Last month, the UK carmaker initiated the most significant shift in its 102-year history, unveiling a new logo and a “social media tease” as part of its transition to an all-electric brand. However, critics dismissed the campaign as “woke”, noting the absence of any actual cars.

Earlier this month, JLR also revealed its new EV concept, the Type 00, at the Miami Art Fair. The car’s bold colours—shocking pink and electric blue—sparked further debate.

Though JLR’s finance transformation director wasn’t there to defend the brand’s strategy, he noted that exclusivity requires a focus on target customers, not universal appeal.

“This is an ambitious strategy. Some people love it, and some people hate it, but the world is changing to a point that no change equals no future for us,” said Lee, JLR’s director of finance.

Lee told conference delegates how JLR is heavily investing in electrification and automation with a goal of zero carbon emissions by 2039 as part of its wider transformation plan.

According to Lee, his department is central to the car maker’s transformation into a brand that will attract a new generation of eco-conscious, high-net-worth individuals.

“The finance department sees the end-to-end value creation system: every action has a transaction that we record and report; every plan needs a business case. Every strategy needs to be funded and financed.

“So, finance is key. As director of finance, I’m focussed on process excellence and creating data assets which are analytics and AI-ready,” he explained.

Process transformation

Central to this journey, said Lee, was the part of the business that used to take care of delivering business strategy and compliance – which, he added, used to be “a back office shared service centre in a low-cost country”.

This “finance digital core” has now been transformed into a technology hub populated by process and data experts focused on “standardising and automating global processes through technology, data management and analytics,” Lee added.

In terms of technology partners, one key vendor is Appian, Lee added, and the process automation firm has most recently been helping the carmaker with processes around improving and negotiating changes to its material costs.

“The system was designed 20 years ago and has grown over time with lots of technologies being added through purchasing and finance organisations.

“We’ve been trying to automate tasks as opposed to designing end-to-end processes in a coordinated way. It used to be very manual – a Frankenstein process that did not really fit together,” he said.

He added that many organisations ended up with such beasts: “If you focus on task automation you will get an awkward process that doesn’t integrate or connect,” Lee observed.

However, two weeks ago JLR finished integrating an Appian solution that connects all these different processes into a single system, improving the user experience and outcomes and creating a single source of truth.

“We’ve moved from a fragmented process and disparate systems to an end-to-end curated and connected process in a single system,” he added.

Lee also outlined how Appian helped JLR streamline supply chain sourcing post-Brexit and during the Covid-19 pandemic by providing a unified low-code platform to manage complex processes.

By integrating data from disparate systems, Process HQ enabled a real-time view of supply chain operations, enabling JLR to track parts, suppliers, and compliance with post-Brexit regulations efficiently.

“Whatever your politics, Brexit was a pain for us. We had to import £8 billion worth of parts from Europe into the UK to keep our manufacturing plants running,” he told delegates.

“Appian’s solution has helped us mine that data to help us create a really good robust way to understand what was happening and how we could make it better,” he added.

This was achieved by enabling the carmaker to coordinate the supplier, courier, and broker documentation so that it could manage that process in a streamlined and automated way, outlined Lee.

“We used to have 250 people manually working on this process, and now we have less than 100 and we are looking at reducing this further.”

Making the most of JLR assets

According to Lee, JLR has also started a fixed assets management lifecycle system which is a process designed to oversee and manage physical assets—be it machinery, tools, vehicles, and facilities—throughout their lifecycle.

This system claims to ensure efficient utilisation, cost management, and compliance with regulatory standards.

“Previously the data that manufacturing, purchasing and finance we were holding was in Excel spreadsheets and we couldn’t align our data, resulting in a capitalisation backlog of £1.1billion,” he said.  “The Appian solution will make that flow continuous and avoid Excel and backlogs going forward.”

Lee pointed out that aligning the manufacturing maintenance schedule and the finance asset register has also given JLR finance the opportunity to remove finance asset verification from the process that JLR used to run on a cyclical basis every three years.

“Again, another huge efficiency through collaboration,” he added.

Using the same Appian system Lee said that the department has also started a process that aids the circular economy internally, by creating an internet e-commerce marketplace to see what parts are available for reuse to avoid spending more money on new versions of things that the organisation already has.

Lee wrapped up by pointing out that automating all these processes and creating a single source of truth set the organisation up for AI experimentation.  “We’ve created some great opportunities that will create great data assets that will then allow us to explore AI opportunities,” he said.

