The Multiverse blog

Hosted at Multiverse HQ in Paddington, the sold-out event drew a large and diverse crowd, predominantly SaaS Customer Success professionals keen on gaining insights and networking opportunities.

Our panel consisted of:
Eliza Cheyney - Manager, EMEA Commercial Customer Success, Salesloft
Sarah Patel - Head of Customer Success, BlueOptima
Amy Newbury - Head of Customer Success, Kleene
Nick Cornforth - Manager, Customer Success, Adobe

Moderating the panel was Theo Vadgama, Regional Director of Enterprise Customer Success at Multiverse, who also shared Multiverse’s perspective on the evolving role of Customer Success.

The event was kicked off by Jimmy Lee, Multiverse’s SVP of Revenue Operations. Jimmy has founded, scaled, and sold 3 Silicon Valley startups, and is a leading figure in the Customer Success and Revenue Operations space.

Jimmy began with a rousing overview of the industry’s current landscape. He highlighted the economic environment and the revenue pressures faced by many SaaS companies today, underscoring the increased importance of Customer Success (CS). As he pointed out, retaining existing customers is as important as acquiring new ones. According to Jimmy, the best CS teams now serve as high-touch, critical advisors who ensure renewals become seamless non-events.

Something everyone on the panel agreed with was the changing perception of CS. Traditionally seen as a support-only function, CS is now increasingly viewed as an essential part of the sales team, integrating with clients early on — right alongside Account Executives. This strategic alignment develops customer champions from the outset, eliminating stop-start handovers and enabling CS teams to have greater influence on customer satisfaction and retention.

When discussing enablement in the evolving landscape, the panelists were asked about the strategies they're employing to support their teams:

• Deeper discovery of customer pains: The panel emphasized the importance of a thorough and continuous understanding of customer challenges. It’s no longer sufficient to identify just one problem; teams must delve deeper to uncover multiple pain points and address them proactively.
• Reinforcing the CS-Sales relationship: Strengthening the partnership between CS and Sales was universally acknowledged as vital. The panelists discussed the need for consistent communication and collaboration, ensuring both teams work towards common goals to enhance the overall customer experience.
• Weekly risk reporting and forecasting: Regular risk assessment and forecasting have become crucial. Panelists stressed the discipline required to identify and attach risk factors as early as possible, enabling teams to manage potential issues before they escalate.
• Driving accountability with metrics: Establishing clear metrics and holding teams accountable to them was another key takeaway. Metrics provide a tangible way to measure performance and drive continuous improvement across CS teams.

Despite the event's focus on change and adaptation, the general sentiment among both the panelists and the audience was one of excitement. The conversation was overwhelmingly positive, with panel members expressing their enthusiasm for the evolving landscape. They noted the transformation within the profession highlighting the influx of diverse profiles, such as consultants, who are bringing fresh perspectives and skills.

The shift towards a more sales-oriented function and the accompanying increase in responsibility has empowered CS leaders and their teams. This empowerment allows them to deliver greater value and foster stronger accountability within their organizations. The panelists stressed CS is not merely a remedy for a poor product or an unsatisfactory sales experience. Instead, it plays a crucial role in enhancing already exceptional products and effective sales teams, making their work even more rewarding.

Ultimately, this optimism and positive momentum in the CS field suggest a promising future, positioning Customer Success professionals as key drivers of growth and customer satisfaction. If you want to join a CS team where the above is certainly true, Multiverse are hiring.

In its current form, the Apprenticeship Levy is a tax on UK employers, with funds exclusively earmarked for apprenticeships training. Labour’s goal is to broaden its use – creating more opportunities for adults in the UK to gain new skills.

There’s still a lot we don’t know about the future of the Growth and Skills Levy. But, to help employers unpack what a reformed Levy could mean for their business, here’s what we know so far:

What we know about the Growth and Skills Levy

The goal to reform the Apprenticeship Levy into the new Growth and Skills Levy sits at the heart of Labour’s mission to boost skills in the UK. As a key manifesto commitment, Labour plans to broaden flexible access to adult training in the hope that it will open up opportunities for growth across the workforce.

The intention of the reform is not to reduce the number of apprenticeships, but to increase flexibility. Eventually, the new Levy could allow businesses to spend some of their Levy contributions on non-apprenticeship training, with a portion still reserved for apprenticeships.

So far, the Government has not announced any non-apprenticeship training. Instead, they have announced new ‘Foundation Apprenticeships’. These are targeted at young people, with the goal of providing a broad curriculum and developing both employability and job-specific knowledge skills. The first seven foundation apprenticeships will be available from August 2025, with more likely to follow.

To support this change, employers will be asked to fund more of their Level 7 apprenticeships outside of the Levy. From January 2026, new Level 7 apprentices will only be eligible for levy funding if they are aged 16-21.

A new minimum duration for apprenticeships has also been announced. Apprenticeships can now be as short as 8 months, provided they still meet specific requirements. If you are interested in learning more about these changes, please reach out to a member of the team.

What is Skills England?

The Government has also created a new agency, ‘Skills England’, with the functions of the Institute for Apprenticeships and Technical Education (IfATE) transferring to Skills England in June 2025.

Skills England will develop a single picture of national and local skills requirements, bringing together businesses, providers, unions, Mayoral Combined Authorities (MCAs) and national government to assess the skills the economy needs.

Skills England will also shape the future of the Growth and Skills Levy, holding a list of approved qualifications and training that businesses will be able to spend Levy money on. The list will be developed in collaboration with businesses and experts.

What could this mean for employers?

Labour’s mission statement refers to the vital need for upskilling and training – alongside apprenticeships – to meet the needs of developing technology in the workplace.

Under the current system, Apprenticeship Levy-paying employers are only using 55.5% of available funds, on average.

