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Hackathons are exhilarating bursts of creativity and collaboration, offering a playground for problem solvers, tech enthusiasts, and innovators. At Projecting Success, we believe in the power of these events to drive innovation and personal growth. Here are some compelling reasons why you should consider participating in our hackathons:

Skill Enhancement

Hackathons propel participants beyond their comfort zones. Whether you’re a seasoned coder or a curious beginner, you’ll acquire new skills. Projecting Success provides an environment where you can fine-tune your coding, design, and teamwork abilities. From rapid prototyping to debugging, every challenge contributes to your growth.

networking and skill enhancement at our hackathon

Networking Opportunities

Our hackathons attract a diverse crowd, like-minded individuals with whom you can collaborate, exchange ideas, and forge lasting connections. Who knows? Your next collaborator or mentor might be sitting across from you during a late-night coding sprint. We employed our very own Ran after meeting him on a hack!

Unleashing Creativity

Hackathons thrive on unconventional thinking. In our working lives, we can’t always explore using novel techniques against the status quo, but in a hackathon you’ll discover innovative solutions to complex problems. Whether it’s a novel app feature or an elegant algorithm, you’ll surprise yourself with what you create.

Real-World Problem Solving

Our hackathons often tackle real-world challenges. From sustainability to healthcare, you’ll grapple with issues that matter. The skills you acquire can be directly applied to your career or personal projects.

One of our successful hackathons

Prizes and Recognition

Who doesn’t love a prize? Even if you don’t clinch the top spot, the experience itself is invaluable.

Project Exposure

Your hackathon project won’t gather dust. Projecting Success showcases winning projects, and industry experts take notice of our Solutions Centre. Imagine presenting your solution to potential employers or investors and being able to explain its real-world effects. It’s a chance to shine and demonstrate your capabilities.

So grab your laptop, fuel up on caffeine, and dive into the world of innovation. Who knows? Your next breakthrough might be just a hackathon away!

Join us for our upcoming hackathon events:

Project:Hack23 Kick Off and Info Session: Join us in person to network and find out more about our in-person hackathons

Project:Hack 23 at Rolls Royce Derby: Experience the energy and excitement of hacking in person.

Artificial Intelligence (AI) and Data Science are revolutionising industries across the globe. With advancements in automation, predictive analytics, and machine learning, businesses are using AI to transform decision-making and optimise operations. But what are the key AI and Data Science trends driving innovation, and how can professionals stay ahead in this rapidly evolving field?

In this blog post, we’ll explore the top AI and Data Science trends to watch in 2024 and highlight how you can upskill through our Level 7 AI Specialist Apprenticeship, designed to prepare you for the future of work.

1. AI for Business Decision Making and Automation

AI is transforming how companies make decisions, helping businesses analyze massive amounts of data quickly and accurately. In 2024, expect to see more organisations adopting AI-powered automation to streamline processes, reduce costs, and improve productivity. AI is especially valuable in industries like finance, healthcare, and retail, where data-driven decision-making is essential for growth.

To stay competitive, professionals need to be proficient in both AI technologies and Data Science techniques to help businesses leverage AI’s full potential. Upskilling in AI ensures you’re equipped with the tools and knowledge to lead data-driven initiatives.

2. Ethical AI and Bias Mitigation

As artificial intelligence continues to shape industries, ethical concerns around AI bias and fairness are becoming more prominent. AI algorithms, if not carefully designed, can perpetuate existing biases or create new ethical issues. In response, businesses are prioritiaing ethical AI development and ensuring models are transparent and explainable.

Organisations are also adhering to new AI regulations focused on fairness and transparency. Professionals who understand how to mitigate AI bias and ensure compliance with ethical standards will be in high demand.

3. AI-Driven Automation and the Future of Work

AI automation is expected to impact almost every sector, from logistics and manufacturing to marketing and customer service. While some fear that automation will replace jobs, it’s more likely that AI will create new roles and redefine existing ones. In 2024, AI will enhance workplace productivity by handling repetitive tasks and freeing up human workers for more strategic, creative tasks.

To thrive in this evolving landscape, professionals must embrace automation and develop expertise in AI deployment. Learning how to work alongside AI-driven systems will be a crucial skill in the workforce of the future.

