Q: Why were you drawn to work in the utilities industry?

When I initially took a role in this sector, working with a large water company, it wasn’t just about having the opportunity to tackle interesting technical problems. It was about working on projects that have a wider impact and which also aligned with my own personal values. Whether it’s improving the reliability of energy supplies, reducing water waste, or enhancing customer service, the work I do needs to have a direct, positive effect on society. I’ve always been passionate about the environment and the outdoors, and it’s really important to me that my work contributes to a sustainable future for my son and the next generation.

Q: What’s the best thing about your job? 

When I think about what excites me most about working in technology, it’s not the cutting-edge gadgets or the latest software updates. It’s about bringing everyone’s different talents and skills together, tapping into a collective intelligence in order to create something meaningful. The real thrill comes from watching a team collaborate, each member fired-up and committed to a shared goal. When we succeed, and I see the client’s eyes light up because we’ve delivered something truly valuable, that’s when technology feels like a team sport. 

In my role as a strategic advisor, I’ve been lucky enough to work on a wide range of projects – from greenfield initiatives to large-scale transformations. One memorable project involved a large organisation struggling with data access. All of their data was buried deep within their Oracle and SAP systems. We implemented (what we’d now call) a data mesh – a solution that streamlined data retrieval and integration, which allowed the company to deploy some innovative new solutions more effectively. This experience really brought home to me the fundamental role that data plays in driving transformation in utilities. It inspired me to look at ways in which technology could be used to improve processes and operations within the energy, utilities, and environment sectors.

Q: What are some of the key challenges in the utilities sector today?

The utilities sector is well and truly in the spotlight. The complex set of challenges it faces, are under intense public and media scrutiny. Take the water network, for example – frequent leaks and poor performance means that it’s ripe for newspaper headlines. But fixing these issues requires more than just incremental improvements; it needs large-scale transformation driven by technology.

Interestingly, I’ve found that the challenges faced by utilities are strikingly similar to those in government sectors – both involve highly regulated, large, and complex organisations with a slow pace of change. The digital transformation seen in government, particularly the initiatives led by the Government Digital Service (GDS), mirrors the journey utilities will need to take. To meet these challenges head-on, they’ll need to embrace modern, innovative technologies that offer better outcomes, improved pace and greater value for money.

Q: Where do you see the main opportunities for digital transformation in utilities?

With the clever use of technology, we should be able to enhance every step of the energy and water journey – from source to public consumption, leading to more efficient operations and better results for everyone involved. I think we’re really at a digital cross-roads in terms of which direction organisations choose to take. Here’s a closer look at some key areas where digital transformation can make a significant impact:

1. Improving utilities network and infrastructure

From the initial generation of energy or water to the delivery systems that bring these resources into our homes, there’s substantial room for improvement. Technologies like sensors and monitoring systems enable us to keep a close watch on infrastructure, spotting potential issues before they escalate into major problems. This proactive approach means fewer leaks, better maintenance, and more reliable service overall.

2. Data-driven insights in utilities

With the rise of advanced technologies comes an explosion of data. Very few models will generate as much data as a massive water network for example.  But managing and getting insights from this data presents a challenge. Different technologies produce data in various formats, complicating the process of data ingestion and analysis.

Upgrading our data management capabilities is pretty fundamental. When data is handled effectively it can enable us to schedule maintenance where it’s most often needed, allow us to predict equipment failures and optimise usage. This capability not only improves operational efficiency but also allows us to respond to issues promptly. Data can also help to influence behaviour as well. More on this in my blog behavioural change starts with data.

3. Automating energy and utilities operations

A lot of work in the energy and utilities sectors is still done manually, which can be slow and error-prone. Digital tools offer up the opportunity to automate these processes, making operations faster, more reliable, and less prone to mistakes. Automation allows the workforce to focus on more critical tasks, ultimately improving the overall service provided to customers.

4. Supporting remote utility workers with digital solutions

Many workers in the utilities sector are remote, whether they’re in the field or at various sites. Equipping these workers with the right digital tools is essential. For example, providing field workers with real-time access to data and work orders via mobile devices can enhance productivity and safety. Making sure they have the right schematics at their fingertips when they arrive on site for a dig is key to their efficiency.

5. Using data to improve customer experience and reduce bad debt

On the customer side, there’s a significant opportunity to use data to improve experiences and address issues like bad debt. Unpaid bills can amount to billions, making it crucial to understand and support customers who might be struggling with payments.