By creating more flexibility over how the money is spent, the new Growth and Skills Levy could help some employers utilise a greater proportion of their Levy funds – with training that meets specific business needs and skills gaps. For example, it could provide an opportunity to level-up teams with shorter courses in technical skills, such as AI and data. These are vital areas that will be necessary for future business success and to maintain a competitive edge.

This isn’t just beneficial for employers. Employees also stand to benefit from increased investment in training opportunities – being empowered to learn new skills and feeling valued by their company. For employees, upskilling means opportunities to continuously learn and progress in their roles – which also helps improve retention. In fact, we see 94% of individuals remain at their employer beyond their Multiverse apprenticeship.

What do employers need to consider now?

The Growth and Skills Levy is a commitment from the Labour Party to upskilling employees. Fundamentally, the new policy should not change how employers should think about their investment in training: through the lens of increasing employees’ access to gain in-demand skills.

The Multiverse view

With careful implementation, new opportunities could be created for all workers across the economy – delivering ROI for employers and supporting a culture of work-based lifelong learning.

And while we don’t have all the answers just yet, the key to making a reformed Levy successful will be in making sure it's designed with the support and expertise of employers.

Read more about our perspective on the new Levy in our Skills Mission Report.
Want to speak to us about the Levy or other ways to support upskilling in your workplace? Get in touch.

Last updated: 12 June 2025

Prompt engineering involves giving precise and detailed instructions to generative AI tools to produce high-quality outputs. For example, you can use prompt engineering to help an AI tool generate a complex snippet of code or a detailed image. This process helps you get the desired output instead of vague or incorrect results.

Professionals in many industries use prompt engineering to obtain the best results from AI tools. But is prompt engineering a viable career path? This guide explores prompt engineering techniques, current career opportunities, and salary expectations.

Why does prompt engineering matter in the context of generative AI?

Why is prompt engineering important?

Let’s cover some basics. Generative AI models use natural language processing to interpret prompts – or inputs – from users. They then draw on vast databases to produce relevant outputs. But AI models don’t always generate the desired output, especially when asked to perform complex tasks.

Say you prompt ChatGPT to create a lesson plan for a college class on Indigenous novels. A poorly written prompt could cause the AI system to generate content that addresses younger students or uses culturally insensitive stereotypes.

Prompt engineering allows you to craft effective prompts that produce more accurate responses. It also reduces the amount of time you spend fact-checking and revising the outputs generated by AI systems.

What is prompt engineering used for? This discipline has applications in a broad range of professional and creative contexts. Here are a few use cases:

• Audio production - Video Game Developers and Video Editors can prompt generative artificial intelligence tools to create narration and sound effects.
• Code generation - Software Developers use prompt engineering to write code more efficiently. For example, a developer can input specific requirements into GitHub Copilot and receive relevant code snippets.
• Content creation - Prompt engineering allows professionals to generate tailored articles, emails, and other content. For instance, a marketer could ask ChatGPT to write an informative blog post with specific keywords.
• Data analysis - Data Analysts prompt large language models (LLMs) to interpret datasets and generate data visualisations. Generative AI services can also suggest new ways to analyse datasets, leading to fresh insights.
• Image generation - Prompt Engineers can ask text-to-image models to create custom images based on written descriptions. Companies use these generative AI services to create infographics, logos, and other marketing materials.

Examples of prompt engineering with specific tools

By way of example, here are two types of AI-generated outputs — one text and one visual — made by feeding prompts into popular generative AI models.

Prompt engineering with ChatGPT

ChatGPT is an AI chatbot powered by a large language model. It has many applications, from answering questions to writing resumes.

Suppose you want to use ChatGPT to write a jingle for your organic soap business. Here’s the output ChatGPT generates if you input this generic prompt: “Write a commercial jingle for my soap company.”

This output addresses the prompt but lacks humour and emotional appeal. It also doesn’t target a specific audience or reference identifiable products.

Crafting effective prompts lets you generate a more precise and tailored jingle. For example, you could break down the process of creating the jingle into intermediate steps, such as:

• Create a funny and upbeat jingle for Rachel’s Herbal Suds. Focus on the brand’s use of natural ingredients and commitment to sustainability.
• Address the jingle to eco-conscious women in their early twenties who care about gentle skincare and health.
• Include at least two soap puns.
• Limit the jingle to 15 lines or less.

Here’s the final answer when you input this prompt:

This output references the specific brand and includes phrases designed to appeal to the target audience, such as “Join the sudsy revolution.” Subsequent prompts could use different keywords to change the output or ask the AI to address the audience more subtly than "eco gals in your twenties."

Prompt engineering with Magic Media

Magic Media is a free text-to-image model that uses artificial intelligence to translate natural language into visual art.

Say you want to create an image of a cute dog with soap for your company’s marketing materials. Let’s start with a basic prompt: “Create an image of a dog with soap.” Magic Media generates this image:

This image satisfies the prompt but may not fit your desired aesthetic. It also doesn’t make the soap look appealing, so it’s not useful as a marketing image.

A Prompt Engineer can create a more detailed prompt, such as: “Create a marketing image of a cute poodle with soap. Make the soap look lavish and sudsy.”

In response, Magic Media generates these images:

These images depict adorable dogs with a variety of soap products. You could further engineer the prompt to change the background colour, type of soap, and art style.

How to get the most out of prompt engineering: Best practices

Because AI tools rely on natural language inputs, you don’t necessarily need a computer science degree to become a successful Prompt Engineer. Below, we detail some best practices that will help you create prompts to achieve your desired outcomes.

Define the objective

Identify your goal before you start creating prompts. Specific objectives will help you develop focused instructions to guide the AI system.

Here are a few examples of possible goals:

• Prompt the AI to create a dark, dramatic soundtrack for a movie trailer.
• Develop prompts for an AI tutor that explains complex physics concepts to students.
• Craft prompts for a legal AI to summarise Supreme Court decisions.

