Every month in Angela’s Advice, we’ll be sharing answers to the questions that matter most to you as apprentices. From balancing study with work, to making sense of data projects, this is your space for honest, practical advice. Here’s what’s on your mind this month…

Dear Angela, I’m halfway through my apprenticeship and I’m starting to worry about my portfolio. I’ve got bits and pieces, but it doesn’t feel like it’s coming together. What should I do? — Stressed in Sheffield

Hi Stressed, First of all, take a deep breath. You’re not alone! Many learners feel overwhelmed at this stage, but the good news is that you’ve already started, and that’s half the battle. Here are three things you can do right now to get back on track:

1. Map your evidence to the criteria – Don’t just collect work; make sure each piece clearly links to what’s being assessed.

2. Quality over quantity – A few strong, well-explained examples are better than a mountain of vague ones.

3. Ask for feedback early – Your Tutor is here to help. Don’t wait until the end to get input.

Remember, your portfolio is a story of your learning journey. It doesn’t have to be perfect—it just has to be yours.

You’ve got this!

Angela

Dear Angela, I’ve fallen behind on my portfolio and I’m worried I won’t catch up in time. I feel overwhelmed and don’t know where to start. — Overwhelmed in Oxford

Hi Overwhelmed, First, you’re not alone—many learners hit this wall. The key is to prioritise and plan.

· Start by listing what evidence you already have.

· Match it to the relevant KSBs (Knowledge, Skills, Behaviours).

· Then, create a simple timeline—just one or two tasks per week.

Small steps lead to big progress. And remember, your Tutor is here to support you. You’ve got this!

Angela

Dear Angela, I’ve done my project, but I’m stuck on the write-up. I don’t know how much detail to include or what the assessors are looking for. — Blank Page in Birmingham

Hi Blank Page, Think of your write-up as telling the story of your project. The assessors want to see:

· What problem you were solving

· How you approached it

· What tools and techniques you used

· What insights you found

· What impact it had

Use clear headings, include visuals where possible, and explain your thinking—not just what you did, but why you did it. If you can teach someone else what you learned, you’re on the right track.

Angela

This month, we welcomed a new member to our Marketing and Sales team. Beth has joined us as our Business Development Manager, and we sat down for a chat to learn more about her background and experience.

Can you tell us a bit about your background and what brought you to Projecting Success?

I’ve come from a pretty diverse background that’s always had a strong focus on people, partnerships, and problem-solving. I started out in retail and hospitality management, where I developed a solid foundation in leading teams and hitting commercial targets in fast-paced environments. From there, I moved into telecoms, working as a Project Coordinator where I worked closely with major networks and saw firsthand the importance of effective project delivery.

My move into the training and education sector started with another apprenticeship provider, where I worked as both a Learning Coach and Partnership Associate. That combination gave me a 360-degree view of the apprenticeship journey, supporting learners directly and working with employers to create meaningful training solutions. A big part of that was managing accounts in the Civil Service, which taught me just how varied client needs can be, even under the same umbrella. It reinforced the importance of a flexible, consultative approach to business development.

What brought me to Projecting Success was the chance to bring all of that experience together in a role that’s genuinely forward-thinking. The blend of data, AI, and apprenticeships really stood out to me. It’s not just about delivering training, it’s about helping organisations future-proof their project delivery through smarter, data-driven approaches. That mission really clicked with me, and I knew it was something I wanted to be part of.

What attracted you to the Business Development Manager role here?

What excites me the most? Honestly, it’s the chance to be part of something that’s genuinely shifting the landscape. We’re not just selling apprenticeships; we’re opening doors for organisations to embrace the power of data and AI through emerging talent. I’m particularly looking forward to getting stuck into the strategy side, identifying new sectors we can support and tailoring how we position our offer to make sure it really resonates. Plus, working alongside such a forward-thinking team means I’ll be constantly learning and adapting, which is something I really value.

What does your role involve, and what are you most excited to get started on?