4. Edge Computing and AI in IoT

The growth of edge computing is another trend to watch in AI and Data Science. Edge AI brings computing and data analysis closer to the source, rather than relying on cloud-based solutions. This reduces latency and enables real-time data processing, which is critical for IoT devices, smart cities, autonomous vehicles, and healthcare applications.

As the number of connected devices continues to rise, businesses will need AI professionals who can develop and deploy edge computing solutions to handle real-time data efficiently.

5. AI for Sustainability and Climate Action

AI’s potential to address global challenges like climate change is enormous. AI is being used to optimize energy use, reduce waste, and drive sustainable business practices. AI-powered sustainability initiatives are helping industries cut carbon emissions, improve resource management, and combat climate change.

In 2024, more businesses will turn to AI to help meet their sustainability goals. Professionals who specialize in AI for sustainability will be in high demand as companies seek to balance profitability with environmental responsibility.

Upskilling in AI: Why Our Level 7 AI Specialist Apprenticeship is Critical for Your Career

With AI and Data Science evolving rapidly, staying ahead of the curve requires continuous learning and upskilling. Our Level 7 AI Specialist Apprenticeship offers the perfect opportunity to gain cutting-edge skills in AI and Data Science while working on real-world projects. This program covers everything from AI ethics to machine learning algorithms, equipping you with the expertise needed to thrive in a world increasingly driven by data and AI.

Whether you’re a professional looking to enhance your AI skills or someone wanting to transition into the field, our Level 7 apprenticeship offers a comprehensive curriculum that combines theory with practical applications.

Conclusion

The future of AI and Data Science holds immense promise, with trends like automation, ethical AI, and sustainability at the forefront of innovation. By keeping up with these emerging trends and investing in upskilling, you can ensure your career remains future-proof in this rapidly changing landscape.

To learn more about how our Level 7 AI Specialist Apprenticeship can help you stay ahead in the AI revolution, get in touch with us today.

In today’s fast-paced project management landscape, the need for clear, actionable data is more critical than ever. Project delivery dashboards are a key tool in empowering decision-makers, but are your dashboards truly driving better outcomes, or are they overwhelming your users with too much information? In this blog post, we delve into how to transform your project delivery dashboards based on insights shared by Gerard Duggan, a data analytics expert from KBR, in his recent webinar, Breaking Bad: How Project Delivery Dashboards Need to Change. By following best practices in dashboard design and data visualisation, your organisation can avoid common pitfalls and create dashboards that deliver real value.

Why Effective Dashboards Are Critical for Project Delivery

For project managers, data is everywhere. Whether it’s construction, engineering, or another complex field, stakeholders at every level of an organisation need access to the right information to make informed decisions. Yet, many dashboards fail to provide the context necessary for quick, efficient decision-making.

A typical project delivery dashboard might show progress reports, cost breakdowns, or personnel statistics, but as Gerard Duggan points out, numbers alone aren’t enough. Without the right context, how can you know if being 40% complete is good or bad? Is a project ahead of schedule or dangerously behind? Without adding insights such as planned versus actual progress, or highlighting important deviations, these numbers are meaningless.

Key Challenges in Project Delivery Dashboards

Here are some common issues that plague project dashboards:

  1. Information Overload: Many dashboards throw too much information at the user, making it difficult to know what’s important. Gerard recalls his early days in dashboard design, where he would cram too many metrics and visuals onto one screen, confusing rather than enlightening the user.
  2. Lack of Focus: Gerard explains that dashboards must stay focused on key objectives. What do decision-makers really need to know to act? If you can’t answer that question, the dashboard will likely fail.
  3. Poor Visualisation Choices: Choosing the wrong type of visualisation can overwhelm users. Gerard advises using simple, familiar visuals like bar charts or line graphs. Complex visuals such as chord diagrams might look impressive but are often not user-friendly.
  4. Failure to Communicate the Story Behind the Data: A great dashboard tells a story. It provides a logical flow of information, starting with high-level insights and drilling down into more detailed data when needed. Gerard highlights the importance of structuring dashboards so users know where to start and what action to take next.

How to Build Effective Dashboards for Project Delivery

If your dashboards are suffering from any of the challenges above, here are the steps you can take to improve them:

1. Understand Stakeholder Needs

Before you begin designing your dashboard, you need to understand your end users. What information is most critical to them? As Gerard emphasises, stakeholder engagement is key to ensuring the dashboard delivers value. This is where your soft skills come in – engage with project managers, C-suite executives, and even operational staff to understand their specific requirements.

