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As technology advances, though, professionals in the tech sector are required to continuously develop their skills to remain competitive. For them, reskilling and upskilling should be top of mind.

But there’s also general confusion over the differences between upskilling and reskilling. If you’re looking to accelerate your career, you might wonder whether it makes more sense to reskill, upskill, or a combination of the two.

This article will help clear up some of the confusion over these distinct concepts. Below, we'll cover:

• What does it mean to reskill or upskill?
• Should I reskill or upskill to advance my career?
• The best ways to upskill in 2024

Let’s dive in.

What does it mean to reskill?

To reskill, a professional generally must develop new capabilities and specialised knowledge outside their existing skill set.

Reskillers come from all ages, specialisms, and industries. But unlike upskillers, reskillers aim to switch careers entirely.

For example, let’s say you've worked in sales and business development for five years. You’ve developed skills related to this career path, such as those based on customer-facing communications and outreach.

You recently learned, though, that the average data analyst salary is higher than your current pay. To become a data analyst, you’d need to develop a new core skill set. So, you retrain through a technical programme outside of working hours and switch from a sales specialist to a junior data analyst.

What is upskilling?

Upskilling is when a professional improves or develops the core job skills or abilities related to their existing career path.

Upskillers come from all ages and industries. But they share a desire to progress in their current field or industry to propel career growth, rather than switch careers entirely.

To illustrate an example: Let’s say you've been a generalist Software Engineer for three years. You enjoy the work, but you'd like to specialise in cyber security, as that skill set is in demand. You upskill and progress from Software Engineer to Cyber Security Engineer.

Should I reskill or upskill to advance my career?

It’s natural for working adults to question whether it makes greater sense to reskill or upskill to advance their careers.

Both upskilling and reskilling offer unique opportunities and benefits to professionals (more on this below). But to answer the question of which is right for you, it’s best to start from your unique, long-term goals and work backwards.

Here are some questions to ask:

• Do I enjoy the work I currently do? Does it feel meaningful to me?
• Does my current career path align help me achieve my long-term goals, be them personal, professional, or financial?
• Are there options to develop or expand upon my current skill set for greater success in my industry?
• Will demand for my current skills continue to grow in the future? Or could I do more to future-proof my career path?

While a simplification, the above questions can be a useful starting point for further exploration. But before we go any further, let’s examine some of the benefits of upskilling or reskilling for your career.

The benefits of reskilling or upskilling for employees

Workforce training benefits more than just HR Managers and business owners. Here are six ways reskilling or upskilling can also help you as an employee.

1. Increased salary and responsibilities

Data shows that upskilling or reskilling can lead to new job responsibilities, career advancement, and potentially higher earnings. In fact, 69% of apprentices who completed one of our upskilling programs have gained new job responsibilities, while 75% of employees who participated in upskilling have experienced career advancement. Additionally, over 5% of those who upskilled have seen an increase in average earnings compared to those who didn't.

While you're looking to progress in your current role or start a new career path, upskilling or reskilling is a solid business case for achieving your goals. You can learn the abilities needed for new and potentially more complex tasks in your existing role by upskilling. On the other hand, reskilling can provide you with the skills needed to start a new career with more responsibilities and higher earning potential.

2. Greater job security in 2024 and beyond

OpenAI released a demo of ChatGPT in late 2022. By early 2023, demand for new professions, like Prompt Engineers, had emerged. A full 69% of business leaders predict AI will create more demand for professionals with this specialism.

AI has shown us how quickly tech advances can change the way we work. Now is the time to learn the skills of the future and bring innovative approaches to your role. Doing so will increase your job security in the present, and it will also give you the new skills you need to adapt to future tech changes in the workplace.

3. Fewer education gaps

You don't need a new degree-level qualification to advance your career. For some careers, it helps. But there are other factors to consider.

Putting your career on pause to attend university is expensive and a significant time commitment. There's also no guarantee of a job after graduation. The pace of technological change, especially in the tech industry, means new graduates often emerge from university with skills gaps.

Fortunately, you have options. Multiverse offers learners the ability to earn a degree qualification through an upskilling programme without leaving your current role. You'll also earn a nationally recognised certification while working on real projects, meaning no career breaks or learning endless theory.