Provide relevant context

Unlike humans, AI technology doesn’t have previous experiences and contextual understanding. Prompt Engineers must provide relevant background information to get accurate answers. This context could include:

• Constraints
• Desired formats, such as charts or poems
• Expected length
• Industry-specific technical knowledge
• Relevant events
• Tone

Use clear and concise language

Large language models understand human text, but they can’t read the user’s mind. Avoid ambiguity by using concise and precise language.

For example, “Write a two-paragraph email explaining that a shoe order has been delayed due to weather” will generate a more detailed response than “Tell someone their order got delayed.”

Refine your prompt based on the AI’s response

Effective prompt engineering requires iterative refinement. Keep adjusting your prompts in response to the AI’s outputs until you get highly relevant responses. Changing even a single word can drastically alter the outcome, so never settle for the first response.

Experiment with different prompt engineering techniques

Prompt Engineers use many prompting techniques, including:

• Chain of thought prompting - Split complicated tasks into intermediate steps to help large language models engage in complex commonsense reasoning.
• Few shot prompting - Include specific examples to help generative AI models understand the type of response you want.
• Least to most prompting - The Prompt Engineer instructs the AI model to break a problem into subproblems and solve them in order.
• Zero shot prompting - Create simple AI prompts with minimal context.

Understand the limitations of the AI model

Researching the limits of the AI model’s ability will allow you to set realistic expectations. For instance, text-to-image models often generate nonsensical text that the Prompt Engineer must manually replace or erase.

Analyse the final product

Large language models and other AI systems can provide inaccurate or biassed information. Always check AI generated output carefully for errors and hallucinations.

Prompt engineering as a career

Prompt engineering is a relatively new discipline, but people with AI skills can pursue several career opportunities in this area. Here are two prompt engineering jobs:

Prompt Engineer

A Prompt Engineer uses natural language to create effective AI prompts. They also evaluate AI systems to identify technical issues and improve performance.

Prompt Engineers may work for creative companies to design AI images, videos, and other content. Other companies hiring Prompt Engineers include consulting and tech firms.

According to Glassdoor, the average base salary for Prompt Engineers in the UK is £52K. However, Prompt Engineers also average an additional £9k in bonuses and other non-salary compensation.

Prompt Engineers typically need these skills:

• In-depth understanding of artificial intelligence and machine learning principles, such as supervised and unsupervised learning
• Knowledge of prompting techniques
• Natural language processing skills, including sentiment analysis and text classification
• Proficiency in Python and other relevant programming languages

AI Security Engineer

An AI Security Engineer develops security measures to protect AI systems from cybersecurity threats. For example, they can mitigate prompt injection attacks by manually verifying outputs and filtering user inputs to block malicious prompts.

Glassdoor reports that AI Engineers earn an average salary of £51K, while Information Security Engineers earn nearly £62k on average.

AI Security Engineer roles require expertise in these areas:

• Cloud security principles
• Cybersecurity measures, such as encryption and vulnerability management
• Natural language processing and neural networks

Uplevel your AI skills with Multiverse

Prompt engineering is one of the newest career paths in artificial intelligence. The demand for these professionals will likely grow as more companies rely on generative AI to create content and drive innovation.

Multiverse’s free AI Jumpstart module allows apprentices to study prompt engineering, machine learning, and other AI skills while completing a related apprenticeship program. Apprentices also learn how to ethically use AI models in their current roles for free— all while receiving their regular salary.

Complete our easy application to learn more about how Multiverse can help you achieve your career goals in the tech industry and beyond.

The programmes aim to equip team members from various business functions with advanced, industry-relevant data capabilities.

Programmes will be delivered as apprenticeships, by the tech company Multiverse, and include the Data Fellowship, a 15-month level 4 apprenticeship programme delivering best-in-class training in data analysis. Learners will master data analysis techniques as well as data science, Python and an introduction to machine learning.

The Data & Insights for Business Decisions programme, meanwhile, is a 13-month level 3 apprenticeship course designed to impart both core technical skills required to transform data into insights and softer skills such as building narratives and presenting findings.

These programmes have been launched to equip Hoare Lea’s people from all areas of the business with the skills to make faster, data informed decisions, to create compelling stories using data visualisation and to drive transformative change across the business. The skills developed will benefit Hoare Lea’s clients by helping them to deliver high performance buildings, using data-driven methods to arrive at better outcomes whilst saving time on manual data processes. Through the training, Hoare Lea’s employees will develop the skills to analyse and present data to clients, to identify cost savings and efficiencies that deliver better projects.

Hoare Lea, part of the Tetra Tech High Performance Buildings Group, has pioneered the delivery of data training within the group, and will be joined by employees from technical services firm RPS, also a part of Tetra Tech.

Tom Collins, Digital Director at Hoare Lea, said: “Building a data culture is integral to how we make good decisions, design for net zero carbon, and plan for our future as a business. This training is enabling us to offer our people new skills, build our capabilities and, ultimately, make better use of data. Just as importantly, it gives our people access to some of the most in-demand skills in the workplace today, in turn equipping them to make use of data and AI to create a huge positive impact on building performance for our clients.”

Multiverse is a new tech-first institution that combines work and learning to unlock economic opportunity for everyone. It offers apprenticeships targeted in areas including software engineering and data analytics.

Noah Stevenson, Vice President GTM at Multiverse, said: "The effective use of data has the potential to radically transform organisations, and Hoare Lea have recognised this potential. Not only is this an investment in operational efficiency and net zero, but it’s also an investment in people: enriching the career trajectories of the team at Hoare Lea.”

Companies use the insights they gain from business analytics to create data-driven strategies. These approaches can improve customer satisfaction, operational efficiency, and profitability. Retailers, for example, can use data analytics to predict which products will sell fastest and optimise its supply chain management.