2. Use Context to Give Meaning to Data

Numbers by themselves can be meaningless without context. A progress bar showing 40% completion means nothing unless you know whether that’s ahead of or behind schedule. Including planned versus actual progress, benchmarks, and performance against key metrics can help users understand the story the data is telling.

3. Simplify Your Design

Avoid the temptation to use flashy, complex visualisations. Gerard’s advice is simple: keep it clean. Stick to visuals that your users can understand at a glance. A simple line chart showing progress over time or a bar chart comparing planned vs. actual performance is often more effective than a multi-coloured radial chart.

4. Prototype Before You Build

Rather than spending weeks building a dashboard that might not meet your users' needs, sketch it out first. Use paper, or a quick design tool, and show it to stakeholders before committing time and resources. Gerard advocates this approach as it allows you to iterate quickly and ensure that you’re on the right track before you invest heavily in development.

What Makes a Good Project Delivery Dashboard?

The best dashboards share a few common traits:

The Power of Storytelling in Dashboards

Data storytelling is more than just a buzzword; it’s a critical aspect of effective dashboard design. Gerard highlights that dashboards should guide users through the data in a logical way, starting from high-level insights and allowing them to drill down into more detailed data as needed. By using visual hierarchy, colour theory, and cognitive science principles, you can lead the user through the data journey and help them make sense of complex information without being overwhelmed.

One important aspect of storytelling is colour usage. Gerard points out that colour should have a clear purpose in your dashboard. For example, use colour sparingly to highlight key points or areas that require immediate attention, rather than applying a rainbow of colours that competes for the user's focus.

Breaking Bad: How Project Delivery Dashboards Need to Change

In today’s fast-paced project management landscape, the need for clear, actionable data is more critical than ever. Project delivery dashboards are a key tool in empowering decision-makers, but are your dashboards truly driving better outcomes, or are they overwhelming your users with too much information? In this blog post, we delve into how to transform your project delivery dashboards based on insights shared by Gerard Duggan, a data analytics expert from KBR, in his recent webinar, Breaking Bad: How Project Delivery Dashboards Need to Change. By following best practices in dashboard design and data visualisation, your organisation can avoid common pitfalls and create dashboards that deliver real value.

Why Effective Dashboards Are Critical for Project Delivery

For project managers, data is everywhere. Whether it’s construction, engineering, or another complex field, stakeholders at every level of an organisation need access to the right information to make informed decisions. Yet, many dashboards fail to provide the context necessary for quick, efficient decision-making.

A typical project delivery dashboard might show progress reports, cost breakdowns, or personnel statistics, but as Gerard Duggan points out, numbers alone aren’t enough. Without the right context, how can you know if being 40% complete is good or bad? Is a project ahead of schedule or dangerously behind? Without adding insights such as planned versus actual progress, or highlighting important deviations, these numbers are meaningless.

Key Challenges in Project Delivery Dashboard

Here are some common issues that plague project dashboards:

  1. Information Overload: Many dashboards throw too much information at the user, making it difficult to know what’s important. Gerard recalls his early days in dashboard design, where he would cram too many metrics and visuals onto one screen, confusing rather than enlightening the user.
  2. Lack of Focus: Gerard explains that dashboards must stay focused on key objectives. What do decision-makers really need to know to act? If you can’t answer that question, the dashboard will likely fail.
  3. Poor Visualisation Choices: Choosing the wrong type of visualisation can overwhelm users. Gerard advises using simple, familiar visuals like bar charts or line graphs. Complex visuals such as chord diagrams might look impressive but are often not user-friendly.
  4. Failure to Communicate the Story Behind the Data: A great dashboard tells a story. It provides a logical flow of information, starting with high-level insights and drilling down into more detailed data when needed. Gerard highlights the importance of structuring dashboards so users know where to start and what action to take next.

How to Build Effective Dashboards for Project Delivery

If your dashboards are suffering from any of the challenges above, here are the steps you can take to improve them:

1. Understand Stakeholder Needs

Before you begin designing your dashboard, you need to understand your end users. What information is most critical to them? As Gerard emphasises, stakeholder engagement is key to ensuring the dashboard delivers value. This is where your soft skills come in – engage with project managers, C-suite executives, and even operational staff to understand their specific requirements.