I’m essentially at the forefront of driving growth and expanding our reach when it comes to data and AI apprenticeships. It’s all about forging new partnerships with employers, identifying fresh opportunities across industries, and promoting our

programmes to the right audiences. I’ll be building relationships with businesses, industry bodies, and stakeholders, basically anyone who’s just as passionate about transforming project delivery through data as we are. AI might feel like the latest buzzword, but it’s been around for years, and the more we can support organisations in adopting these tools to streamline their processes, the better. I was particularly drawn to the strategic side of the role, spotting new sectors we can support, shaping how we position our offer, and ultimately helping businesses work smarter. And, of course, being part of a forward-thinking team where learning and innovation are front and centre makes me excited to get started.

What previous experience are you bringing to this role?

I’m bringing quite a varied background to this role, which I think gives me a well-rounded perspective. Most recently, I worked for a large training provider where I had a was as a Partnership Associate which involved managing key accounts and driving new business. Previously I was also a Learning Coach, supporting apprentices through their programmes. So I’ve seen the apprenticeship journey from both the delivery and development sides, which has really shaped my understanding of what employers and learners need to succeed. Before that, I worked in telecoms as a Project Coordinator on a large-scale upgrade project, collaborating with major networks and navigating the kind of complex stakeholder environments that really test your communication and organisational skills. I also have hands-on management experience from roles in both retail and hospitality, where leading teams and hitting targets were daily priorities. I’ve got strong account management experience from running a beauty account in a major health and beauty retailer which taught me how to balance commercial goals with customer relationships. All of this has helped build my ability to connect with people, spot opportunities, and drive results which are skills I’m excited to bring to Projecting Success.

How has your past work shaped your approach to business development?

It’s definitely given me a flexible and tailored approach to business development. While working as a Partnership Associate, I managed accounts for the Civil Service contract and that experience really opened my eyes to how varied client needs can be, even within the same sector. Different departments operated like separate businesses, each with their own priorities, structures, and ways of working. It taught me that there’s no one-size-fits-all solution, and the key to successful business development is really listening and understanding each client’s unique position, and adapting the offer accordingly. I’ve always approached business development as more than just selling, it’s about building trust, solving problems, and creating long-term value.

What trends in business development or sales are you currently most interested in?

One of the trends I’m most interested in at the moment is how data and AI are being used to enhance business development within the project delivery space. With more organisations looking to work smarter, tools that can analyse project performance, predict risk, and identify areas for automation are opening up new conversations around how we add value. It’s not just about selling a service anymore, it’s about showing how it can directly impact delivery outcomes, reduce costs, and improve confidence in hitting deadlines. Project delivery teams are under increasing pressure to do more with less, so they need solutions that are tailored, measurable, and aligned with their strategic goals. That means understanding the client’s specific delivery challenges, whether it’s around upskilling, talent pipelines, or digital transformation, and offering a practical, future-focused solution. It’s exciting to be part of a movement that’s focused on real impact, not just ticking boxes.

What are your early impressions of the team and the culture here?

My early impressions of the team and culture here have been overwhelmingly positive. There’s genuine collaboration and everyone is keen to support each other, and share ideas, it’s clear that agile teamwork is a big part of how things get done here, and there’s a real openness to trying new approaches and adapting quickly when needed.

What I’ve also really appreciated is how approachable and knowledgeable everyone is. No matter how busy things get, the team always makes time to share insights, offer

support, or just have a quick catch-up. It’s the kind of environment where you feel empowered to contribute but also encouraged to keep learning.

What’s one fun or unexpected fact about you?

Most people are surprised to find out I’m a big ice hockey fan! I’m usually known as the “pink Barbie girl” of the group, so it throws people off when I say I love being rink side, right next to the action. There’s just something about the energy of a live game, the speed, the atmosphere and the unpredictability, I absolutely love it!

Do you have a favourite quote or piece of advice that guides your work?

"People don’t buy what you do; they buy why you do it." – Simon Sinek

This quote really resonates with me as it’s a great reminder that success isn’t just about the product or service, it’s about the purpose behind it. When you’re passionate about what you’re offering and genuinely believe in the impact it can have, that comes across in every conversation.

LLMs & Data Generations Power Couple Series

Part 1

Author: Ronald Dyer

The advent of LLMs has created several opportunities for organisations to automate routine processes and increase productivity. This has been a boon both for the technology driven AI companies as well as their users. While large language models (LLMs) and agents are becoming more prevalent for routine tasks there remain some missed opportunities. One such opportunity is the use of LLMs to create synthetic datasets.