The people who wrangle data to create strategies are Business Data Analysts. Business Data Analysts and professionals with similar titles help companies make sense of data and apply insights strategically. This career path often appeals to people who enjoy solving problems and crunching numbers. But is it the right path for you? Below, we break down everything you need to know about becoming a Business Data Analyst, including responsibilities, skills, and salary. Let’s dive in.

What is business data analytics?

Business data analytics uses software and statistical techniques to interpret data and gain meaningful insights. This process allows organisations to understand their operations better and improve performance. Business analytics also assists with strategic planning and risk management.

Say, for example, a national restaurant brand wants to update its menu. Data analytics allows the company to interpret customer reviews and sales trends to determine which meals and ingredients perform best. Based on these insights, the restaurant can tailor its menu to satisfy customers and boost sales.

Understanding business data analysis

You may have already started to master some of the components of business data analytics. This process involves a few basic steps:

1. Ask a question - Start with a specific question or concern you want to address with data. For instance, you could explore customer trends to discover why your business's sales have declined.
2. Data collection - Identify relevant data sources and gather information. You could survey customers or use data mining techniques to harvest social media posts.
3. Data cleaning and processing - Organise the raw data into a usable format. This often involves transforming data by loading it into an environment optimised for analysis. You’ll also fill in missing data, remove inconsistencies, and correct errors.
4. Data analysis - Apply statistical techniques and software to reveal patterns and correlations in the data set.
5. Data visualisation - Use software to transform the data into easy-to-understand graphics. These visualisations may include charts, graphs, and maps.
6. Data interpretation - Study the results to extract meaningful insights. For instance, you might determine your target audience’s interests have changed, leading to a sales dip.
7. Communicate findings - Share your results with decision makers and recommend the next steps. In this scenario, you might advise developing new services that better align with your consumer base.

Real-world applications of business data analytics

Business data analytics has many practical applications across industries. Here are three case studies that illustrate the value and versatility of this approach.

Tracking Machine Health in Manufacturing

The manufacturer John Deere uses data analytics to improve its customer service and repair machines more effectively.

The company instals advanced telematics software in its construction machinery. The software collects data about different aspects of machine behaviour, including fault codes, fuel consumption, and idle time. This data gets streamed through the cloud to John Deere’s Machine Health Center in Iowa.

Local dealers use this data to diagnose machine problems remotely instead of travelling to construction sites or farms. They can select the necessary parts and repair tools to bring to the service appointment, saving time and reducing trips. Additionally, John Deere uses this information to identify and fix potential manufacturing errors.

These applications allow John Deere to improve its performance over time and provide more efficient service.

Improving patient care in healthcare

Data analytics allows Stanford Medicine Children’s Health to understand and improve the patient experience.

The organisation uses evaluation forms to collect data about patients’ experiences during their hospital stays. Analysts use AI tools to synthesise the information and reveal patterns, such as complaints about staff responsiveness and wait times.

According to Chief Analytics Officer Brendan Watkins, the organisation places these insights “directly into the hands of the folks who can make a difference, make systemic change with this data.” These stakeholders include healthcare providers who can use the information to deliver better patient care.

Boosting productivity in retail

The American grocery chain Kroger has developed two data-driven applications to improve employee productivity.

First, the company created a task management application for Night Crew Managers. This application displays each store’s inventory and merchandise deliveries in real time. It also uses data analytics to optimise employee to-do lists to help them restock stores efficiently.

Additionally, Kroger uses a store management application to streamline store audits. This tool also automatically recommends tasks for employees as they prepare for audits.

Both applications help Kroger associates adapt to changing store conditions and improve the customer experience.

The role of a Business Data Analyst

A Business Data Analyst uses data to solve business problems and identify growth opportunities. They also support decision makers by offering recommendations based on their findings.

The day-to-day responsibilities of these professionals vary by role but typically include these tasks:

• Collect data from a wide range of sources, such as customer feedback forms, financial records, or in-product data from a software application
• Develop databases to organise information
• Process raw data to prepare it for analysis
• Build and train machine learning (ML) models to analyse enormous data sets
• Design predictive models to forecast potential outcomes
• Create data visualisations
• Deliver presentations about their findings
• Collaborate with colleagues in marketing, sales, and other departments
• Learn about the latest advancements and trends in business data analytics

Business Data Analysts wield significant influence in their organisations. Leaders rely on their expertise for a broad range of business decisions, such as:

• Choosing marketing and sales tactics
• Deciding whether to invest in a new venture
• Managing financial resources
• Selecting prototypes to develop into new products
• Scheduling manufacturing equipment for maintenance and replacement

Because Business Data Analysts deliver considerable value, they can earn relatively competitive salaries. According to Glassdoor, the median salary for this career is £38800 per year in the United Kingdom.

Essential skills for Business Data Analysts

You’ll need the right technical and soft skills to thrive in a business data analytics role. If you’re interested in this career path, focus on developing these foundational abilities.

Technical skills

Business Data Analysts rely heavily on technology to interpret data. After all, you wouldn’t get very far if you had to analyse a spreadsheet with thousands of data points by hand. These technical skills will help you manage and process data effectively:

• Structured Query Language (SQL) - This language allows you to organise, manipulate, and search structured databases.
• Programming languages - Use R for exploratory data analysis and data visualisation. Python enables you to automate tasks, clean data, and build ML algorithms.
• Statistical analysis - Understand how to use statistical methods to interpret data. For example, descriptive analytics evaluates historical data to understand events and patterns. Prescriptive analytics uses past and present data to recommend future actions.
• Artificial intelligence (AI) and ML - Companies increasingly rely on AI and ML to analyse data and predict future trends. Study foundational ML concepts like clustering algorithms, decision trees, and linear regression. You should also know how to use ML frameworks and libraries like PyTorch and TensorFlow.
• Data visualisation - Transform data into accessible and visually appealing graphics. Popular data visualisation platforms include Microsoft Power BI, Tableau, and Zoho Analytics.
• Reporting - Use business intelligence tools like Qlikview and Sisense to create interactive dashboards and reports for stakeholders.

