As we enter 2026, Artificial Intelligence (AI) continues to expand in workplaces and processes. It’s a core driver of business transformation.   From predictive analytics to generative AI, organisations are leveraging AI to unlock efficiencies and deliver personalised experiences. But with this rapid adoption comes a critical question: How do we innovate responsibly while safeguarding data and maintaining compliance?  The answer lies in robust data governance. Without it, AI can… Read more: Data Governance for AI Applications: How Microsoft Solutions Enable Secure Innovation 

Artificial Intelligence is transforming the way we write code and GitHub Copilot is at the forefront of this change. Acting as an AI-powered pair programmer, Copilot is no longer just a novelty. It’s becoming an essential tool for developers seeking efficiency and innovation.  Traditional pair programming involves two developers working together to solve problems and… Read more: Github Copilot – a Developer’s assistant

Tools, Techniques, and Best Practices  In the era of Artificial Intelligence (AI) and Machine Learning (ML), data has become the lifeblood of innovation. Yet, the quality of that data determines whether your AI initiatives soar or stumble. This is where data profiling comes in : a foundational step that ensures your datasets are accurate, consistent,… Read more: Data Profiling for AI Success

Hallucinations occur when a language model generates content that sounds plausible but is factually incorrect or entirely made up. These errors often stem from the model’s design: most large language models (LLMs) are generalists, trained on vast datasets to handle a wide range of tasks rather than specialising in one domain.

Recent outages from major cloud providers like AWS and Azure have made headlines, exposing critical vulnerabilities in cloud architecture. These disruptions have caused significant operational challenges for businesses, prompting many to reassess their cloud strategies and explore alternative infrastructure models…Read more in my latest post #AI #Cloud #Onpremise #datastorage #MachineLearning

I am addressing data types in today’s issue. Occasionally, we hear suggestions that there is no such thing as unstructured data. Proponents argue that all data has structure. “It only depends on how you view the data” . Perhaps! Perhaps we do not have tools for all types of data. Perhaps the structure of data… Read more: Navigating Structured vs Unstructured Data for AI Applications

To assess and decide on applicable governance to Generative AI, organisations must first understand what AI model is available to them. They also need to determine how it will be used. Most organisations are looking to leverage LLMs(Large language models) to implement AI. Some organisations may also wish to develop their own bespoke models which… Read more: Effective Governance for Generative AI Adoption vol 2

Addressing common challenges of Generative AI; Generative AI like gpt , Gemini is widely used. AI continues to be incorporated with readily available tools suitable for everyday tasks. GenAI does have some issues now which lead to user frustration. Some of the frustrating things which users face with GenAI use include; The highlighted issues can… Read more: Effective Governance for Generative AI Adoption vol 1

AI – Artificial Intelligence has many controversial definitions. This can make it tricky for organisations to understand how AI fits into their processes. In a broad sense Artificial Intelligence is research into how machines can carry out human actions autonomously. Is this possible? I don’t entirely believe so but that is why AI is research.… Read more: Understanding AI: Using vs Doing

Data Perception is essential to understand and correctly define your organisational data. Data context is business user perception of its various business processes. Business areas often combine data from different business processes like Sales, Customer registrations, Budget and Procurement. These processes need to be organised to present a unified output. When organising data, each business… Read more: Data Context is your Data perception

What is Data Ownership? Data ownership broadly refers to designated business stakeholders who define how the business data will be used to achieve the desired business outcomes. Is data ownership necessary? With data ownership comes accountability and responsibility which may not be desirable for teams to take on however, it is a necessity for teams… Read more: Data Ownership

There is no doubt we have all come across AI be it in the news, education or our day to day activities. I have and remain cautiously optimistic about the positive impacts of AI on human life. Below, i am sharing why AI Data Governance should be front and centre of AI solutions. What is… Read more: AI Data Governance

Very often when organizations embark on Software or System projects, they do so without considering who should be responsible for the data created or stored in the applications. The question of whose responsibility the Program or Project is, should always form an essential input to planning. Information Technology management will mostly be focused on technology… Read more: Whose Budget is it Anyway?

The Certified Data Management Professional (CDMP) certification is a renowned exam offered by the Data Management Association. An international body for data practitioners. I took my CMP exam last week and I was very pleased to achieve the certification at first try albeit not achieving the score I aimed for. The CDMP certification has 3… Read more: CDMP Certification

Many organizations have a requirement to understand their business data, improve data quality and locate the right information across the organization. This could be for regulatory reporting purposes, auditing or general business processing. Indeed an organization can only assess its own performance by looking back at its data, current or historical to identify how it… Read more: Reliable Data with Master Data Management