The post Jaguar Land Rover’s finance director on driving process transformation appeared first on TechInformed.

In February, the EU’s Copernicus Climate Change Service warned that global warming and heatwaves had caused a year-long breach of the 1.5C target set out in the Paris Agreement.

The post Turning the heat up: an employer’s guide to heatwaves appeared first on TechInformed.

In today’s digital age, drivers rely heavily on data from every conceivable direction — from engineering strategies in the control room to real-time tactics on the track — to remain competitive.

“Without data, we can’t even get the car to move, let alone race,” tells Brian Johnston, head of IT Trackside at Aston Martin F1, to NetApp, its data partner.

Aston Martin F1’s data strategy for race day success

“F1 teams generate a huge amount of data, gathered from simulation back at the campus, as well as on track. Every time the car completes a lap with the team, the simulation and actuals need to be correlated,” adds Clare Lansley, AMF1’s CIO.

On top of this, there’s also the media coverage that each race and team receive. “All this data needs to be tagged, analysed, stored and leveraged for competitive gain — all of which relies heavily on IT to enable and do so at lightning speed,” she explains.

Lansley underscores that Aston Martin Aramco F1’s strategy is deeply rooted in data — lots of it — and therefore, careful organisation and scaling are crucial. “The digital world is founded on data and lots of it,” she notes.

“Analytics, machine learning, and now, artificial intelligence all require access to vast volumes of data. Businesses are keen to leverage this data, transforming it into a strategic asset to drive innovation, enhance efficiency, and ultimately secure a competitive edge.”

Simultaneously, F1 teams must adhere to the stringent FIA budget regulations, spending no more than $140 million annually to maintain fairness and competition.

This emphasis on cost-effectiveness, coupled with the need for sustainability, led AMF1 to select NetApp as its data management partner.

How NetApp and cloud solutions power F1 performance

NetApp’s partnership integrates its comprehensive technology with FlexPod (its data centre solution with technology giant Cisco) and cloud computing firm VMware, to effectively handle all of AMF1’s data needs.

“We need both on-prem and cloud to give us the flexibility with capacity and computer capability where we need it,” says Lansley. “NetApp was the obvious choice for helping us build a race fabric to be competitive.”

A pivotal part of this partnership is the secure and cost-effective data storage while making it accessible to drive performance.

Since its return to the grid in 2021, AMF1, with over 600 team members, has outfitted each car with more than 300 sensors. These sensors constantly collect metadata, including weather conditions, timing, torque, and driver performance, fuelling race-winning designs and real-time trackside analysis.

“Demands such as this increase in data are driving the need to rearchitect infrastructure,” Lansley notes. “Users want performant, resilient solutions that can run queries simultaneously with instantaneous scalability to ‘burst’ capacity as required.”

Driving innovation with sustainable data solutions in F1

Key to maintaining its competitive edge is ensuring the infrastructure is secure against cyber threats, which drives the need for data centres to be rearchitected and modernised.

Plus, as Piero Gallucci, vice president and general manager, UK & Ireland at NetApp, puts it, F1 is surprisingly sustainability-focused, intending to reach net zero by 2030.

He explains that with NetApp’s intelligent data infrastructure, extracting any ‘dark data’ (invaluable data), AMF1 can optimise the energy costs of data storage.

Lansley further explains, “We look at how and where data is being stored and use tiering to optimise storage solutions, which include ‘cold’ offline storage where it’s not draining power 24/7.

We also use renewable energy; the new Aston Martin Aramco Technology Campus building has 1,936 solar panels on the roof, which makes a considerable, sustainable contribution to our power consumption.”

Choosing partners aligned with sustainability is a priority for Aston Martin F1. “Our work with NetApp allows us to meet sustainability goals, giving us greater control over our temperature and power monitoring so we can better manage our energy consumption,” Lansley concludes.

This commitment ensures that while the team races toward victory, it upholds its responsibility to the environment — pushing the pedal to the metal with a conscious effort towards sustainability.

The post How Aston Martin F1’s data management is driving its cars appeared first on TechInformed.

Storage giant NetApp serves diverse clients, including public sector organisations, banks, US government departments, energy companies, and even Formula 1 teams.

Caley, who nowadays opts for a white Americano, started his technology career working with databases in the NHS and as a technical designer at IBM.

The self-professed gamer, who owns a Steam deck and VR headset, also touched on AI’s impact on customers’ sustainability and cybersecurity goals, the intricacies of working with motorsport data, and valuable lessons from the recent CrowdStrike Windows outage.