2. Use Context to Give Meaning to Data

Numbers by themselves can be meaningless without context. A progress bar showing 40% completion means nothing unless you know whether that’s ahead of or behind schedule. Including planned versus actual progress, benchmarks, and performance against key metrics can help users understand the story the data is telling.

3. Simplify Your Design

Avoid the temptation to use flashy, complex visualisations. Gerard’s advice is simple: keep it clean. Stick to visuals that your users can understand at a glance. A simple line chart showing progress over time or a bar chart comparing planned vs. actual performance is often more effective than a multi-coloured radial chart.

4. Prototype Before You Build

Rather than spending weeks building a dashboard that might not meet your users' needs, sketch it out first. Use paper, or a quick design tool, and show it to stakeholders before committing time and resources. Gerard advocates this approach as it allows you to iterate quickly and ensure that you’re on the right track before you invest heavily in development.

What Makes a Good Project Delivery Dashboard?

The best dashboards share a few common traits:

The Power of Storytelling in Dashboards

Data storytelling is more than just a buzzword; it’s a critical aspect of effective dashboard design. Gerard highlights that dashboards should guide users through the data in a logical way, starting from high-level insights and allowing them to drill down into more detailed data as needed. By using visual hierarchy, colour theory, and cognitive science principles, you can lead the user through the data journey and help them make sense of complex information without being overwhelmed.

One important aspect of storytelling is colour usage. Gerard points out that colour should have a clear purpose in your dashboard. For example, use colour sparingly to highlight key points or areas that require immediate attention, rather than applying a rainbow of colours that competes for the user's focus.

Conclusion

Effective project delivery dashboards can make or break a project. By focusing on simplicity, stakeholder engagement, and actionable insights, you can create dashboards that not only look good but actually help your team make better decisions. As Gerard Duggan’s webinar illustrates, the key to success lies in understanding what your users need and presenting the data in a clear, focused way.

Want to learn more? Watch the full webinar Breaking Bad: How Project Delivery Dashboards Need to Change or sign up for our upcoming hackathons to get hands-on experience in dashboard design.

Today’s webinar, The Future of Risk Management, delivered valuable insights into the evolving landscape of risk management and project delivery. Led by Martin Paver (CEO, Projecting Success) and David Turnbull (Enterprise Project Controls Manager, EDF) the session focused on how risk professionals can stay ahead of the curve by embracing innovative approaches and leveraging cutting-edge tools and skills.

Embracing the Future: A No-Brainer for Risk Professionals

Martin kicked off the session by emphasizing the critical importance of future-proofing in the risk management profession. "If I was in this profession, and someone offered to future-proof me, I'd bite their hand off," he remarked, underscoring the urgency for risk professionals to boost their CVs and become pioneers in the field. The message was clear: the future is here, and it’s an opportunity for professionals to lead rather than follow.

Reimagining Project Delivery at EDF: Mindset, Skillset, and Toolset

David Turnbull from EDF shared how his organization is reimagining project delivery through a holistic approach focused on mindset, skillset, and toolset. According to Turnbull, 2023 was the "year of the toolset," where EDF concentrated on building the infrastructure necessary to harness data effectively. This included the creation of data lakes, real-time information access, and ensuring the infrastructure was in place to utilize data in innovative ways.

Looking ahead to 2024, Turnbull described it as the "year of the skillset," where EDF is focused on upskilling both seasoned professionals and those already proficient in data analytics. By combining a robust infrastructure with enhanced skills, Turnbull believes the organization can change the mindset of its employees, ultimately transforming how projects are delivered. He highlighted the importance of using data to optimize processes, drawing an analogy to using real-time traffic data to avoid congestion and choose better routes.

The Importance of Changing Mindsets

David also spoke passionately about the need for a mindset shift across the UK’s project delivery landscape. He noted that the country has struggled with delivering major construction projects on time and within scope, with many projects stalling or failing to meet their original objectives. For Turnbull, the answer lies in reimagining how projects are delivered, starting with a more strategic approach to risk management.