Check out our programme options for more information.

4. Enhanced productivity and problem-solving

More and more businesses see the value of employees investing time into their skills. One study showed that 93% of CEOs introducing advanced upskilling programmes see increased workforce productivity and fewer skills gaps or mismatches. A further 94% see greater innovation and accelerated digital transformation. Additionally, 95% see better employee engagement. But when your employer upskills or reskills the workforce, what does that mean for you as an employee?

Whether you're at the start of or mid-way through your career, evolving your skills will help you solve advanced problems. Plus, when your employer invests in you, you feel more valued and engaged. Being empowered to solve problems and feeling more engaged at work makes you more productive. You'll also be less likely to switch jobs, which is an indicator of greater job satisfaction.

5. Better work-life balance

Regardless of your position on the work-from-home debate, fully remote or hybrid working opportunities may bring better work-life balance. Still, that all depends on your preferences or commitments.

Some professionals prefer a clear separation of work and home, in which case an in-office role might be best. Others may prefer the option to eliminate the office commute so they can spend that time with their families.

In either case, learning in-demand tech skills lets you choose your preferred workplace setup. That's especially true of remote work, as many tech professionals can do their work from anywhere.

6. Increased confidence in the workplace

Experts predict that 85% of all job roles in 2030 have yet to be invented. Even as we speak, 90% of workers see the need to update their skills annually at a minimum.

So, if you want to thrive, rather than survive, in the future of work, now is the time to learn transformational skills.

With your new skills, you'll gain confidence in your abilities. That confidence will put you in the driver's seat of your career and help you meet current and future workforce demands. You'll also have the knowledge to effectively lobby for new tools, processes, and workflows that bring actual business results.

Best ways to upskill or reskill in 2024

Now you know why you should upskill or reskill, here are three ways to get started.

Take courses through learning platforms

Platforms like Coursera offer flexibility and the ability to use any professional development budget you have through work. Still, you might have to pay for them if your employer doesn't offer professional development. You may also have to dedicate time outside work to learning the course content. There could also be no support from industry experts or any actionable job-ready skills.

Take a full-time or part-time boot camp

Full-time and part-time boot camps give you the chance to upskill and reskill. If your employer offers a professional development budget, you could use this to pay for the boot camp. But even if your employer funds it, you still have to invest time—aka, use your own time outside of work to study. If you go full-time, you'll have to put your career on pause for course outcomes that might not suit your goals.

Pursue an on-the-job upskilling programme

Like online courses and boot camps, on-the-job upskilling programmes can allow you to upskill in the areas needed to advance your career. But unlike online classes and boot camps, you don't need to quit your job, self-fund the programme, or spend personal time completing the course.

Upskill for free with Multiverse without quitting your job

With a Multiverse programme, you can upskill on the job for free. There's also a focus on applied learning so you won't be stuck in a classroom reading endless theories. You'll get paid to learn and use your new abilities in your day-to-day role.

We tailor our programmes to give you durable and future skills, setting you up for success beyond the present. Plus, you'll learn from experienced professionals who have been where you are.

Fill out our quick application to get started today Our team can then double-check your eligibility and discuss apprenticeship options with you — including how to upskill with your current employer.

The AI for Business Value programme apprenticeship aims to equip team members from business functions across the organisation with advanced, industry-relevant AI and machine learning capabilities. Delivered by the tech-first company Multiverse over a 13-month period, the AI for Business Value programme trains apprentices to identify business value gains that can be achieved through using AI and executing ethical AI projects.

The training is part of The Crown Estate’s ambition to better utilise AI to drive value for the nation. A significant national landowner, The Crown Estate delivers financial value to the Treasury and creates wider environmental, social and economic value for England, Wales and Northern Ireland through its diverse £16bn portfolio that includes urban centres and development opportunities; one of the largest rural holdings in the country; and Windsor Great Park. It also manages the seabed and much of the coastline around the same parts of the UK, playing a major role in the country’s world leading offshore wind sector.

Participants on the AI for Business Value programme will be embedded across the organisation, delivering AI innovation and utilising real-time data in areas ranging from administration and property management through to urban design and marine planning.