Five lessons from the CrowdStrike Windows IT outage

What has motivated you to stay at NetApp for 24 years?

The culture at NetApp has always been great. It’s constantly evolving with technological changes like AI and cloud computing. I’ve transitioned through various roles, starting in pre-sales in the UK, moving to a global role in pre-sales, and travelling the world for about seven years.

Eventually, I shifted to a chief technologist role in the UK, then into pre-sales management, and now I’m the pre-sales director. The variety and the great culture have kept me here.

Have you felt the effects of increasing use of generative AI?

We’re seeing a lot of interest and projects starting to spin up around generative AI. However, it’s still new territory for many customers who are figuring out how to use and integrate it with private data securely. The challenge is to use this technology efficiently while keeping data secure and sustainable.

Which industries are handling it the best?

It’s varied. Gen AI has applications across numerous fields — customer experience, programming, research, technical writing, and more. No single industry stands out because many are finding diverse and innovative uses for it. It’s like a Swiss Army knife; you need to figure out the best way to use it for your specific needs.

How do you address sustainability concerns regarding the use of AI?

AI relies heavily on data, which requires significant storage and processing power. We certainly talk to a lot of companies and advise them on optimising their data to use less infrastructure, which in turn uses less power and cooling.

One big mistake is just putting all of your data into generative AI — you don’t necessarily need to because a lot of it could be junk or irrelevant. Tidying up your data before feeding it in and using less data means less infrastructure, less cooling, and less energy.

Is data security a major consideration when exposing data to Gen AI models?

It does introduce new challenges. You’re exposing private and public data to these models. You need to think of the ‘cyber resilience wrapper’ that goes around this because using so much data makes your firm a target for cybercriminals.

Companies have to consider many additional considerations in that respect, not just using data but also securing it and ultimately making it recoverable.

If generative AI becomes critical to business operations and companies do get attacked, any loss could massively impact regulations. So, they must be fully secure and recoverable.

How did the CrowdStrike incident illustrate the risks of relying too heavily on a single vendor for IT security?

The CrowdStrike incident was interesting because it highlighted the reliance on single vendors for specific tasks within IT infrastructure. The FCA and the UK, for example, are introducing many rules about over-reliance on the cloud and warning financial services to be aware of this.

The DORA regulation has also arrived in Europe, making sure companies build protection mechanisms around that. If there is an outage in an environment, they’ve got the capability to fail over somewhere else and recover to keep on running to avoid these instances.

You can argue that the CrowdStrike incident was a wake-up call for many companies. Still, it will mean that we’ll start to see companies diversifying where they put their data and what technologies they use around it to ensure that they’re not reliant on single vendors.

How do motorsports teams exemplify strong data management practices?

We sponsor Aston Martin F1 and Porsche’s Formula E, and we’ve sponsored Ducati in Moto GP — I suppose someone on our marketing team must love racing.

Data in those sports is critical because every car and motorbike nowadays is almost like a mobile sensor array. They’re capturing video feeds and sensor feeds in real-time. All of that comes to the trackside, is manipulated to give the driver advice, and is then also passed to the design centre for onward processing and analytics.

Data is a huge driver of motorsports, and that’s one reason we work quite closely with those companies.

Although it doesn’t seem like it, you’d be amazed at some of the sustainability technologies they’re building into these cars. I think it’s good because those technologies will be in our cars five to six years from now, once they become commercialised.

Data comes into play when determining how much infrastructure you need to optimise. That’s a big part of what we do to help them be sustainable, at least on the infrastructure side.

How do you wind down and switch off at the end of a long day?

I’m a gadget geek. I enjoy playing with new technologies. My wife and I also enjoy walking with our two dogs in the Lake District, which is a great way to relax.

I’m into all kinds of gadgets, from smart home devices to gaming consoles like the Steam Deck and VR headsets. I can’t resist new and interesting tech.

The post A coffee with… Grant Caley, UK and Ireland Solutions Director, NetApp appeared first on TechInformed.

Furthermore, a McKinsey & Company survey shows more companies are using AI across multiple business functions — half of respondents reported adoption in two or more areas in 2024, up from less than a third in 2023.

Similarly, according to Statista, almost 11%  of employees working at global firms have tried using ChatGPT in the workplace at least once.

However, this widespread adoption brings new security challenges, particularly regarding data privacy. For example, of those who used ChatGPT at work, almost 5% have put confidential corporate data into the AI-powered tool.