Soft skills

Data Analysts need strong interpersonal skills to excel in the workplace, including:

• Adaptability - Business analytics evolves quickly, so prepare to embrace new approaches and tools.
• Collaboration - Share ideas and responsibilities with team members from different backgrounds and departments.
• Communication - Express your ideas clearly in conversations, presentations, and written reports. You should also learn to translate complex technical concepts for lay audiences.
• Critical thinking - Evaluate the accuracy of data, identify potential biases in the results, and assess potential recommendations for feasibility.
• Negotiation - Collaborate with multiple stakeholders to develop solutions that meet everyone’s needs.
• Problem-solving - Learn how to approach problems from different angles and devise novel solutions.

Steps to becoming a Business Data Analyst

There’s no universal blueprint to becoming a Business Data Analyst. You can use many resources and strategies to gain the knowledge and skills required for this career. Here are a few common pathways.

Study at university

A university education is a traditional — but not required — educational pathway for Business Analysts. Many colleges and universities offer degrees in business analysis, data science, mathematics, and other relevant fields.

Enrolling in a business data analytics program offers several benefits. A structured curriculum gives you a solid foundation in data management, statistical analysis, and other necessary skills. You’ll also receive feedback and guidance from faculty.

But a university education has a few drawbacks. First, a three-year degree requires a significant investment of time and money. In England and Wales, full-time students pay £9,250 on average annually for tuition, books, and other expenses, according to the BBC. You’ll also need to dedicate extensive time to studying and attending classes. People with full-time jobs, families, and other obligations may struggle to balance their responsibilities with a college education.

Many universities also provide limited hands-on experience. A student reading business data analytics may learn foundational theories but not be able to apply these concepts in the real world. As a result, they may lack the experience and portfolio needed to land a position.

Obtain relevant certifications

Certifications enable you to develop your skills and showcase your abilities to potential employers. Here are a few relevant credentials that could help you prepare for data analytics roles:

• Entry Certificate in Business Analytics (ECBA) - The International Institute of Business Analysis offers this certificate for aspiring and entry-level data professionals. The certification demonstrates foundational competencies in business analysis planning, elicitation and collaboration, and other areas.
• Professional in Business Analysis (PMI-PBA) - The Project Management Institute designed this certification for Business Analysts who use data to support projects.
• Certified Foundation Level Business Analyst - The International Qualification Board for Business Analysis offers this foundational certification. It demonstrates proficiency in business modelling and creating business solutions.

Certifications cost much less than the average four-year degree and typically take less than a year to earn. They can accelerate your professional development and prove your commitment to the field to potential employers.

Upskilling

If you’re an established professional, you may already have many skills needed to succeed in business analytics. But everyone has areas for improvement. Thankfully, upskilling can fill any gaps in your knowledge — making you more productive at work and better prepared to advance in your career.

In fact, according to Gartner, 75% of employees who participate in upskilling programs agree it contributes to career progression.

Multiverse’s Advanced Data Fellowship is one of the most effective ways to level up your skills. This free program allows you to immerse yourself in the field of business analytics — all while working for your current employer.

The apprenticeship includes 23 immersive modules that teach you how to make data driven decisions and improve business processes. You’ll also learn data analysis and visualisation skills you can immediately apply in. This fusion of structured learning and hands-on experience will help you grow your career in business analysis.

Gain hands-on experience

Developing practical experience strengthens your skills and gives you a competitive advantage in the job market. Look for opportunities to apply your skills with real data sets.

For example, you could volunteer to analyse customer data for your current employer and recommend ways to improve marketing initiatives. You could also help clients solve business problems as a freelancer or consultant.

As you create projects, assemble them into a digital portfolio. Include a detailed description of each project and highlight their measurable outcomes. Potential employers can review your portfolio to gauge your experience level and skills.

If you’re looking for hand-on projects, Multiverse’s Applied Analytics Accelerator equips you to upskill your data chops while staying in your current role.

Take the next step in your data analytics journey with Multiverse

As a Business Analyst, you play a critical role in business decision making and strategic planning. Your insights can help companies develop cutting-edge innovations, improve customer experiences, reduce costs, and more.

Prepare for a career in this in-demand field with a Multiverse apprenticeship. Apprentices get paid to upskill and gain hands-on experience with real business analytics projects. They also receive one-on-one tailored to their goals.

Tell us about yourself by completing our quick application, and the Multiverse team will get in touch with the next steps.

We launched this tool to be there for apprentices whenever they have questions, with no delays: real-time, personalized, expert support delivered on-demand.

Atlas is our biggest step towards AI-enhanced learning, and the great news is that learners are using Atlas even more than we anticipated, and overwhelmingly finding it a helpful tool to understand course material, overcome challenges, and brainstorm ideas more efficiently. We're also building evidence that these technologies can be particularly useful for meeting the needs of some historically underserved communities, supporting more equitable access to high-quality training and education.

Atlas enabling scale

In a matter of months, 3,600 learners - that’s more than a quarter of our active learners - have asked more than 40,000 questions to Multiverse Atlas. With our bold ambitions to reach 100,000 learners before the end of the decade, it’s hugely important that we have confidence we can deliver personalized learning, at scale, and Atlas is enabling this.

While ensuring adoption was crucial, we wanted to make sure Multiverse Atlas provides genuine value and facilitates better learning outcomes, rather than driving superficial usage.

One of the measures we use to track this is a ‘response helpfulness score’ where we ask all users to say whether they find the responses provided by Atlas useful. The data showed consistently high helpfulness scores, slightly above 91%.

About half of the learners that message Atlas once will become frequent users: they are coming back again and again after seeing the value this on-demand coaching can offer.