The programme will enable teams to identify new opportunities to improve operational and design efficiencies, reduce emissions and carry out optimum planning, among other strategic outcomes, supporting The Crown Estate’s objectives on net-zero, economic growth and productivity and stewarding the UK’s natural environment and biodiversity.

With businesses predicting that almost half (44%) of workers’ core skills will be disrupted by 2027, with AI widely reported to be a key disrupter, the training will also enhance the careers of apprentices who are enrolled, empowering them with the most in-demand skills in the economy today, and allowing them to act as champions for AI within the organisation.

Aruj Haider, Head of Digital Emerging Technology & Innovation, The Crown Estate, said: “AI represents an enormous opportunity for us, that will increase our ability to effectively serve the country and give a huge boost to our efficiency and capability to achieve net zero and grow our productivity. This training will unlock our team’s ability to define the role AI will play in our organisational journey. Just as importantly, it gives our people access to some of the most in-demand skills in the workplace today.

“At The Crown Estate, our AI strategy is geared towards developing the skills, mindsets and behaviours to use AI effectively within our organisation. This apprenticeship is a key marker on that journey, and I really look forward to seeing some of the plans and impacts participants make on this programme over the next 13 months.”

Multiverse is a new tech-first institution that combines work and learning to unlock economic opportunity for everyone. It works with more than 1,500 organisations to close critical skill gaps in the workforce in AI, data and tech, through a new kind of apprenticeship.

Gary Eimerman, Chief Learning Officer, Multiverse said: "The Crown Estate has recognised the potential of AI to upscale innovation and increase efficiencies across its portfolio. The skills development provided is empowering the Crown Estate team members to use emerging technology to achieve their aims; while also enriching their career opportunities.”

According to the World Economic Forum’s latest Future of Jobs report, almost two-thirds of companies globally predict AI will grow in importance, with AI and big data the number one priority for company training strategies with more than 50,000 employees. It’s becoming ever more critical for employers to heed the advent of AI, both to harness the numerous benefits it can yield for their business and to prepare their people for the future of work.

U.K. professionals can use many resources to gain in-demand skills and advance their careers. For example, Multiverse’s tuition-free upskilling programs combine structured curricula with applied learning in the workplace. Upskillers can study AI, data analytics, transformative leadership, and other future-proof fields. Other effective strategies for upskilling include taking online courses and volunteering.

This guide explores the best ways to upskill in 2024. We’ll also highlight critical skills and emerging career opportunities to accelerate your professional journey.

Upskilling defined: Hard skills vs soft skills

Upskilling is a way to strengthen your core skill set and gain new knowledge related to your current career path. This process involves learning new best practices, methods, and technologies to adapt to industry changes.

People often confuse upskilling and reskilling, but they’re different processes. Upskillers expand their existing expertise to take on more advanced responsibilities and roles in their current fields. By contrast, reskillers learn new skills to pivot into different career paths or industries.

Upskillers typically focus on enriching two types of competencies: hard and soft skills.

Hard skills

Hard skills are the technical skills and knowledge needed to handle job-specific responsibilities and tasks. For example, Software Engineers must know how to use programming languages to write code for applications, while Data Scientists need a strong understanding of statistics to analyse datasets effectively.

The demand for hard skills has skyrocketed as industries increasingly become tech- and data-driven. However, many employers struggle to find employees with the right training. The British Chamber of Commerce’s June 2024 Business Barometer found that 62% of U.K. businesses face a skills shortage.

Upskillers can help meet this critical need by learning in-demand technical competencies and tools. According to the Robert Half 2024 Salary Guide, the most sought-after technical skills in the UK labour market include:

• Cloud computing
• Data analytics
• Data management
• Financial modelling
• Marketing automation
• Programming languages
• Software engineering

Soft skills

Soft skills are behaviours and interpersonal abilities that enable professionals to build relationships and navigate complex workplace dynamics. Workers use these skills to collaborate, lead teams, and resolve conflicts effectively.