Upskilling and Surfing the AI Wave

Martin provided further details on the future of risk management, particularly the role of apprenticeships in building critical skills. He discussed a 15-month apprenticeship program that combines online learning, hackathons, and off-the-job hours to help professionals build a strong portfolio. Martin also emphasised the importance of upskilling to stay relevant in an increasingly AI-driven world, noting that those who embrace these changes are likely to see significant career advancements and increased earning potential.

Paver’s advice to risk professionals was clear: don’t wait. He encouraged participants to actively engage with new technologies, learn continuously, and become pathfinders in their field. "It’s a perfect time to surf the AI wave rather than get crushed by it," Martin stated, encapsulating the essence of the webinar.

Conclusion: A Call to Action

As the session drew to a close, Martin and David reiterated the importance of embracing change and leading the charge in reimagining project delivery. They invited participants to join the conversation, get involved in the coalition, and explore the training opportunities available to become part of the future of risk management.

The webinar concluded with a call to action for risk professionals to take proactive steps towards upskilling, adopting new technologies, and transforming how projects are managed in the UK. The message was clear: the future of risk management is not just about surviving change—it’s about thriving in it.

To find out more about our risk management course, contact us now at enquiries@projectingsuccess.co.uk

We're also excited to introduce Project:Womxn, a women-led initiative focused on increasing women's representation and involvement in the tech industry. As part of this initiative, we'll hear from Zoe Bello, a data tutor at Projecting Success, and Lavinia Descaluțu from PMI UK. They'll share their experiences, challenges, and successes in leading projects and pushing for greater inclusion in tech.

Whether you've been managing projects for years or you're just starting, "Project Pioneers: Embracing Data to Lead and Succeed" is your chance to hear inspiring stories, pick up practical tips, and meet others who are passionate about advancing in the world of project management. Don't miss out on the chance to learn from the best and connect with people who share your vision for a more inclusive future!

In the world of project management, risk management is a critical function that often involves time-consuming and repetitive tasks. However, with the advent of advanced technologies like machine learning and automated workflows, it's possible to streamline these processes, allowing project managers to focus on more strategic activities. Below are several ways to automate the mundane aspects of project risk management, transforming it from a tedious chore into a more efficient and insightful practice.

1. Suggesting Risks Using Risk Libraries and Machine Learning

 One of the first steps in risk management is identifying potential risks. Traditionally, this has relied on the experience and intuition of project managers and their teams. However, by integrating risk libraries—repositories of common risks associated with similar projects—into your project management software, you can automate the suggestion of potential risks. Machine learning models can take this a step further by analysing past projects and suggesting potential sources of variance that may not be immediately obvious. These models can scan through historical data, identifying patterns and anomalies that could indicate emerging risks, providing a proactive approach to risk management.

2. Automating Risk Updates with Workflows

Another mundane but essential task in risk management is ensuring that risks and their associated management actions are regularly updated. This often involves chasing down team members for updates, a task that can be automated using workflows. Automated workflows can be set up to send reminders and requests for updates at specified intervals, ensuring that risk information remains current without the need for constant human intervention. This not only saves time but also improves the accuracy and timeliness of the risk data.

3. Developing Automated Dashboards on Risk Status

Keeping stakeholders informed about the status of project risks is crucial, but manually compiling and updating reports can be laborious. By automating the development of dashboards, you can provide real-time insights into the status of risks, including which risks are most pressing, what mitigation actions are being taken, and where the project stands in relation to its risk tolerance thresholds. These dashboards can be tailored to different audiences, ensuring that everyone from the project team to executive stakeholders has the information they need, when they need it.

4. Analysing Who is Effective at Risk Management

One of the more advanced applications of automation in risk management is using data analytics to assess who within the team is particularly effective at managing risks. By analysing historical data, you can identify patterns that show which individuals or teams are most successful at identifying risks early and mitigating them effectively. This can inform future team compositions, training needs, and even recognition programmes, helping to build a culture of proactive risk management.

5. Monitoring Engagement and Effectiveness in Risk Processes

Finally, automating the analysis of engagement in the risk management process can provide valuable insights into how effectively your team is managing risks. Dashboards can be developed to track who is engaging with the risk management process, how often updates are made, and the outcomes of those updates. This data can be used to refine processes, identify areas where additional support or training may be needed, and ensure that the risk management process is as effective as

Pushing the Boundaries of Automation in Risk Management

These examples represent just the basics of what can be achieved by automating parts of the risk management process. As technology continues to advance, the potential for further automation—and the insights it can provide—only grows. Imagine a system that not only suggests risks but also predicts them with high accuracy, develops mitigation strategies, and automatically adjusts project plans to account for emerging risks in real-time. The future of risk management is one where data-driven insights take centre stage, enabling more informed, proactive decision-making and ultimately leading to more successful project outcomes.