In fact, nearly one-third of employees have admitted to placing sensitive data into GenAI tools, making data leaks a top concern.

According to a report by AI security solutions provider Hidden Layer, more than three-quarters of companies either using or exploring AI have experienced AI-related security breaches.

How are businesses using Gen AI?

A study by Harmonic Security titled GenAI Unleashed found that Gen AI was used by employees to upload data to 8.25 apps on average every month.

The study found that content creation, summarising, and editing were overwhelmingly popular among workplace users, with around 47% of prompts asking apps for help in those areas.

They were followed by software engineering (15%), data interpretation, processing, and analysis (12%), business and finance (7%) and problem-solving/troubleshooting (6%).

The most popular Gen AI tool by far was ChatGPT, used by 84% of users — 6 times more popular than Google Gemini (14%), the next most popular tool.

Alastair Paterson, co-founder and CEO of Harmonic Security, explains, “With a choice of over 5,000 GenAI apps and a high number of average apps used by employees, there are too many out there for IT departments to properly keep track of using existing tools. We particularly urge organisations to pay attention to apps that are training on customer data.”

How can companies ensure data privacy when using Gen AI tools?

TechInformed consulted industry experts to compile a list of best practices for safeguarding data privacy in the era of Gen AI; here are our top tips.

1. 1. Avoid inputting personal or sensitive information into Gen AI LLMs

Generative AI tools and large language models (LLMs) can store and repurpose data provided to them. To prevent unauthorised access, avoid inputting personal or proprietary information into these tools.

Sebastian Gierlinger, VP of Engineering at Storyblok, says, “The biggest threat we are aware of is the potential for human error when using generative AI tools to result in data breaches. Employees sharing sensitive business information while using services such as ChatGPT risk that data will be retrieved later, which could lead to leaks of confidential data and subsequent hacks.”

He says the solution could be as simple as educating employees about how to use tools like ChatGPT safely.

That said, Leanne Allen, head of AI at KPMG UK, adds that “there are security measures that can remove sensitive or personal data automatically from prompts before they are used by a generative AI model. These measures can help mitigate the risk of data leaks and breaches of legally protected information – especially since human error will likely still occur.”

2. 2. Create and enforce an AI & Privacy policy

A comprehensive company policy on AI usage and data privacy can help mitigate many risks associated with Gen AI tools.

Angus Allan, senior product manager at CreateFuture, says, “Establishing a clear AI policy from the outset can streamline this entire process by enabling businesses to tailor controls to their risk tolerance and specific use case.”

Allan stresses the importance of tailoring any policy to the specific company and addressing how AI will be uniquely leveraged for that industry and use case.

“An AI policy not only pre-empts data privacy risks but also sets clear expectations reduces ambiguity, and empowers teams to focus on solving the right problems,” he says.

“In an era of GDPR and increased regulatory scrutiny of AI, it’s imperative for every business to get these basics right to minimise data risks and protect customers.”

3. 3. Manage data privacy settings

Most Gen AI tools have features that allow users to disable data storage. Employees should navigate to the tool’s settings and disable such features to prevent company data from being used for AI model training.

Patrick Spencer, VP of corporate marketing at Kiteworks, explains, “A typical disablement feature looks something like this: navigate to Settings and, under Data Control, disable the “Improve Model for Everyone” option. Regularly review permissions to prevent unnecessary data access, ensure privacy, and thwart unauthorised access.”

Deleting chat histories in AI tools can also reduce the risk of sensitive information being stored, he says.

“OpenAI typically deletes chats within 30 days; however, their usage policy specifies that some chats can be retained for security or legal reasons. To delete chats, access the AI tool’s settings and find the option to manage or delete chat history.”

He adds this should be done periodically to maintain data privacy and minimise vulnerabilities.

4. 4. Regularly change passwords; or ditch them altogether

When using passwords, they should be long, complex, and unique for each account, including those linked to AI systems. However, CEO of cybersecurity startup Teleport, Ev Kontsevoy, advocates for moving away from passwords altogether.

He details, “Every enterprise housing modern infrastructure should cryptographically secure identities. This means basing access not on passwords but on physical-world attributes like biometric authentication and enforcing access with short-lived privileges that are only granted for individual tasks that need performing.

Cryptographic identities consist of three components: the device’s machine identity, the employee’s biometric marker, and a PIN. Kontsevoy says businesses can significantly reduce the attack surface threat actors can exploit with social engineering tactics by using them.