What is synthetic data?

Synthetic data is data that is artificially generated to mimic real-world data generally utilizing AI techniques. Its value rest primarily in being a placeholder in scenarios where real-world data cannot or should not be used due to GDPR or other primary data safety concerns. They can also be used in machine learning as training data where real data is scarce or unavailable. They fall into three main categories:

1. Fully Synthetic – AI generated that includes no real-world data identifiers

2. Partially Synthetic – Partly derived from real-world information but portions are replaced that contain sensitive information

3. Hybrid – Combining real datasets with synthetic to create insights without identification of sensitive organizational specific information

Applicability to Project Management/Project Data Analytics

A probable question might be: What is the value of synthetic data creation in project management? The answer rest with the ability to create a “digital twin” of real project datasets that potentially present risk of security if used wholesale in AI environments. By developing synthetic twins of datasets project organsations and by extension project managers can leverage synthetic data to create improved delivery scenarios and pre-determine potential outcomes. Moreover, they do so without risking the safety of their proprietary datasets, which can potentially end up in the wrong hands given historical issues with LLMs.

Scenario: NovaStruct Construction is embarking on a smart-rail expansion project as part of UK critical infrastructure development. The project includes several complexities, with fluctuating resources, unpredictability and access to the location of critical rail infrastructure data. They wish to model potential risk to critical rail infrastructure in an AI-based environment as part of scenario analysis without revealing the location of critical assets. NovaStruct uses a custom LLM-based synthetic data generator to simulate realistic project scenarios. Here's how:

1. Data Augmentation for Risk Models: training the LLM on existing project documentation, timelines, and incident reports using pseudocodes & classifications.

2. Scenario Planning: Project managers use these to run “what-if” simulations and stress-test contingency plans on critical assets based on weather, terrorism etc., without revealing real locations.

3. Training AI Assistants: NovaStruct utilizes the trained synthetic data to create a project assistant chatbot to support Q&A with actual delivery

Utilising these approaches NovaStruct can generate multiple scenario-based outcomes to support delivery that can reduce project delivery forecasting accuracy, decrease reliance on incomplete historical data and speed project manager onboarding using simulated scenarios.

Synthetic Data Benefits

Customisation – tailor the data to meet the specification of a project even before actual delivery to assess possible outcomes

Efficiency – reduced the time to test scenarios using real data gathering techniques

Increases/Maintains data privacy – Data resembles real world but is a facsimile

Richer Data – allows for multiple layers of augmentation

Conclusion

Key considerations to start utilising synthetic data are:

1. Start small – Utilise smaller projects as test cases for dataset generation

2. Build ethics and bias reduction in at the onset – by incorporating lessons learned from previous projects of similar design (remember to anonymise critical components)

3. Verify – Check/test data to validate post generation through robust project workflow development

About the Author

Ronald Dyer is a Senior Data Analyst Tutor with a passion for helping others master data-driven decision-makin

When it comes to the future of project delivery, data and AI are at the forefront. For Nermeen Latif, Technical Director and Digital Solutions Lead at WSP, the decision to join the Level 7 AI Data Specialist Apprenticeship was about staying ahead, driving innovation, and helping her organisation embrace new possibilities.

Life Before the Apprenticeship

With over 25 years in PMO, programme management, and project controls, Nermeen’s role was already multifaceted: balancing team management, business strategy, and delivering projects. While digital tools were already becoming the norm, she recognised that the industry was still largely focused on descriptive analytics.

She wanted to go beyond reporting the past and start shaping the future with predictive and AI-driven insights.

Why the Level 7?

WSP, as a large employer, pays the apprenticeship levy, so for Nermeen, the opportunity to upskill through the Level 7 apprenticeship was a natural step.

“I wanted to understand what needs to be done, what foundations need to be in place, and how AI can truly help project delivery. The course helps you look under the hood of complex models and appreciate the work that goes into building them.”

Her strong technical background, including a Master’s in Computer Science, made Level 7 the perfect fit over a Level 4.