One innovative way to approach this is by using data to identify potential vulnerability triggers. For instance, local government datasets, such as council tax arrears, can offer clues about individuals who might be at risk of defaulting on their bills. By analysing this kind of data, organisations can anticipate problems before they escalate and offer targeted support.

Imagine using insights from these datasets to tailor services or create intervention strategies for customers who are likely to face financial difficulties. This proactive approach not only helps in managing risk more effectively but also improves customer satisfaction. When customers feel understood and supported, they’re more likely to engage with the services provided, leading to fewer instances of unpaid bills and better overall outcomes.

Q: How can utilities better handle their data?

Utilities companies often partner with third-party providers to build infrastructure and deploy sensors. However, challenges arise in managing the data these systems generate. Sometimes, the data remains with the provider, making it difficult for the utility company to efficiently imbibe and use it.

Key questions they should be asking are: How do we gather and manage this data? How do we filter out irrelevant information and get the relevant data to the right teams? And how do we ensure data reliability, traceability, and governance?

Solving these issues is not just a technical challenge but also a cultural shift. Companies need to start treating data as a product, focusing on delivering value and user-centricity. Many organisations are still learning how to do this, but they need to keep the goal in mind. Their aim should be to look at how data can be used to better serve internal teams and improve overall efficiency.

Q: What does the future look like from where you’re standing?

If you’re in the utility sector, the possibilities for using technology and data are not just exciting – they’re transformative. Now is the time to act. The future of utilities will be defined by our ability to innovate, adapt, and use digital tools to make a meaningful impact on society.

For me, this is more than just a professional journey – it’s a personal mission. I’m passionate about creating a sustainable future. The work we do in this sector has the potential to solve some of the most pressing challenges of our time, from reducing waste to improving customer service and ensuring the reliability of essential resources.

At Made Tech, we’re committed to being at the forefront of this change. If you’d like to find out more about jobs here, visit our careers pages. 

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James West, Industry Director for Public Safety and National Security at Made Tech, gives us his views on some of the challenges and lessons he’s learnt whilst working on some recent data and digital transformation initiatives.

Q: What are some of the key challenges currently facing UK Public Safety and National Security  agencies?

As you’d expect, all of the public safety organisations I talk to tell me they’re facing a relentless battle against increasingly sophisticated crime. Tactics are now multi-faceted and digital crime regularly crosses international borders. Attackers are constantly innovating. Techniques which were once easy to spot, now mimic legitimate sources making them even more likely to succeed. Cybercriminals are also taking advantage of AI and machine learning (ML) to automate tasks and personalise attacks, making crime more targeted and impactful¹.

I’ve seen a noticeable shift in end-user expectations, being driven by the progress of technology and the “Netflix effect” of instant service. Citizens now expect quick, easy access to public services. Public safety organisations must match the service standards seen in consumer markets. Whether it’s tracking a reported crime or checking their passport status, people want real-time updates at their fingertips.

One of the most pressing challenges is identifying the insights from massive data sets. Public safety organisations are all inundated with large volumes of data, which create issues in terms of interoperability, data quality, insights and informed decision-making. Various sources generate vast amounts of data and manual processes with legacy systems complicate day-to-day operations, making it difficult to use information effectively.

Last but by no means least, many public safety and justice organisations are up against it when it comes to capacity and demand management. This limits their ability to fix underlying problems. Organisations are often stretched thin, with tight budgets and employee shortages. In my experience this forces people to prioritise immediate tactical investments over larger transformation initiatives.

Q: What are some of the lessons you’ve learnt when tackling the challenges in public safety?

1. Expose the problem

‘Numb pain’ is something I come across regularly in my advisory and delivery work with public sector organisations. It’s a silent adversary that’s often underestimated. Users live with limited technology and data investment to the point that when faced with process challenges or tech inadequacies, they often put in place ‘workarounds’ so they can continue to deliver – albeit inefficiently. In many instances these new ways of working are accepted as the norm and users become blind to the actual issue.

To get a handle on this, departments need help exploring the aspects of ‘numb pain’ within the organisation. From that point, you can then talk to stakeholders at all levels to identify the pain points that have been overlooked or covered up. Next step is to prioritise the problem resolution that will deliver the most impact and devise an accelerated action plan to tackle the issues. Gone are the days of waiting months or years for your return on investment. With the right digital partner you can perform a mega leap.