By embracing these automated solutions, organisations can significantly refine their current approaches to risk management, making the process not only more efficient but also more effective. The future of risk management lies in leveraging technology to handle the mundane, allowing human expertise to focus on strategic decision-making and innovation.

In today’s dynamic business environment, effective risk management is crucial. Leveraging advanced data analytics and AI can significantly improve your ability to identify, assess, and mitigate risks. Here are five practical tips to elevate your risk management strategy:

1. Automate Workflows for Risk Status and Management Actions

Implement AI-driven workflows to streamline the updating of risk statuses and management actions. This automated process ensures that risk information is current and that management actions are tracked and adjusted as needed. Furthermore, use these workflows to create dynamic dashboards that not only provide a real-time view of risks but also highlight who within your organisation is proactively engaging with these risks. This visibility encourages accountability and ensures that risks are being actively managed by the right people.

2. Leverage Libraries of Common Risks with AI Assistance

Establish and maintain libraries of known or commonly occurring risks, drawing on historical data, industry benchmarks, and relevant reports like NAO (National Audit Office) findings. Enhance these libraries by creating a Large Language Model (LLM) to assist users in identifying potential areas of risk. This model can prompt risk managers on what to consider, offering tailored suggestions based on the specific context of their projects or operations. Where possible, train the LLM on lessons learned reports, NAO reports, or utilise a community-driven model like Marvin. This AI-powered guidance ensures a more comprehensive risk identification process.

3. Maintain a Repository of Mitigation Methods

Develop a repository of effective mitigation strategies tailored to specific risks. By cataloguing these methods, your organisation can quickly deploy proven responses to new threats. Advanced analytics can be used to continuously evaluate the effectiveness of these mitigation strategies, allowing for refinement and adaptation over time. This repository becomes a critical tool in ensuring that risk responses are both timely and effective, reducing the likelihood of repeat issues.

4. Utilise Predictive Analytics for Risk Forecasting

Leverage predictive analytics to anticipate potential risks before they occur. By analysing historical data, predictive models can identify trends and forecast threats, providing valuable foresight. This allows your organisation to take pre-emptive actions, reducing the likelihood and impact of unexpected disruptions. Predictive analytics ensures that your risk management is forward-looking, rather than just reactive.

5. Implement Real-Time Monitoring

Adopt AI-powered tools for real-time risk monitoring across all operations. These tools can provide instant alerts to emerging risks, allowing for immediate responses. Additionally, real-time monitoring should extend to tracking regulatory changes, cybersecurity threats, and operational anomalies. By staying informed in real time, your organisation can mitigate risks before they escalate, ensuring continuous protection against potential threats.

Conclusion

Integrating advanced data analytics and AI into your risk management processes can dramatically improve your organisation's ability to identify, assess, and mitigate risks. By automating workflows, leveraging AI-driven libraries and models, and utilising predictive and real-time monitoring, you can ensure that risks are managed proactively and effectively. Embrace these technologies to safeguard your organisation against the complex and evolving risks of today’s business landscape.

I’m Christoph Kuhle, Operations Director at Projecting Success. I’m thrilled to share the key highlights from yesterday’s dynamic webinar, which I had the pleasure of leading. The session featured valuable insights from Dave Turnbull of EDF and Gareth Parkes from Sir Robert McAlpine. Together, we explored the complexities of project data analytics and discussed the collaborative efforts propelling the PDA community forward.

A graph showing webinar attendees organisations current use of data analytics and AI in project delivery

Key Takeaway's from the webinar

1. Overcoming Challenges in Data Analytics

I emphasised the importance of overcoming challenges in data analytics with actionable strategies. Dave Turnbull shared a real-time example from EDF. The transition from monthly to weekly data updates in Primavera, visualized through Power BI, highlighted how presenting data to senior leaders can catalyse change. For instance, a milestone dashboard unveiled discrepancies in milestone baselines, prompting a shift in behaviour and better data integrity.