Balancing Work, Study & Life

Like many senior professionals, Nermeen had concerns about balancing a full-time leadership role with studying. However, the apprenticeship structure, dedicated study time, live online sessions every other week, and practical assignments, made it possible.

She emphasises the importance of employer support and creating space for learning alongside work commitments.

Key Learnings and Lightbulb Moments

One of Nermeen’s biggest takeaways so far?

“It’s all about data, data, data. Whether you’re using AI or not, structured, well-governed data is the foundation for trustworthy decision-making.”

From understanding bias in models to seeing how neural networks could be applied in project delivery, the course has given her the tools to conceptualise real-world applications for AI in her organisation.

Impact and Opportunities

While still completing the programme, Nermeen is already seeing the benefits:

• Growing confidence in sharing knowledge at conferences and industry events.
• Helping demystify AI for project professionals—making it accessible rather than intimidating.
• Identifying how AI could improve project scope development, business case validation, and real-time project delivery decisions.

She also plans to cascade learnings to her team, embedding both data literacy and AI literacy across WSP.

Advice to Future Learners

For anyone considering the apprenticeship, Nermeen has one clear message:

“Absolutely do it. Don’t be afraid. Most people come with no coding background, and Python is very intuitive. The course makes AI approachable and relevant for everyone in project delivery.”

What’s Next?

Nermeen’s vision is to use her learning to help WSP and the wider industry move beyond describing the past, toward using AI for better, faster, and more confident decisions in project delivery.

Watch the full case study interview with Nermeen on YouTube

Interested in enrolling in the Level 7 AI Data Specialist Apprenticeship?

At Projecting Success, we’re proud to work with industry experts who bring a wealth of knowledge and practical experience to their teaching. One of these experts is Ron Dyer, a Monitoring & Evaluations project professional with more than 20 years of experience spanning technology, education, agriculture, energy, and finance project management.

Ron specializes in risk analysis, project performance, and lessons learned, skills that directly impact how organizations can grow, adapt, and deliver results with resilience.

Areas of Expertise

Ron brings both practical and academic expertise into the classroom. His background includes:

• Lessons Learned Evaluation – including sentiment analysis for project performance improvement
• Risk Modeling & Analytics – applying diagnostic-level data analytics to assess and mitigate risks
• Logical Framework Development – capturing and structuring project data for better outcomes
• Project Feasibility Assessment – evaluating the viability of new initiatives
• Applied Research – contributing to publications, case studies, and thought leadership in project management

Teaching Focus

Ron is passionate about equipping learners with hands-on, practical tools that translate directly into their roles. His teaching focuses include:

• Data Cleaning & Transformation – using MS Excel and Power Query to prepare high-quality data
• Data Visualization – bringing insights to life through Power BI, Looker Studio, and Tableau
• Statistical Methods – applying descriptive and diagnostic approaches to interpret data
• AI Prompt & Content Design – exploring how AI can support data-driven project success

Why Ron is Excited to Support MoD Learners

For Ron, working with Ministry of Defence (MoD) learners is both an opportunity and a privilege:

“MoD represents an opportunity to stretch my skills set to support development of learners in a leading-edge sector. The ability to support competence towards improved analytical thinking augurs well for national defense strategy goals as well as employee capacity and capability development for organizational resilience.”

Through his expertise, Ron is committed to helping MoD learners strengthen their analytical thinking, enhance project delivery, and ultimately contribute to organizational and national objectives.

Outside of data and project management, Ron is an an avid baker and chef who enjoys experimenting in the kitchen.

In today’s data-driven world, organisations across all industry verticals are under increasing pressure to enhance their project delivery capabilities. With skills gaps widening, particularly in data analytics, upskilling a workforce through apprenticeship programs, present a powerful, government-backed opportunity to upskill teams, increase retention, and futureproof organisations. Yet despite the benefits, apprenticeship uptake remains frustratingly low in many sectors, including project delivery. 

Why? 

This Blog explores the top barriers to apprenticeship uptake and, crucially, how we can overcome them as training providers, employer- partners, and apprenticeship advocates. 

Misconceptions surrounding apprenticeships

Many still associate apprenticeships with entry-level school leavers or trades. This outdated perception leads decision-makers to overlook apprenticeships as a viable route for professional development — especially in white-collar roles like data analytics and project management. 