2. The key to digital transformation success is simplicity

I’ve picked up some useful skills and battle scars along the way to deal with the complexity of digital transformation in public safety services. It’s a cliché but you really should keep things simple. Even in the face of significant tech transformations or re-engineering out-of-date legacy systems, simplicity is key to making sure your tech or your new processes get adopted.

I’ve found that it’s best to first compartmentalise the information. Once I’ve understood how it’s all linked, I can build an architecture map in my mind and walk others through the journey. I’m then able to see the bigger picture and translate what’s possible in terms of transformation to the business.

3. Discover how to use technology as your capacity multiplier

I often hear clients call out capacity constraints as the root of many of their persistent problems. But capacity should really be about maximising the impact of the invaluable people you have working at any given time. I often see technology and data projects approached with initial scepticism, but we really are in an era where tools like automation, data and AI provide the opportunity to stretch your capacity.

It may sound obvious – but one way to do this is to simply observe and record the repeatability of tasks performed. Or identify the multiple requests for updates or information in respect of the same topic. Using technology and data points to deliver these tasks frees up valuable time and energy to address more critical issues.

Q: How can data and automation help bridge the gap between lack of resources and transformation?

The sheer volume, inadequate data management systems and fragmented information across different public safety departments exacerbates a lot of day-to-day problems. It makes it pretty much impossible in many cases to provide timely and accurate services to citizens. That being said, we still manage it in most cases. But I often challenge people at what cost? The huge volume of data that Public Safety agencies have at their disposal represents a big opportunity. This is where AI and ML can step in. Helping organisations make sense of the mountains of data and empowering people to make decisions in real-time. Arguably in the future, we’ll be able to autonomously prevent demand with intuitive citizen contact.

The power of integrated data in criminal justice

Consider the case of a prolific offender who interacts with multiple agencies within the criminal justice system. From probation services to court proceedings, each touchpoint generates valuable data that. As a whole this paints a picture of this person’s journey through the system. As a result of spotting intervention points early in this journey, agencies may be able to prevent re-offending. They can address underlying issues such as unemployment or housing instability which may have led to the initial offending. When agencies gain better access to integrated data and actionable insights, they can collaborate more effectively. This allows them to provide tailored support that makes a real difference.

Using automation to drive public safety transformation

While this vision of a single citizen view may seem daunting, the process can be broken down into manageable steps. Agencies can use automation to streamline routine activities and focus their time and effort on high-impact tasks. For example, automating searches for missing persons across multiple databases can significantly reduce response times and improve outcomes.

The journey towards public safety transformation is not without its challenges. From the dead weight of ‘numb pain’ to the constant pressure to meet high user expectations. But for those that embark into the potential unknown safely with pace, the rewards will be well worth the effort.
If you’d like to find out more about some of the services we provide to public safety and national security clients at Made Tech, check out our case studies with the Ministry of Justice and Prisoner Services or take a look at our web page.

1. IBM Security. (2023, July 18). IBM X-Force Threat Intelligence Index 2023. ↩︎

The post Top 3 ways to address the challenges and demands in public safety services appeared first on Made Tech.

Q: What does a data scientist do?

My data journey started in academia. I don’t think I would have stuck at physics as long as I did if I didn’t enjoy analysing data and answering questions. Even with a PhD, I struggled to break into the industry. I started as a junior developer and involved myself with as much data work as I could. 

As a data scientist at Made Tech, my main task is to analyse large datasets and provide actionable insights for clients. Often, I start projects without much context or background knowledge, receiving raw data and being asked to extract value from it. It’s about not being intimidated by vast amounts of data and asking the right questions to understand the problem at hand.

Q: What skills do you need in data science?

Data science is primarily about your ability to craft and present a story using data. Communicating technical information to non-technical stakeholders and presenting, are your bread and butter. Secondary to this, it’s helpful to have a strong foundation in programming Python or R, as well as proficiency in statistical analysis and machine learning algorithms. 

Q: What are some of the challenges when it comes to working on data projects?

How to get hold of the right data

Organisations usually provide what they believe to be the right data, but I’ll often need to ask additional questions to gather further context and make sure that the data they’ve given me is actually related to the problem at hand. 

Clients aren’t trying to hide anything; they genuinely want some help to understand and make better use of their data. However, they may not always provide all the right information upfront. Knowledge that is second nature to them often isn’t obvious to an outsider looking in.