Gareth Parkes focused on the significance of bridging the gap between IT and business functions. By creating a Data Governance Board, Sir Robert McAlpine is aligning priorities and fostering collaboration between data owners and system managers, ensuring a holistic approach to data management.

2. Prioritising and Implementing Solutions

Both speakers discussed methods for prioritizing and implementing data solutions in large organisations:

3. Moving from Descriptive to Predictive and Prescriptive Analytics

Vivian’s question about advancing from descriptive to prescriptive analytics sparked a discussion on industry readiness. Gareth acknowledged that while the construction industry is not fully there yet, efforts are focused on improving real-time reporting and predictive insights.

Dave highlighted the role of predictive analytics in decision-making, using historical data to forecast trends and enhance project delivery. His analogy of shopping behaviors across generations illustrated the shift from intuitive decision-making to data-driven approaches.

4. Educating Senior Leaders

Christoph asked about educating senior leaders, a crucial aspect for data-driven transformation. Dave noted the challenge of shifting mindsets, especially for senior leaders accustomed to traditional decision-making methods. By presenting data in a more predictive and visual format, EDF is gradually changing perceptions and improving decision-making.

Gareth emphasised the need for continuous education and the role of apprenticeships in building data literacy. The coalition’s focus on data-driven skills and collaboration across organizations is essential for advancing the industry.

5. The Coalition’s Impact and Future Steps

The discussion wrapped up with thoughts on the coalition’s role. Gareth highlighted the importance of data sharing and pooling, as outlined in the Belfast Paper and the manifesto for data-driven projects. He stressed the need for collective problem-solving and shared solutions.

Dave underscored the coalition’s value in fostering collaboration and leveraging collective knowledge. By solving problems together, organisations can avoid reinventing the wheel and drive more effective project delivery.

next steps

Thank you to Dave Turnbull and Gareth Parkes for their invaluable insights and to all attendees for their participation.

For further information on our apprenticeship courses, reach out to us at enquiries@projectingsuccess.co.uk

"Artificial Intelligence will have a more profound impact on Humanity than fire, electricity and the internet" Sundar Pichai, CEO Alphabet Inc. 

It feels almost like yesterday that our world became “data driven”. And no sooner have (some people) caught up with this, the world has changed again with the next BIG thing: AI.  

AI data specialists are at the forefront of this technological innovation- they're ahead of the curve and leaving most of us trailing in their wake.  

The AI data Specialist is playing a crucial role in transforming vast amounts of data into actionable insights that the rest of us can benefit from.  

With organisations across various industries recognising the value of data, the demand for AI data specialists is skyrocketing and demand is likely to outstrip supply for a long time to come.  So in this blog post I explore some of the career opportunities available for AI data specialists and what you can expect in this exciting and dynamic field. 

The Role of an AI Data Specialist 

AI data specialists are professionals who collect, manage, and analyse large datasets to support AI and machine learning (ML) initiatives. Their expertise lies in ensuring that data is accurate, clean, and ready for use in training AI models. Key responsibilities include: 

Career Pathways 

AI data specialists have a variety of career pathways to choose from, each with its unique opportunities and challenges. Some of the prominent roles include: 

1. Data Analyst 

Data analysts focus on interpreting data to help organizations make informed decisions. They use statistical techniques and software to analyse datasets, identify trends, and generate reports. 

2. Data Engineer 

Data engineers design and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are robust, scalable, and efficient. 

3. Machine Learning Engineer 

Machine learning engineers build and deploy machine learning models. They work closely with data scientists to turn raw data into actionable insights through model training and optimisation. 

4. Data Scientist 

Data scientists combine their expertise in statistics, programming, and domain knowledge to extract actionable insights from data. They develop predictive models and algorithms to solve complex business problems. 

5. AI Research Scientist 

AI research scientists work on advancing the field of AI through research and development. They explore new algorithms, methodologies, and applications of AI to push the boundaries of what is possible. 

Industries Hiring AI Data Specialists 

AI data specialists are in demand across a wide range of industries. Some of the sectors actively hiring include: 

Future Outlook 

The future for AI data specialists looks promising, with continued growth expected in the coming years. As AI technology advances and more organisations recognise the value of data-driven decision-making, the demand for skilled data specialists will only increase. 