How to Overcome It: 

• Change the language. Refer to “professional upskilling” or “fully funded training” in initial conversations before introducing the apprenticeship route. 

• Showcase real examples. Use case studies of professionals who have been upskilled through apprenticeships in data and digital roles. 

Lack of Awareness About Funding and Levy Use

Many organisations, particularly SMEs, are unaware that apprenticeship training is either fully or 95% funded. Even larger employers with levy pots often let these funds expire without using them. 

How to Overcome It: 

• Educate HR and Finance teams. We can help you run webinars, create short guides, or offer free consultations to explain the funding model. 

• Map ROI to business outcomes. Show how using the apprenticeship levy reduces L&D costs while improving delivery capacity- Projecting Success can support you with this. 

Time Concerns

Managers and stakeholders worry that taking staff off the job for training will disrupt delivery. Some believe apprenticeships are too time-consuming or inflexible. 

How to Overcome It: 

• Make it bitesize chunks. Break down the 20%-off-the-job requirement into manageable components (e.g. 1 day/week). 

• Link apprenticeship projects to day-to-day work. Our apprentices’ are encouraged to use their apprenticeship projects for real world project delivery challenges, providing immediate business impact. 

• Understand that this time invested is performance-enhancing. Upskilling a stakeholder might take time to start with but will give them the skills to be more efficient in their role, saving time in the long run as well as enabling better decision-making. 

Difficulty Identifying Suitable Candidates

Some organisations don’t know how to identify who would benefit most from an apprenticeship. Others assume existing staff are too senior or too junior. 

How to Overcome It: 

• Offer skills audits. Projecting Success can provide data maturity surveys to help teams map skills gaps and align them with programmes. 

• Target underleveraged talent. Apprenticeships can be a great fit for career pivoters, aspiring analysts, or project coordinators moving into data-heavy roles. 

• Build internal advocates. Equip HR and team leads with checklists or guidance to spot ideal candidates. 

Employer Bureacracy and procurement barriers

Some larger employers struggle with lengthy procurement processes, especially when adding new training providers or working through Digital Apprenticeship Service (DAS) steps. 

How to Overcome It: 

• Provide a white-glove service. We can support every stage from procurement to learner onboarding. 

• Streamline documentation. Our programme specs, pricing, and onboarding guides are easily accessible. 

lack of urgency or competing priorities

Training often takes a backseat to short-term delivery pressures — particularly in fast-paced project environments. 

How to Overcome It: 

• Connect to strategic goals. Senior leaders are the key here: Framing apprenticeships as tools for delivery excellence, risk reduction, and digital transformation. A “from the top” approach will promote long-term thinking. Highlight how upskilling reduces reliance on contractors and supports succession planning. 

• Use success stories. We can provide testimonials and case studies from similar organisations to help inspire action. 

Shifting Mindsets, Unlocking Potential 

Apprenticeships are one of the most powerful, yet underutilised tools in the UK’s project delivery workforce development toolkit. As training providers, employer- partners and apprenticeship advocates we have a responsibility to tackle these barriers head-on with empathy, clarity, and strategic messaging to unlock the value of data analytics training and future-ready skills for the project delivery professional. 

Introduce yourself

Hi! I’m Scott Daley I work for The Ministry of Defence as a Programme Data Manager. I am currently on the Level 4 Data Analyst Apprenticeship and started in September 2024.

Why did you choose to do an apprenticeship?

I was drawn to this opportunity as I am looking to improve my skills and CPD with the ambition to obtain a degree. This stood out to me as my previous roles were in Commercial/ Contract Analysis, I wanted to improve my understanding of Project Analysis coming into my current role so I could go above and beyond to provide data driven insights to my department. I attended an open forum hosted by Martin internally to MOD and I fell in love with it.

What have you learned so far?

My bread and butter is Power-BI and Excel, I love these tools and have used them in various roles throughout MOD. I had some experience with Power Platform but had no Machine Learning experience. I’m nearly at the EPA stage of the Apprenticeship, I am now a fully-fledged Power Platform Developer, implementing tools inside and outside of work utilising Dataverse.