Addressing poor data governance

My nemesis is Excel because it gives everybody just enough power to cause a lot of trouble! Versioning issues and poor data governance can seriously affect data management projects. A beautifully formatted Excel spreadsheet with animations and gradients looks fantastic, but actually doing something meaningful with it can be hard. First I have to strip away the bells and whistles just to get to the raw data, and this is all before I discover that everyone has their own version of “real_final_final_report_v3.xls”. 

Handling bad historical data quality

If you go back a few years, data quality was considerably worse than it is now. So for example, if I’m developing predictive models, then I need to look back in order to look forward and the further back I go, the worse the data quality. The penny has started to drop. Organisations are now realising that the quality of their data will affect their future ability to deploy AI and Machine Learning (ML) tools. As a result, I’ve seen data quality really starting to improve. 

Q: What’s the first step on a data project? 

The first question I always ask is about a client’s data maturity. It’s important to understand their current level of data management. If they’re unsure what ‘data maturity’ actually means, a simpler question like, “Where do you store your data?” can help to shed light on the situation. 

Clients often boast about using data-driven insight. But in reality, it’s usually just one person trying to interpret a graph. These individuals often lack the training of an analyst or data scientist and they may feel overwhelmed by the data they’ve inherited.

Q: Can you give me an example of a data project you’ve worked on and the benefits it’s delivered?

An example that springs to mind is a piece of data modelling work we did recently with Skills for Care, a strategic planning body that monitors the adult social care industry.

Care providers aren’t currently required by the government to report the number of carers they employ or how many work at a specific facility. This means that at the moment, the government has limited visibility of the true scale of the care sector. 

While Skills for Care were using machine learning to estimate the size of the sector, they still lacked the time and experience to really harness the full potential of the data they have.  After joining the project, I quickly worked through some initial exploratory analysis, before building a predictive data model that incorporated characteristics such as geographical location and other features of care homes. The improved model can now far more accurately predict the number of carers at a specific facility, providing valuable insights for the government on the size of the adult social care sector, which includes tens of thousands of care homes. 

Some care homes already report their numbers reliably, but our model has significantly improved the Skills for Care data quality. Now they know which questions to ask and have automated processes to make it easier to upload data.

Improved data accuracy

Skills for Care initially used just two features to create a simple statistical (regression) model with their data, which took their analytics team about six months to prepare and run, achieving an accuracy (R-squared value) of around 56%. By the time we completed our project in March, we had revolutionised their data process. What used to take six months is now a 20-minute automated job that runs every two weeks. We expanded their model from using two features to 58, boosting the accuracy to an R-squared value of 86-90%. 

For me, the most rewarding part is enhancing the quality of data and reporting. This data is now included in reports provided to the Cabinet Office and while we can’t control government decisions, we can make sure that they have access to accurate data for informed decision-making.

Q: Can you tell me more about the intersection between Data Science and AI?

To be clear, AI is a branch of data science. In recent years, the hype has focused on Generative or Gen AI and Large Language Models (LLMs). But the reality is that these tools aren’t as new as they seem. Data scientists have been using similar techniques for years. The promotion is being driven by marketing and venture capital interests. Companies use AI as a buzzword to attract investors and boost their share prices. In my experience, there is a limit to how much impact a fancy chatbot can have.

The risks of AI

Without the human-in-the-loop, AI models can mislead and cause significant legal and ethical issues. Hallucinations, where AI generates false information, are already causing havoc. Another area where organisations regularly trip up is around compliance. If you’re using AI models such as ChatGPT and anthropic Claude Three, your data will be loaded to servers in the US. This raises privacy concerns, and falls foul of regulations like GDPR. Instead, the true potential of AI and the art of data science lies in augmenting human processes, offering a greater level of insight to leaders and decision makers and providing real time analysis that allows them to make better decisions.

What is responsible AI?

​​Understanding how your AI model interacts with your data to produce results is crucial. The explainability of your AI needs to be a top priority. At the end of the day, whichever mathematical model you’re working with, be it a regression, classification, or a clustering model, it’s ultimately the maths behind it that drives the outcomes.

For instance, if your model categorises a person as ‘A’ or predicts a spending amount of ‘B’, it’s essential to be able to explain why these decisions were made. Someone might ask for clarity on these points, and being able to break down the model’s process is a key aspect of data science. This transparency is what we mean by responsible AI and explainability.

Q: What can organisations do to protect themselves when using GenAI?

