To say data has “integrity” means that it can be trusted and relied upon and is ultimately useful. Data integrity also conveys a sense of unity and completeness. The greatest challenges to ensuring that data has integrity are any characteristics or events that detract from the data’s usefulness, trustworthiness, and reliability, as well as anything […]
Innovating with Data Mesh and Data Governance
Large organizations want to create a flexible environment to innovate and respond quickly based on new data insights. But at the same time, these businesses want some structure for good Data Quality, data fit for consumption, simplifying and speeding up data access. Using a data mesh, which is a decentralized data architecture (collecting, integrating, and analyzing […]
Navigating the Sea of Data Mapping Solutions
This is the third and final blog post in my “Charting a Course Through the Data Mapping Maze.” If you’ve been following the previous posts, thanks for joining me on this journey. Part one defined data mapping and outlined key components and why it’s essential. Part two explored how data mapping works, the common techniques used, […]
What Is Data Stewardship?
Data stewardship (DS) is the practice of overseeing an organization’s data assets to ensure they are accessible, reliable, and secure throughout their lifecycles. It is a framework of roles, responsibilities, and processes designed to support the organizational strategy through a data governance (DG) program. At its core, data stewardship comprises data stewards – formalized roles that take responsibility for their […]
Poor Data Fuels Interdepartmental Conflict and Organizational Inefficiency – Here’s What to Do
In a recent conversation with one of our customers, my team uncovered a troubling reality: Poor data quality wasn’t just impacting their bottom line but also causing friction between departments. In one instance, they explained how their marketing team could not run a campaign on time because they had to wait for the IT team […]
Getting Started with Data Quality
Imagine burning three trillion U.S. dollars. Businesses do this virtually every year because of poor data quality (DQ). In a data-driven age, organizations cannot afford to waste this time and money. Instead, they need to focus on achieving good data quality through a comprehensive program dedicated to business needs. But how does a company implement an effective […]
Three Steps Toward Quality, AI-Ready Data
AI is the new technology darling of the business world, and rightfully so. IDC found that companies that invest in AI realize an average return of $3.50 for every dollar spent. Generative AI alone could add the equivalent of $2.6 to $4.4 trillion annually to the global economy across the 63 use cases McKinsey and Company analyzed. The excitement […]
Putting Data Mapping to Work
In my previous blog post, I defined data mapping and its importance. Here, I explore how it works, the most popular techniques, and the common challenges that crop up and that teams must overcome to ensure the integrity and accuracy of the mapped data. Data mapping establishes relationships and connections between data elements so we can […]
3 AI Misconceptions That Are Constraining Real-World Results
The promise of artificial intelligence to catapult businesses to new heights in productivity, decision-making, and customer experience is very real. Unfortunately, a lot of the information about AI that enterprise leaders are using to guide decisions right now is not. For companies to achieve the true potential of emerging AI-driven technologies, we need to set […]
How to Become a Data Steward
An essential role in any data-driven organization, a data steward is expected to manage and protect valuable data resources while ensuring that the integrity of the data remains intact. A steward must ensure that the data is useful to the company and meets its business objectives. Data stewards are also responsible for monitoring the data […]