Any interesting projects you’ve worked on at the MOD as a result

Prior to me starting the apprenticeship, I was asked to improve the data quality and management of our portfolio. Using this as a baseline I identified 3 areas that needed to be focused on, Project/ Programme Management; Artefact Management & Approval and Financial Analysis. After creating a prototype dashboard with the data I was given, I was able to provide a concept, and Power-BI Data Model that are being used to migrate our Portfolio Data to Dataverse to improve Data Security, Management, Quality and Accessibility. Now I am working towards a tool that can be deployed across the Enterprise into the Front-Line Commands to support their Portfolio Management.

How has it impacted your day-to-day role?

Massively, my tutor Scott Owens knows all too well how this is has impacted me, I can’t stop saying how much I see the benefit of this Apprenticeship and Hacks. I am more confident in my abilities and the work I am doing is impacting people and I am already seeing this. I can produce work faster, smarter and more automated.

Which ProjectHack did you attend?

I have attended 2 Hacks so far, #ProjectHack24 and #ProjectHack25. I am proud to say that I was part of the 1st Place winning Team from #ProjectHack24. Both hacks I have dabbled in the Assurance Challenges. I normally aim for the MOD sponsored ones, as I find these to be more challenging and I know that any of the solutions would benefit MOD, and I would like to be a part of a team that could change the way that they approach data challenges.

What did you enjoy most about the event?

Even though we placed 1st at #ProjectHack24 I found that I enjoyed #ProjectHack25 more. Working closely with another apprentice Laura whom I teamed up with last year. We joined forces again and made a really good solution that has caught the eye of a few Senior Leaders in MOD. I was lucky enough to be a guide for the VIPs during the 2 days and was chosen to be part of a discussion with Senior Leaders from the MOD to showcase the Apprenticeship so they can see the ROI.

Did you learn something new or come away with a memorable moment?

I have learnt not to be afraid to get stuck in, even if you are unsure of the challenge. It is a great opportunity for you to go into the challenge with the view I want to write an LLM, the opportunity is there. Don’t forget to READ THE HACK PACKS BEFORE THE DAY! This will help with choosing a challenge to tackle. A memorable moment, is winning a #ProjectHack on my first go round.

What are you most looking forward to during the rest of your apprenticeship?

Completing my project work, I love data and developing solutions to tackle these problems, and I can’t wait to show off what I have achieved and how this is improving my departments outputs. And attending #ProjectHack26 in October!

Any future career goals or areas you want to explore?

Since I started this journey, I have known that I want to continue higher education, getting a BSC / MSC equivalents (hopefully these can be funded), by partaking in the Level 7.

Would you recommend the apprenticeship (and ProjectHack) to others?

100% yes! There are teething problems, but you will find that with any activity undertaken but, use this to your advantage and become a voice for change and see the bigger picture, see the ROI and become the best version of yourself!

Any advice for someone just starting their journey?

Don’t be afraid to reach out for help, use the Hacks to help you whilst tackling those real-world problems. For those MOD Apprentice’s drop me a line if you want to troubleshoot anything or just chat my door is open.

“Take the blue pill… report what happened. Take the red pill… prevent what’s about to.”

We’ve built a theatre one that looks like control, but isn’t.

Plans are updated. Dashboards are polished. Metrics are tracked. But like a stage set, it’s all surface. Behind it? Complexity. Ambiguity. Drift. And, far too often, silence.

We’ve mistaken measurement for management.

That’s the real disease in project controls. As Greg Lawton put it so well:

“It’s like installing CCTV across a jobsite… then never checking the footage. Just hoping the rebar thefts would fix themselves.”

He asked a project team one simple question:

“Which metric changed a decision in the last 4 weeks?”

Silence.

Because we’ve built mirrors rather than steering wheels. Dashboards that impress, but don’t inform. Metrics that describe, but don’t direct.

This isn’t failure. It’s misalignment. We’ve been asking professionals to manage complex adaptive systems with tools built for predictable, ordered ones. Systems that shift as people interact, as work adapts, as constraints emerge and yet we try to tame them with stage gates and fixed baselines.

It’s time to escape the theatre.

