting & Educational Services and the publisher emeritus […]
Data Governance: A Journey into AI Governance and Beyond
In today’s data-driven world, organizations are racing to leverage artificial intelligence (AI) to glean actionable insights, streamline processes, and gain a competitive edge. However, many initiatives fail to reach their full potential due to inadequate foundations in data governance. To create impactful AI solutions, organizations need not only clean, compliant, and well-managed data, but also […]
Mind the Gap: R.I.P. Data Governance
This is the first in an ongoing series exploring reimagining data governance. Happy New Year, and welcome back to Mind the Gap. OK, enough pleasantries. Recently, I’ve been thinking about the following observation: Many, if not most, organizations have been struggling with the same information management and data governance challenges for decades. Certainly, we’ve seen traction in some […]
Data Quality: Ensuring Data You Can Trust
In an increasingly data-driven world, having correct and dependable information that we use to make choices has never been more important. From companies wanting to streamline their operations to medical systems making life-or-death calls, data quality serves as the backbone supporting sound decision-making. However, poor data quality can result in misguided actions, wasted resources, and […]
SQL and the Relational Model: Enduring Standards in the Age of AI
In 1970, Ted Codd introduced the relational data model, which proposed representing data as tuples, grouped into relations, to allow for declarative methods to specify data. SQL was developed at IBM as a way to query relational databases. It is a declarative programming language, expressing what data is to be retrieved, as opposed to imperative programming languages […]