Driving real business impact

While hard data is always going to be our guiding star, it’s qualitative feedback that really brings the impact of tools like Atlas to life.

Some highlighted the speed and accessibility as a big advantage. For example, one apprentice said they used Atlas to catch up after a hospital appointment which meant they had missed some sessions. Another said it helped keep them focused, as they could quickly locate answers without navigating through lots of different documents or waiting for a coach to reply.

Others emphasized the benefits of a flexible learning style. One person said they already liked to teach themselves by researching online, so this approach suited them very well. Atlas also allows learners to specify and personalize the type of responses they prefer. A number of users said they found this helpful as it felt harder to do with human coaches.

Ryan, a production controller at an aerospace company and an apprentice on our Data Literacy program, uses Atlas every day to solve functions and macros as he’s working with data. He said: “Whenever I’m stuck on something I can just pop it into Atlas and it works - it seems to be really good at interpreting what I need.

“I used to spend hours on YouTube following it step-by-step and I’d keep rewinding it back to the right bit so I can follow along. It was a really slow process and didn’t always work even then.”

These examples clearly demonstrate how Atlas is fulfilling our vision of blended, guided support that is delivering real impact for our learners and their employers.

Supporting apprentices at every age and every stage of their career

More than 50% of our learners are over the age of 30 on upskilling and reskilling programmes, and we know the benefit apprenticeships can have for those of any age. Any tool that we build cannot simply be there for so-called ‘digital native’ generations, it needs to be accessible and useful to everyone.

Contrary to assumptions that AI is grasped more intuitively by younger digital natives, Atlas has seen the highest adoption among apprentices over 40 years old. More than four in ten (46%) of learners in this age group have used it so far, compared to 31% of users aged 24 and under.

We think this is happening for two reasons. Our main hypothesis is that our younger apprentices are regularly using other AI tools (such as ChatGPT) already, as lots of research has found higher adoption among Gen Z and Millenials. For many of our apprentices over 40, Atlas may therefore be helping to familiarize them with gen-AI tools.

During user research interviews several older apprentices also said they liked the anonymity it provides and allowed them to ask questions they might have been embarrassed to ask otherwise. This is a good example of where Atlas can help remove perceived stigma around knowledge gaps.

The equity imperative

Research and media coverage scrutinizing the risks of AI bias, continually shows how these systems can accidentally reproduce and amplify societal prejudices and inequalities from the real world, while accessibility is often an after-thought. From the outset, we were determined Atlas would be different.

Atlas was designed with a big focus on accessibility - including by following AA level Web Content Accessibility Guidelines - to make it easy to use for those with a variety of disabilities. This included features like keyboard navigation and screen reader compatibility. Strong guardrails were also built into Atlas to reduce biases and ensure professional and contextually appropriate responses.

Atlas has seen slightly higher adoption rates among those with additional learning needs. 46% of learners with critical additional learning needs have adopted Atlas so far, compared to 37% with no additional needs. This clearly demonstrates the value of investing in accessibility.

We're also seeing equitable usage across ethnicities, with similar adoption levels for Asian (37%), Multi-racial (36%), and White (41%) apprentices. However, Black apprentice usage is lower at 32%. We believe this can be attributed to the higher Atlas take-up among our older apprenticeship cohorts, which are less ethnically diverse than younger groups. However, we’ll be investigating further to make sure there’s nothing else that could be contributing to the difference.

Gender adoption has been pretty much equal too, with 38% of male apprentices and 40% of female apprentices using Atlas to date. This is significant as a study carried about by the University of Chicago found that “women are about 20 percentage points less likely to use ChatGPT than men in the same occupation”.

A continuously improving, context-aware companion

While we’re encouraged by these initial results, we're just beginning our exploration of Atlas's potential as an AI-powered learning companion.

Our long-term vision is for Atlas to become an advanced, deeply personalized, and context-aware AI coach. Working alongside human coaches it should be capable of supporting apprentices at every step of their journey towards competency, mastery, and career success. We've already making progress and identified where we’ll focus next:

• Continue to closely track user experience data and gather in-depth feedback from apprentices and coaches. Their perspectives matter most in shaping Atlas's future development.
• Make it easier for apprentices to personalize and fine-tune Atlas to their own preferred learning styles through tailored settings and configurations.
• Enhance Atlas with advanced capabilities to provide hyper-contextual support mapped to each learner’s precise journey and progress through their apprenticeship program.
• Integrate Atlas more closely within the workflows of our human coaches teams with auto-suggested tailored content and guidance to facilitate more seamless hybrid human-AI instruction.

We're excited to build a future where everyone can access the personalized learning support they need to enable support upskilling and career growth - regardless of age, ethnicity, gender, wealth, or learning style.

Through AI powered tools like Atlas, we’re also able to do this at even greater scale, helping lots more companies to deliver continuous learning and close their skills gaps as they prepare for the AI-enabled future.

And just as we ask our apprentices to commit to their learning journeys, we'll continue our unwavering commitment to place equity at the heart of our approach to AI in education. We do this because it’s the right thing to do, but also because it’s needed if we want to close the large skill gaps that exist right across society.

As part of the NHS Long Term Workforce Plan, leaders across the country are seeking to modernise the organisation’s digital and data infrastructure. By making more effective use of data, they aim to better meet patient needs and offer a higher level of personalised care.

Since 2020, Multiverse has partnered with over 40 different NHS bodies – ranging from NHS trusts, to whole integrated care systems like Leeds Health and Care Academy – to launch NHS Data Academies, designed to embed data skills across the workforce, support NHS digital transformation strategies, and improve patient outcomes.

Equipping NHS employees with in-demand data skills

Through the NHS Data Academies, over 500 NHS employees have enrolled on Multiverse data upskilling programmes in the past three years. This includes personnel in clinical, operational, administrative, and IT-related roles – laying the necessary foundations to improve data literacy organisation-wide.