“If you need a poster child for this security model, it already exists, and it’s called the iPhone. It uses facial recognition for biometric authentication, a PIN code, and a Trusted Platform Module chip inside the phone that governs its ‘machine identity.’ This is why you never hear about iPhones getting hacked.”

5. 5. Disconnect your systems from the internet

Tony Hasek, CEO and co-founder of cybersecurity firm Goldilock offers a unique solution: physical network segmentation, the ability to connect and disconnect networks at the press of a button.

“Through a hardware-based approach, physical network segmentation enables users to segment all digital assets, from LLMs to entire networks, remotely, instantly and without using the internet,” he says.

He adds that businesses can reduce the level of sensitive data exposure by rethinking which parts of their networks they keep online and moving away from an “always-on” model.

“Companies who are building their own internal large language models (LLMs) in-house are essentially creating a repository for their company’s most valuable data and intellectual property, including customer and employee data, trade secrets, and product strategies. This makes LLMs and other Gen AI models a prime target for cybercriminals.”

He concludes, “Keeping Gen AI models offline until they are needed to generate a response is a critical step in ensuring the valuable data they contain is kept safe, and physical network segmentation can ensure networks can switch from online to offline seamlessly.”

Now that you’ve handled security and data privacy, you can find out how to lead the adoption of Gen AI in your enterprise (when half of all uptake is happening outside the IT department) — read more here.

The post Five best practices to protect your data privacy when implementing Gen AI appeared first on TechInformed.

Amidst the buzz of campaign slogans and policy pledges, TechInformed delves into the digital DNA of the main parties’ manifestos, unravelling how the Conservative, Green, Labour, Liberal Democrat, and Reform parties plan to code the future of Britain.

From AI investment to 5G rollouts, here’s a byte-sized breakdown of ten key areas where the major parties have promised to act and what each has pledged.

1. Digital Infrastructure

According to the Digital Catapult Digital Futures Index, the UK is one of the most advanced digital economies in the world, ranking first in Europe for advanced digital technologies. In fact, only the US and China are viewed as ahead of the UK. Key pledges span investments in mobile technology, data centres, and planning reform.

2. Broadband & 5G

According to regulator Ofcom, the UK ranks highly on the GSMA Mobile Connectivity Index, with almost 100% of the population covered by mobile broadband and 76% of the population covered by 5G. 24 million homes have access to gigabit broadband.

3. Tech Innovation & Regulation

All the major parties claim they want to position the UK as a leader in tech by creating an investment-friendly regulatory structure and backing research and development.

4. Tax

Tax has been a topic of significant debate during the election run-in. Rishi Sunak has pledged to cut taxes, although the UK tax burden is higher now than ever before, Labour claims. The PM has also accused rival Starmer of planning to raise taxes – though Labour has insisted it will not increase the tax burden on “working people”.

5. AI (Regulation / Investment)

AI is a hot topic, and Sunak seeks to position the UK at the forefront of the GenAI revolution. Last year, the PM held the world’s first AI Safety Summit at Bletchley Park.

6. Data

The manifestos focus on data safety, age verification, and promoting the use of data to boost growth.

7. Cybersecurity

Cyber threats to businesses are growing in both the number and complexity of attacks. Keeping the UK safe is no longer just a military consideration, with all major parties pledging to invest in cyber security.

8. Public Sector

The UK has a massive public sector that employs more than 500,000 full-time equivalent civil servants. Training in AI and other digital tools is seen as vital to improving efficiency within the sector.

9. NHS

The National Health Service is in crisis. The Covid-19 pandemic left waiting lists for NHS appointments at an all-time high, while an ageing population is increasing pressure on longer-term care. Recent cyber attacks against NHS partners has also sharpened focus. Can tech play a role in fixing the NHS?

10. Energy & Climate Change

The UK has pledged to be carbon neutral by 2050, but balancing the cost of adopting net zero policies with the recent cost of living crisis has led to a divide between the major parties.

The Green Party & Reform UK

While neither the Green Party nor the Reform Party are as detailed about tech policies in their manifestos, they each highlighted a few key areas relevant to technology and innovation. The SNP is also a major party, but we opted to focus on the major contenders because it and Plaid Cymru are not UK-wide parties.

Here’s a summary of the Green and Reform positions:

The post UK Election 2024: 10 Tech Commitments appeared first on TechInformed.

“Data centres are under pressure to become more energy-efficient than ever — AI-compute will continue to pile on the pressure. The GPUs that power AI at scale require radically different infrastructure from traditional CPU-powered compute. Many operators that want to embrace AI-compute must overhaul existing power, cooling, and cabling infrastructure.