From static metrics to dynamic sensemaking

Our traditional approach to control is rooted in command-and-control thinking; an era where predictability was assumed, and deviation was the enemy. But in today’s world, deviation is constant. It’s how the system learns. It’s a signal, not a failure.

What we need isn’t tighter control. It’s deeper understanding.

That means:

• Moving from assurance theatre to emergent insight
• Replacing heat maps with signal detection
• Swapping lagging indicators for leading, predictive intelligence
• From heatmaps to hypothesis-driven insight
• From dashboards to decisions

We need to recognise that projects are complex systems, not complicated machines. They cannot be controlled through force of will but they can be understood through data. They can be influenced. Navigated. Nudged.

And that begins with metrics that actually matter.

Problem → Solution → Data: Rewiring the system

Too often, metrics are created in a vacuum. We measure what’s easy to report, not what helps us act. But meaningful metrics don’t start with what data we have. They start with what problem we’re trying to solve.

That’s the chain:

Problem → Solution → Data.

Want to forecast variance? You need leading indicators that correlate with outcomes not lagging reports that describe what’s already happened. Want to automate assurance? You need structured, accessible data that connects performance with intent. Want to understand systemic blockers? You need to listen for weak signals, not just wait for escalation.

And this is where most organisations get stuck. They invest in solutions without the data to power them. Or they collect data without understanding how it feeds actionable insight.

That’s where Project Brain comes in

Project Brain: Intelligence, not just information

Project Brain is more than a product. It’s a new way of thinking. A knowledge layer that links the problems we face to the solutions we build and the data needed to make them work.

It maps decision-making logic. It helps teams identify what they need to measure and why. It enables AI to spot patterns before people can. And it empowers project professionals to navigate complexity not just react to it.

Instead of chasing change, they anticipate it. Instead of managing the illusion of order, they manage reality.

This isn’t science fiction. It’s already happening through apprenticeships, hackathons, and real-world deployments across the Project Data Analytics Coalition. The flywheel is turning. The system is rewiring.

Control, reimagined

Let’s give project controls the credit it deserves.

This profession has held projects together through chaos. Stitching plans. Tracking shifts. Absorbing pressure. But the game is changing. Fast.

AI isn’t here to take your role. It is here to amplify it.

It doesn’t need a pristine Gantt chart to know something’s off. It sees variance across risk registers, change logs, logistics plans, even email trails. It models uncertainty. Detects drift. Surfaces signals. And it cuts through the theatre.

So here’s the choice:

Blue pill: Stick with the rituals. The reports. The backward-looking commentary. Call it control.

Red pill: Reframe the whole concept. Step into a world where control means clarity. Where metrics provoke action. Where insight flows in real time, and your role shifts from documenting the past to shaping what happens next.

But here’s the truth. Most professionals were never trained for this future.

How many of us were taught statistics in a way we actually use? How many understand how to apply data analysis in emergent systems, where outcomes evolve, adapt, and defy linear planning?

This is where the Project Data Academy comes in.

Not just to train. To co-create.

Every assignment isn’t just about passing a module. It feeds into the future. It helps to shape the frameworks, insights and tools that define what comes next. You are not just learning. You are part of building what’s next.

In The Matrix, Neo didn’t win by following the rules. He rewired the system from within. That’s your opportunity.

The Project Data Academy and Project Brain are your red pills.

Take it. Upskill. Contribute. And help lead the profession into its next chapter. One where we don’t just survive complexity. We master it.

Lastly, project professionals don’t have a right to these roles in the future. They will be competing with analysts and data scientists. But with a mix of domain experience and knowledge of data, stats and AI, they’ll be unbeatable.

In our recent Introduction to Data Visualisation workshop, led by Senior Tutor Scott Owens, attendees were introduced to the power of business intelligence and how tools like Power BI can transform raw data into clear, compelling insights that drive smarter decision-making.

Whether you’re just getting started with data or looking to sharpen your visualisation skills, this session was packed with actionable advice, real-world examples, and a live dashboard demonstration that brought data to life.

Why Data Visualisation Matters

Scott kicked off the session by reminding us that data doesn’t speak for itself—it needs a storyteller. Using visualisation tools like Power BI, businesses can uncover trends, track performance, and understand the narrative within their numbers:

What happened? Where are we now? How did we get here and where are we going?