Conclusion 

A career as an AI data specialist not only future-proofs your career but offers diverse opportunities and the chance to work on exciting and innovative projects that shape the future of the world in which we live in.  

With the right skills and a passion for data, you can be at the forefront of this amazing field and make a significant impact across various industries. Whether you're just starting or looking to advance your career, the path of an AI data specialist promises growth, challenges, and the satisfaction of contributing to groundbreaking advancements in AI. 

To find out how you could advance your career in AI, contact us about our Level 7 AI Data Specialist Apprenticeship at enquiries@projectingsuccess.co.uk

Artificial Intelligence (AI) and data science are transforming industries, creating new opportunities for innovation and efficiency. As these fields continue to evolve, the demand for skilled AI data specialists is growing rapidly. Whether you're looking to enhance your current skill set or pivot into a new career, the AI Data Specialist Course offers comprehensive training to equip you with the necessary tools and knowledge. Here are the top 10 skills you will learn in this course:

1. Data Analysis and Interpretation

In this course, you'll learn how to analyse and interpret complex data sets. You'll gain the ability to extract meaningful insights that can inform decision-making and drive business strategies. This skill is fundamental in understanding patterns, trends, and anomalies within data.

2. Machine Learning Algorithms

A significant portion of the course is dedicated to machine learning. You'll learn to implement and optimise various machine learning algorithms for predictive modeling and data analysis. This includes both supervised and unsupervised learning techniques, empowering you to handle a wide range of data problems.

3. Data Preprocessing Techniques

Before data can be analysed or used in machine learning models, it often needs to be preprocessed. This course will teach you essential techniques for cleaning, transforming, and preparing data. Effective preprocessing is crucial for improving the accuracy and performance of your models.

4. Programming Skills

Programming is at the heart of data science and AI. You'll develop strong programming skills in languages commonly used in these fields, such as Python and R. These languages are versatile and widely adopted, making them valuable tools in your AI and data science toolkit.

5. Statistical Methods

Understanding and applying statistical methods is key to analyzing data and validating models. The course covers a range of statistical techniques that are essential for hypothesis testing, regression analysis, and more. These methods help ensure that your conclusions are based on sound data analysis.

6. Data Visualisation

Communicating your findings effectively is just as important as the analysis itself. You'll learn to create compelling data visualizations that convey insights clearly and persuasively. Mastering tools like Matplotlib, Seaborn, and Tableau will enable you to present data in a way that is easily understood by stakeholders.

7. Effective Storytelling with Data

In addition to visualization techniques, you'll master the art of storytelling with data. This involves structuring your presentations to highlight key insights and narratives, making data more relatable and impactful. You'll learn to craft stories that resonate with your audience, using data to support your message and drive home the importance of your findings. This skill is essential for influencing decisions and driving strategic initiatives based on your analyses.

8. Deep Learning

Deep learning is a subset of machine learning that focuses on neural networks. You'll gain knowledge in deep learning techniques and learn to use frameworks like TensorFlow and PyTorch. This skill is particularly valuable for tasks such as image and speech recognition, natural language processing, and more.

9. AI Ethics and Governance

As AI becomes more integrated into society, understanding the ethical considerations and governance issues is crucial. The course covers topics such as data privacy, algorithmic bias, and ethical AI practices. You'll learn to navigate the complex landscape of AI ethics and ensure your work aligns with responsible standards.

10. Project Management

Successful AI and data projects require effective project management. You'll develop skills in planning, executing, and delivering AI projects. This includes understanding project lifecycles, managing resources, and ensuring that projects meet their objectives and deadlines.

Conclusion

The AI Data Specialist Course is designed to provide you with a robust and comprehensive education in AI and data science. By mastering these top 10 skills, you'll be well-prepared to tackle complex data challenges and contribute to the growing field of AI. Whether you're aiming to advance in your current role or transition to a new career, this course offers the expertise you need to succeed.

Embark on your journey to becoming an AI data specialist and unlock new opportunities in the world of data and technology, get in touch with us now!

Enquire now

Projecting Success aren’t just working on data driven project delivery, we are taking a pivotal role in shaping and creating this future. If you’d like to speak to one of our experts on the subject then please register below, or drop us an email to enquiries@projectingsuccess.co.uk