Employees are empowered to upskill, improve their data literacy, and develop digital skills across a range of learning pathways.

Each of the NHS Data Academies share a common set of goals:

• Improving operational efficiency by helping staff improve their confidence and efficiency in handling data. Across the NHS data academies, apprentices have tracked an average time saving of 6.4 hours per week from improving their data skills.
• Enabling data-driven decision-making by extracting actionable insights from complex data sets.
• Supporting improved patient outcomes by leveraging data to provide more personalised care.

During their programme, employees gain new skills through applied learning – using their new skills in real-time, on projects at the front lines of the NHS. It’s this combination that drives measurable impact.

Improving operational efficiencies and patient outcomes at North London Mental Health Partnership

When North London Mental Health Partnership (NLMHP) launched its Digital Academy with Multiverse, it took another stride towards a more efficient way of working. The initiative emphasises the intelligent use of data to drive improvements and efficiencies, by providing employee training to more than 100 staff across every division of NLMHP.

Sarah Wilkins, Chief Digital Information Officer at NLMHP, says, ‘‘Being part of this initiative means understanding of our data and insights will be embedded through the organisation, enabling us to enhance our services and improve patient and service user outcomes.

"Not only will it drive operational efficiency, but it will also serve as a stepping stone in our commitment to professional development for our staff."

With improved digital capabilities, NLMHP aims to help employees become more efficient across a wide range of workflows, including project management, analysis and forecasting. The training aims to make the Partnership more data-driven and productive, ultimately resulting in improvements to patient outcomes and services.

Driving digital transformation at the National Institute for Health and Care Excellence

The National Institute for Health and Care Excellence (NICE) helps practitioners and commissioners get the best care to people, fast, while ensuring value for the taxpayer. NICE does this by producing useful and usable guidance for health and care practitioners.

NICE had access to plenty of data to help inform their people and services, but wanted to leverage it more effectively, to boost productivity.

Elena Doyle, Associate Director of Data Management at NICE, says, "NICE is on a transformation journey to ensure we’re meeting the changing needs of our nation’s health and care systems. Improving the data and digital skills of our people is an essential part of this transformation.

“That’s why in June 2023, we launched our Data Academy, so we can deliver even more relevant, timely, usable and impactful guidance for our partners.”

At the start of the partnership, Multiverse conducted a skills gap analysis across each division at NICE, which identified the target areas that would see the biggest impact from an upskilling programme.

“To date, 10% of our workforce has joined the Academy, with every directorate represented. This is so we can reach a transformational tipping point,” says Elena.

“We have seen significant productivity improvements, growth in awareness of how to leverage data the right way, as well as better use of tools we have invested in like Power BI.”

Unlock digital transformation with employee upskilling

To learn more about how Multiverse can support your own digital transformation goals through upskilling, get in touch.

What is MVLA?

The Multiverse Leadership Accelerator (MVLA) is a 12-month leadership programme, sponsored by our Founder & CEO Euan, that offers mentorship, sponsorship and coaching to employees on their leadership journey at Multiverse. To be eligible for the programme, employees must meet certain demographic criteria, so that we are developing employees from backgrounds currently underrepresented within our leadership team.

The MVLA was built as part of our strategic approach to creating a more diverse senior leadership team. This initiative is a testament to our belief in the power of diversity to drive innovation, creativity, and success. MVLA fits into our Career mobility strategy as a “Grow” initiative; implementing the infrastructure to foster career mobility.

The story so far:

Now 2 cohorts in, the programme has already begun to yield tangible results. Results that have not only transformed participants' professional trajectories but have also enhanced Multiverse’s culture.

So far we’ve seen:

• Progression: Promotion rates above the company average
• Retention: Attrition rate lower than company average
• Satisfaction: High programme satisfaction scores

Participants have spoken to the power of group coaching, applied learning opportunities, and one-on-one mentoring offered throughout the programme as game changers to their careers, unlocking insight, enhanced skills and added confidence; skills that participants are now passing onto their direct reports and peers.

More than just a programme:

This success is only the beginning. As we continue to support and develop underrepresented individuals through MVLA, we look forward to sharing more successes, more breakthroughs, and more impact. The MVLA is not just about creating leaders; it's about redefining what leadership looks like and ensuring that the path to the top is open to all.

We're inspired by the progress and eager to see what the future holds for MVLA and our other career mobility initiatives planned for FY25.

If you're inspired by our commitment to growth and diversity and wish to be a part of a company that invests genuinely in its people, we would love to have you on board. See open roles.

By 2028, the cloud computing market is expected to reach a record value of $1.24 trillion. As a result, the demand for highly skilled software engineers, capable of developing advanced cloud-based solutions, is set to rise.

Building a software team capable of driving innovation through emerging technologies is a top priority for leaders. But this mission presents talent acquisition challenges – externally hiring and retaining mid-senior level software engineers is a time and capital-consuming process.

Keep reading to learn how upskilling your junior tech workforce can help your organisation to accelerate innovation, unlock productivity, and reduce costs.

We’ll explore some of the key obstacles facing leaders today, and discuss some of the transformational benefits you can expect from building a high-performing software engineering team from within your ranks.

What are the common barriers to building high-performing software engineering teams?

The software engineering job market has seen a turbulent few years, cycling between talent shortages and layoffs. But throughout this, hiring for mid-senior level talent has remained a slow and often expensive process, leaving teams short of crucial expertise.

Leaders want to fill these skills gaps, but they face several barriers to creating high-performing software engineering teams. These include:

• A lack of emerging skills: Software engineers today need the skills to capitalise on emerging new technologies like AI and cloud. With skills gaps in the workforce, many companies struggle to innovate in these fast-moving domains.
• Heavy reliance on junior talent: As we often hear from Multiverse customers, hiring junior software engineering talent is easier. As a result, development teams are often made up of less experienced developers, requiring more time from senior software engineers to provide mentoring and Quality Assurance (QA).
• High demand and limited availability for mid-senior engineers: There’s currently a lack of long-term solutions to the shortage of mid-senior talent, presenting an ongoing challenge for companies, and resulting in a small, overstretched talent pool. Burned out software engineers can struggle to feel motivated and engaged with their progression, leading to retention issues.