With experience spanning industries from painting to pension funds, Scott brought a fresh perspective to the role of business intelligence in everyday decision-making. His key message? Data is only as powerful as your ability to interpret and communicate it.

Power BI: Your Visualisation Superpower

Scott introduced Power BI as a user-friendly, powerful tool, often freely available via Microsoft Office 365, that enables users to create interactive dashboards. These dashboards allow teams to collaborate, track key performance indicators, and identify trends with just a few clicks.

He shared some simple but effective rules for building dashboards:

• Keep it clean: Avoid clutter and focus on what really matters.
• Tell a story: Use the right visuals to guide your audience through the data.
• Know your audience: Design with the end user in mind—clarity over complexity.

From tree maps to slicers and line charts, Scott demonstrated how to build a Power BI dashboard step-by-step using a real financial dataset. He covered:

• Importing and transforming data using Power Query
• Creating intuitive filters (slicers)
• Designing visuals that highlight gross sales, profit, and regional performance
• Incorporating maps to visualise geographic trends

The result? A dashboard that not only looks great, but actually helps organisations act on what the data is telling them.

Advanced Features & What's Next

The session wrapped up with a peek into Power BI’s more advanced capabilities—like predictive analytics, time series, and bookmarks—along with encouragement to dive deeper and continue exploring.

For those looking to take the next step, Scott introduced the Level 4 Data Analyst Apprenticeship, a hands-on course designed to equip learners with core data analysis and visualisation skills. From regular tutor support to real-world projects and hackathons, the course offers a structured yet flexible way to build data confidence.

Bonus: attendees also received a 50% discount code for our upcoming October Hackathon, a fun and competitive opportunity to test your new skills, meet other learners, and even win prizes.

Interested in joining the next session or learning more about the course?

Whether you’re new to data or looking to upskill your team, our Level 4 Data Analyst course can help you turn curiosity into capability.

Enquire now or join our next free workshop to start your journey into data storytelling.

By Sandeep, Level 7 AI Data Specialist Apprentice at the Science and Technology Facilities Council

As part of our Level 7 AI Data Specialist apprenticeship, we're proud to support professionals like Sandeep who are using artificial intelligence to bring meaningful change to complex project environments. In this blog, Sandeep shares why he joined the course, how he’s applying his learning, and what he hopes to achieve by blending technical AI knowledge with real-world project delivery in the science and technology sector.

Why I chose the Level 7 AI Data Specialist course

I chose the Level 7 AI Data Specialist apprenticeship because I saw a clear opportunity to bring real value to the way we plan and deliver research and experimental projects in the physics and space domain. My role involves supporting Project Managers and Work Package Managers with scheduling, monitoring, and controlling tasks and it’s clear there’s significant scope to improve how we handle data, automate repetitive processes, and make better-informed decisions.

There’s huge potential for AI and data-driven tools to support early risk identification, resource forecasting, and reducing the admin burden on project teams. I wanted to develop the skills to build and apply these kinds of tools in practice and this course felt like the right way to do that.

What I’ve learnt so far and how I’m applying it to my role

The course began with the fundamentals of AI and machine learning, which gave me a solid grounding in the core concepts and techniques. More recently, we’ve explored deep neural networks which I’ve found particularly interesting. It’s made me consider how we might use these models to identify patterns in historical project data, or even predict issues like schedule overruns before they occur.

Even at this early stage, I’ve started applying some of the learning. For example, identifying manual processes that could be automated, or using structured data more effectively to spot trends and anomalies. It’s also changed how I think about data not just as something we collect, but as something we can actively use to improve project delivery.

What’s next

Looking ahead, I’m keen to start developing small AI-based tools that we can test within our team. These might include predictive models for scheduling risks or using natural language processing to extract insights from technical reports and risk logs. I also see real potential in building dashboards or digital assistants to support stakeholders with day-to-day monitoring and reporting.

Ultimately, my goal is to help create a more intelligent, data-aware project environment where decisions are evidence-based, and where we’re making the most of the data we already have. As I progress through the course, I hope to bridge the gap between technical AI knowledge and practical project delivery in a way that genuinely benefits the organisation.

Want to start your own data journey?