“For many data centres, this entails expensive renovations and retrofits well before the site’s end of life. If operators want to avoid large-scale changes before a site’s planned end-of-life, they must adopt a holistic design approach that facilitates interchangeability in parts and site functionality.”

Niklas Lindqvist, Nordic general manager, Onnec 

“Nearly 90% of data in cloud data centres resides on hard drives. As data centre infrastructure approach natural refresh cycles, there is an opportunity to replace fleets of lower-capacity hard drives with higher capacity drives.

Today, the average hard drive capacity is 16TB, based on conventional PMR technology. Because it can physically store more bits on a single drive, new HAMR technology will enable data centre managers to populate those same slots with 30TB high-density drives, yielding significant power and space savings and massive TCO efficiencies — including CPU, RAM, and floor space.

“HAMR technology will also help data centre operators to further reduce their carbon footprint with more durable and energy efficient storage, playing an even more influential part in tackling the 53.6 million metric tons that make up the global e-waste mountain.”

BS Teh, chief commercial officer, Seagate Technology 

“Many organisations that are reliant on datacentres are reporting that their most pressing issue right now is one of capacity. A growing number of datacentres are full, and don’t have the space or power available to deploy new platforms.

“In 2024, this will result in widespread efforts to achieve efficiency gains, even on existing datacentre platforms, as this is the only way they will be able to reclaim space and power to accommodate the use of new technologies inside the datacentre.

“To optimise the sustainability of existing datacentre footprints, we’ll see operators looking to switch to new, more power efficient technology, with smaller space and cooling requirements. This is in essence extending the life of the datacentre – an essential factor when considering the need for new technologies in the wake of the rise of AI.”

Patrick Smith, Field CTO for EMEA, Pure Storage 

2: The rise of MLOps

“Demand for machine learning engineering roles has been steadily rising, with a more recent trend of hiring managers seeking machine learning operations (MLOps) engineers — professionals who can bridge the gap between data scientists and data engineers. By sitting within and working alongside both teams, these people purely focus on building, deploying and maintaining the various platforms and pipelines to facilitate the work of data scientists.

“Some companies are even going as far as building brand new MLOps teams, but it’s a very new area. Many companies haven’t yet developed that function, and where those people sit within the business is still quite undefined. The roles that we’ve placed have typically been working alongside a data science and a data engineering team.

“With an exponentially increasing recognition of the value of data and the development of a range of new tools, we’re likely to see many more types of organisations looking to hire ML specialists to leverage their data for the benefits of the business.”

Rosie O’Callaghan, business manager, Data Science and Machine Learning, Harnham

3: Businesses will rethink data architectures

“In 2023, organisations started to experiment with data lakehouse capabilities, but its true value hasn’t shone through. So, 2024 will be the year of the open data lakehouse whereby all the elements of an organisation’s tech stack start to distribute data between services more effectively. This will enable concrete, real world use cases – such as taking a point-in-time snapshot of data for financial compliance – as organisations start to take control of their data and treat it more like code.”

Chris Royles, EMEA field CTO at Cloudera 

“Organisations are going to break down silos between different data types with unified data storage — which combines all a company’s data types across on-premises and cloud environments—to fuel the new era of AI and analytics.

“As a result, we are going to see more organisations rethink their data architectures to consolidate data streams and treat them as a single source. Unified data storage will help make enterprise data more easily accessible and unlock hidden connections between different types of data.”

Matt Asay, VP of Developer Relations, MongoDB

4: Specialisation of job roles

“Data science as a specific subject of study has become increasingly prevalent in recent years. Numerous universities now offer data science master’s degrees and some of the country’s top universities such as UCL, Edinburgh and Kings College London now offer courses in machine learning, data science, and AI.

“Unsurprisingly, this is having a knock-on effect on the candidate make-up of the market. We’re seeing more people coming through with specialist degrees. Many may have chosen to complete a bachelor’s in a STEM subject such as maths or physics and have then taken a specialist master’s degree or PhD. For an employer, someone who holds a maths degree and a machine learning masters presents an ideal candidate.

“We’re likely to see even more specialisation as the role AI can play across the business domain becomes clearer and better understood by stakeholders.”

Rosie O’Callaghan, business manager, Harnham

To read this year’s AI predictions click here

The post 2024 trends in data storage and architecture appeared first on TechInformed.