Newsletter - March 2025

The Myth of 100% Data Quality

With The myth of "100% data quality" is a classic one.

 It is not uncommon for teams to burn resources trying to reach an impossible standard when, in reality, fit-for-purpose data is often what matters more. 

Not every dataset needs to be flawless, but it does need to be reliable enough for the business decisions it supports.

And Modern Data 101’s point about over-reliance on tools is unfortunately often true! Technology is powerful, but without clear ownership and a strong data culture, even the best tools won’t fix underlying quality issues. 

That’s why it's important to consider…

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Is Data Everyone’s Problem?

When data is everyone’s problem but nobody’s responsibility, quality and accountability suffer!

It’s not uncommon for organisations to treat data like an afterthought, assuming IT will ‘clean it up’ or that quality issues will somehow resolve themselves. But, as Rosanne Werner suggests, without clear ownership and accountability, bad data becomes a systemic issue rather than a one-off problem.

What many organisations can probably resonate with here is the idea that data creators rarely see the downstream impact of their inputs.

 If teams don’t understand how their actions (or inaction) affect AI models or reporting, there’s little motivation to change. And when leadership doesn’t set clear quality targets, data management remains reactive rather than strategic.

Curious

Data Dashboards should be Fit for Purpose

Many dashboards fail because they try to do too much, cluttering the screen with unnecessary charts, vanity metrics, or confusing layouts.

That’s why we love Yassine Mahboub’s 33 practical rules for better dashboard design, covering planning, layout, data visualisation, color usage, interactivity, data integrity, and usability. 

The emphasis is on making dashboards clear and easy to navigate, ensuring they make things easier to understand rather than more complicated.

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Are Data Warehouses Dead?

Data governance remains a weak link in AI adoption with many companies still lacking structured frameworks, data literacy, and ethical policies.

And yet without proper governance, trying to use AI effectively is like building a house on quicksand. But what can be done? Where do data professionals start? 

Well, while all steps towards effective governance are important, some have a greater immediate impact on AI readiness:

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In person Northern Event - June

Please note, it’s only March and registration for this June event is already closed - a helpful reminder of just how popular these in-person meetups are and to register early next time one is heading your way! Keep an eye on our upcoming events here.

Open Webinar - Information Fitness - Ben Meade

With AI continuing to dominate business and leader discussions, those leaders that fail to create a robust data strategy and adopt good data governance risk not achieving lasting ROI and competitive agility.

Increasingly, organisations need a strategic and disciplined approach to acquiring, growing, refining, safeguarding and deploying data to improve the maturity of their enterprise data lifecycle as a perquisite for success, especially with AI.

The more successful leaders around the world are adopting a proactive approach to data management, emphasizing awareness and understanding across the organization. This shift frames data and analytics as a product that drives growth and innovation to manage risk and reduce costs (Gartner, 2023).

In this webinar, host Ben Meade will delve into the key factors that should be on every executive's radar when deciding which data creates the most business value, and how to effectively safeguard and manage this increasingly invaluable asset.

Ben is offering valuable takeaways well as making time for a Q&A session. So sign up with questions at the ready!

Exploring the synergy between master data management (MDM) and AI

Like it or not, AI is here to stay, demanding data governance which is more rigorous than ever.

In this webinar, Jesper Grode of Stibo Systems explores the powerful synergy between master data management (MDM) and AI. 

In this very timely session, Jesper equips you with up-to-date insights into how MDM and AI intersect; demonstrating how AI can optimize MDM processes to deliver outstanding business outcomes and showing the vital role high-quality master data plays in driving AI success along with the potential pitfalls of poor data quality.

The Business Impact of a Data-Literate Workforce

With data literacy being a fundamental driver of cultural transformation, decision-making, operational efficiency, and innovation, having a data literate workforce has never been more important to leveraging AI, analytics, and emerging technologies.

In this session, Katy and Farran explored the key benefits of Data Literacy – From fostering a confident, data-driven culture to reducing reliance on data teams and accelerating insights. And how to overcome common barriers such as fear of data, leadership buy-in, bridging technical and non-technical gaps, and managing time and resource constraints with scalable, persona-based learning strategies.

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Each of our webinars addresses a specific aspect of data management or the challenges and opportunities facing us data practitioners. They’re hugely popular and valuable so, if you didn’t catch them all,  why not take a look at our fantastic archive here DAMA UK - Past Public Events