Overall, these challenges can result in a productivity decline, a slow speed of innovation, and mounting costs for hiring and retaining skilled developers.

The solution: Upskilling your software engineers

It’s clear that technology and engineering leaders need a new approach to navigate these challenges.

That’s why as demand for skills increases, many organisations are divesting from hiring software engineers externally to upskilling and reskilling their existing workforce. With our data suggesting that 69% of businesses will need different workforce skills to stay competitive by 2030, this comes at an opportune time.

By providing junior software engineers with opportunities to learn, improve their skills, and apply their new training on the job, leaders can increase mid-senior level engineering capacity and address challenges head-on.

The business benefits of a high-performing software engineering team

Upskilling junior software engineers presents an opportunity to deliver financial, talent, and business benefits. These are some of the top advantages:

1. Increased productivity

Upskilling can deliver measurable productivity benefits by increasing the overall capacity of your software engineering team – without adding to headcount.

Offering additional, advanced on-the-job training opportunities can reduce the time it takes to ramp junior talent up to mid-senior productivity levels, as they gain expertise across many engineering disciplines, including cybersecurity, software development, and testing principles.

In turn, output volume and accuracy are increased, with less intervention required from senior engineers, who can focus their time on more complex or strategic priorities.

2. Faster speed of innovation

Upskilling your software development team empowers them to leverage new technologies and drive digital transformation. For example, many programmes explore emerging business applications for cloud, data strategy, and AI.

Equipping your staff with the skills to meet today’s demands paves the way for technology-powered business change in the years to come.

The culture of innovation established with upskilling can also help bring products to market faster, and drive efficiencies through new ways of working.

3. Reduced costs

Our data shows that 76% of leaders plan to increase their spending in upskilling – and it’s an investment that can provide impressive returns. By filling in-demand mid-senior level roles through levelling up existing employees, you can reduce hiring costs that would have been required to source talent externally.

Investing in your employees’ professional development also has proven benefits for retention, saving further costs through reduced attrition. When employers provide opportunities to learn new skills and advance their careers, employees feel more motivated and engaged at work – 93% of leaders report improvements in workforce retention and resilience after launching upskilling initiatives.

Understand the skills gaps in your software engineering team

Upskilling is the key to building high-performing software teams, offering substantial benefits to organisations striving to deliver ambitious software engineering strategies.

It’s particularly useful for teams feeling the pinch on mid-senior level developer capacity, helping transform more junior members of staff into experienced engineers.

What are the key areas to upskill your software engineering teams?

In our eBook, "How to build high-performing software engineering teams", we explore four essential areas to upskill your existing employees and unlock full potential.

Want to learn more? Explore our upskilling courses for software developers.

ERGs are groups created for employees and led by people that share common life experiences or identities. Typically, these groups are comprised of people within communities that have been historically and systemically marginalised.

ERGs provide support and safe space for underrepresented and/or marginalised communities. They help employees in their professional development, increase engagement and retention, and drive towards organisation-wide goals. LGBTQ+ @ Multiverse is a community for those who identify as lesbian, gay, bisexual, trans or queer as well as nonbinary or anyone who identifies as intersex, pansexual or asexual, as well as allies to this community. Currently 12% of our workforce self-identify as being part of the LGBTQ+ community.

We sat down with Julia Paolillo, a Project Manager based in our New York office and the new LGBTQ+ @ Multiverse Chair, to talk about her new role.

What inspired you to run for Chair of LGBTQ+ @ Multiverse?

Multiverse is the first place I’ve ever worked that has ERGs, and one of the first workplaces where I’ve been fully out as a member of the LGBTQ+ community. The sense of belonging that I felt as a member of the LGBTQ+ @ Multiverse was so unique and compelling - the perfect mixture of learning, advocacy, and plain old fun. I knew I wanted to do my part in helping the community grow and flourish - and here I am!

What is the main goal of your ERG?

Our mission is to create an inclusive space for all LGBTQ+ Multiversers to create community, and for allies to learn about our community. We uphold this work with three pillars:

• 📣 Awareness - Sharing the richness of our community with others at Multiverse.
• 🌈 Inclusion - Creating a space that is inclusive of the many different identities under the LGBTQIA+ umbrella... no small feat!
• 🪩 Celebration - Creating both informal and formal spaces in which we can enjoy our culture.

Why do you think the ERG is important?

As a member of the LGBTQ+ community, I've loved the experience of learning and expanding my mindsets and definitions during the 10 years that I've been publicly "out" (🎉). Constant growth and learning are often hallmarks of the ever-evolving queer community, but far too often the LGBTQ+ world exists in siloes.

LGBTQ+ @ Multiverse can help us break down those siloes, and bring together lesbian women, gay men, bisexual people, trans people, and non-binary or gender nonconforming folks into a space where we learn from each other, support each other in the workplace, and share our unique experiences navigating life and work as members of this community. Additionally, I hope that we can create a truly supportive internal community at Multiverse, I believe there’s also work to be done in promoting awareness about our group to allies within the company.

What are the big plans for the ERG this year?

We’re kicking off with a bang for Pride Month in June! We are co-hosting a networking event in London with the PRISM Network, an apprentice-led group committed to creating space for LGBTQ+ Multiverse Apprentices, past and present.

For the rest of the year, we plan to organise volunteer days across the UK and US, to host external speakers and come together with the other Multiverse ERGs to practice what we preach: that we are stronger and better together.

If our commitment to diversity inspires you and you wish to be a part of a company that invests genuinely in its people, we would love to have you on board. See open roles.