With spending on digital transformation initiatives worldwide projected to hit $3.9 trillion by 2027, the pressure is on organizations – and specifically the C-suite – to ensure that not only are they best positioned to tackle the digital challenges of today but that they can quickly adapt to those of tomorrow as well. C-suite leaders find themselves […]
12 Key AI Patterns for Improving Data Quality (DQ)
AI is the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. A typical AI system has five key building blocks [1]. 1. Data: Data is number, characters, images, audio, video, symbols, or any digital repository on which operations can be performed by a computer. 2. Algorithm: An algorithm […]
Data Logistics Mandates: Devising a Plan to Ensure Long-Term Data Access
Microsoft 365 has become the nucleus for many organizations for centralized communication and collaboration, especially large organizations with more than 1,000 full-time employees. One million companies globally use 365 and create 1.6 billion documents each day on the platform and in the next two years, that is expected to grow by 4.4 times, according to a […]
Maximizing AI’s Potential: High-Value Data Produces High-Quality Results
With the rapid development of artificial intelligence (AI) and large language models (LLMs), companies are rushing to incorporate automated technology into their networks and applications. However, as the age of automation persists, organizations must reassess the data on which their automated platforms are being trained. To maximize the potential of AI using sensitive data, we […]
Beyond the Basics: Advanced Tips for Effective Data Extraction
Data extraction is a cornerstone in data analytics, enabling organizations to extract valuable insights from raw data. While basic extraction techniques are fundamental, understanding advanced strategies is crucial for maximizing efficiency and accuracy. This article will explore advanced tips for effective data extraction, shedding light on automation tools, leveraging APIs and web scraping techniques, enhancing […]
Taming Access Creep: Strategies to Rein in Unnecessary Privileges
One of the most pervasive cybersecurity challenges is “access creep” – the gradual, often unnoticed accumulation of access privileges by employees beyond what their current role requires. This phenomenon occurs when initial access rights granted for specific roles are not revoked as employees change positions or their job duties evolve. Over time, this unchecked accrual […]
Data Privacy Through Robust Data Governance: Strategies and Best Practices
Today, more than ever, people are concerned about data privacy. Reflecting this, countries all over the world have introduced privacy laws – GDPR and CCPA being the biggest examples. These laws govern how businesses should collect, manage, and maintain data. This has prompted businesses to reevaluate their data collection operations. But to keep data private and secure businesses […]
Driving Data Governance: The Role of Data Strategy and Data Literacy Programs
In today’s data-driven world, organizations face increasing pressure to manage and govern their data assets effectively. Data governance plays a crucial role in ensuring that data is managed responsibly, securely, and in accordance with regulatory requirements. One key component and driver of successful data governance is the implementation of a robust data strategy coupled with […]
Ask a Data Ethicist: What Happens When Language Becomes Data?
At a recent presentation for a local post-secondary institution, I fielded a number of questions related to the use of language, primarily English language texts, as training data for generative AI. There were questions around cultural impacts and related ethical concerns. These queries were more nuanced than the usual ones I get around copyright or […]
MDM vs. CDP: Which Does Your Organization Need?
Most, if not all, organizations need help utilizing the data collected from various sources efficiently, thanks to the ever-evolving enterprise data management landscape. Often, the reasons include: 1) Data is collected and stored in siloed systems; 2) Different verticals or departments own different types of data; 3) Inconsistent data quality across the organization. Implementing a central […]