A couple of years ago, my answer would have been to avoid using them altogether – there are too many security and liability issues. There’s also an entire data science sub-discipline – Natural Language Processing (NLP)-  that would deliver 90% of the value with none of the risk or cost (but these services are expensive to integrate). That said, GenAI has continued to develop and, these days, organisations can reduce the risks considerably. These are some of the options:

Understanding the maths behind the data

The world of data science and AI has been an exciting journey for me. Whether it’s improving the quality of data for better decision-making, or making sure that AI models are transparent, the principles remain the same. It’s about breaking down the data, understanding the mathematical relations  behind it, and turning raw information into insights that make a real difference.

And at the end of the day, never forget that it’s all just maths. Whether you’re working on a simple regression model or deploying advanced Generative AIs, understanding the underlying mathematics will always point to the right solution.

If you’d like to find out more about jobs at Made Tech browse our careers or take a look at some of the data consultancy and AI services we provide to clients.

Laying the groundwork for AI

Unlock your AI potential: Discover your archetype, master the 3 pillars of data maturity, and learn from real-world transformations in our latest whitepaper, Laying the Groundwork for AI.

Download the whitepaper

The post Turning numbers into narratives appeared first on Made Tech.

Why federated governance is key to agility

Here’s where federated governance can provide the answer. This decentralised approach to managing data and making decisions, gives a new perspective; focused on principles rather than processes. It reminds me of when software architecture changed from monolithic mainframes to micro-services, allowing organisations to make use of centralised data platforms whilst embracing decentralised decision-making.

It prompts you to consider how you want your data to be processed, governed and shared, setting the stage for a more agile and collaborative approach. A move to this way of working has now become essential if you want to introduce transformational technologies. 

Data quality is fundamental to AI transformation

In the world of AI and ML, data quality rules. Federated governance acknowledges this and relies on all parties sticking to the same data standards. It’s not just about having data; it’s about having data of a known quality.  If you want to use information from external sources you need to be able to trust it. Using a decentralised  approach it’s crucial to minimise the risks associated with inconsistent data and ensure integrity throughout the process.

The two phases of data science – training and inference, where you’re using models to make predictions or draw conclusions, are heavily reliant on data quality. If the data you’ve used for training is flawed, it can compromise the entire process. Therefore, establishing stable and reliable data sources is key. Understanding the sources of your data is also vital. In my opinion, unknown data quality is the worst scenario, as it leaves you in the dark about which techniques to adopt for improvement.

Why domain experts should govern their own data

Centralised teams often struggle to keep expertise across diverse domains, but with federated governance, the data expertise stays aligned with the domain knowledge. In this way, each team governs its own data according to agreed-upon principles. Identifying the data with meta tags then becomes all important, allowing those closest to the data to decide what the access policies should look like. I believe that all of this helps to foster a more agile and inclusive approach to data management, moving away from bureaucratic processes. 

The cultural shift needed for data transformation

To get buy-in for this new approach, it’s important to understand that you’ll also need to support a cultural change. As the teams who own the data begin to define the access principles and take on the responsibility for that data, data management starts to become a wider team responsibility. This shift from leaning on a central team means that some stream-aligned teams will need on the job training and support to be helped through the initial learning phase. By gradually decentralising responsibilities, domain experts will ultimately feel more empowered and enabled and you’ll rapidly see the rewards of this transition.

How federated governance fuels innovation

So if you’re looking to take advantage of the full potential of AI and ML technologies, moving to a federated governance model is essential. By decentralising, encouraging collaboration, and driving cultural change, you’ll be able access the full potential of your data and drive innovation for your clients and end users.

Supporting Hackney residents with a modern data platform

At Made Tech, we recently helped Hackney council to harness the power of their data, creating a new platform that would allow them to store their information, consolidate and analyse it all in one place. We worked closely with the team to design and deliver a cloud platform to help the council get better visibility of their data and deliver the best possible service for local residents. 

I’m hosting an upcoming event with Government Transformation Magazine on the theme of federated data governance and how it will be crucial for AI integration in the Public Sector.  Sign up for our newsletter if you want to be one of the first to read the post-event round-up and in the meantime visit our web page to find out more about our data capabilities.

Laying the groundwork for AI

Unlock your AI potential: Discover your archetype, master the 3 pillars of data maturity, and learn from real-world transformations in our latest whitepaper, Laying the Groundwork for AI.

Download the whitepaper

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