A 2024 LinkedIn survey shows that 90% of U.K. employers value soft skills more than educational qualifications. Here are a few of the most in-demand soft skills:

• Communication
• Customer service
• Management
• Leadership
• Teamwork
• Project management
• Problem-solving

These universal skills transfer across many industries and roles. For instance, Product Managers and Marketers both need strong communication skills to coordinate cross-departmental projects.

How to assess your hard and soft skills

Evaluate your existing skill set to understand your strengths and identify opportunities for upskilling.

Start by self-reflecting on your current abilities and areas of expertise. What skills have you used in your current and past roles? What areas do you excel in, and where would you like to improve?

As you answer these questions, create a list of your hard and soft skills. Consider rating each skill as “Advanced,” “Proficient,” “Developing,” or “Needs Improvement.” This exercise allows you to assess your current abilities and detect areas where you could build your expertise.

Next, ask trusted colleagues and mentors for feedback on your performance and skills. Invite them to share their observations about your strengths and areas for growth. These conversations can help you evaluate your abilities objectively and gain fresh insights into the skills that matter most in your field.

Finally, consult job profiles on LinkedIn, Indeed, and other career boards to learn about the necessary qualifications and skills for your career path. Compare these competencies to your existing skill set to identify gaps and focus on these areas during your upskilling journey.

Upskilling and the jobs of the future

Professionals have always needed to upskill throughout their careers, but recent technological advancements have made this process more critical than ever.

Businesses across industries have increasingly embraced AI, automation, data science, and other innovations. This shift has profoundly disrupted the U.K. job market, with many traditional roles evolving or becoming obsolete. The Institute for Public Policy Research predicts that AI could affect the jobs of up to 8 million U.K. professionals in the next few years.

Upskillers can prepare for this shift by strengthening their digital fluency. Employers in all sectors seek candidates with proficiency in AI, data analysis, and emerging technologies, even outside traditional tech roles. For example, Marketers can use AI to generate content and automatically send emails. Developing these skills will help you drive innovation in your organisation and increase your marketability.

Upskilling can also unlock exciting career opportunities in emerging fields. According to a 2024 LinkedIn report, the fastest-growing jobs in the U.K. include:

• Sustainability Manager
• Sales Development Representative
• Underwriting Analyst
• Chief Revenue Officer
• Dental Therapist
• Home Health Aide
• Artificial Intelligence Engineer

Many of these roles require cutting-edge technical skills. For instance, Sustainability Managers must analyse environmental data, while Artificial Intelligence Engineers need proficiency in deep learning and machine learning algorithms. Continuous upskilling will enable you to future-proof your career and pursue leadership roles in these high-growth fields.

How to upskill in 2024

U.K. professionals can take advantage of many upskilling resources, from short YouTube tutorials to 18-month apprenticeships. The most effective upskilling opportunities focus on career-ready skills and allow you to practise applying your knowledge in real-world scenarios.

Digital learning and flexible courses

Online courses and certifications allow upskillers to acquire hard skills without the financial commitment of attending university. Platforms like Coursera and LinkedIn Learning offer online courses on various skills, such as data visualisation, prompt engineering, and user experience (UX) design.

Digital learning allows students to study countless topics at their own pace without commuting to campus. This flexibility and versatility make this approach ideal for busy professionals.

Volunteering and practical experience

Volunteering allows you to gain valuable hands-on experience while giving back to your community. Look for opportunities that enable you to gain relevant hard and soft skills as you contribute to meaningful causes.

For example, you could strengthen your JavaScript skills by developing a website for a local homeless shelter. Similarly, you might learn digital marketing by running social media campaigns for a food bank. These activities can boost your career progression and help you build a portfolio to show potential employers.

Upskilling on the job

Multiverse’s upskilling programmes allows professionals to gain new skills while working at their current roles. Upskillers expand their knowledge by attending live workshops, collaborating with peers, and completing virtual modules. They also gain hands-on experience by applying their newfound skills in their current roles.

Multiverse learners develop in-demand AI, business, data, and tech skills. For example, one apprentice used the tech skills he gained during his apprenticeship to launch a charity platform and secure a Project Manager job. Meanwhile, Jeffrey Owusu was promoted to a senior management role during his apprenticeship and helped his colleagues improve their productivity. These case studies demonstrate how Multiverse empowers professionals to make valuable impacts in their organisations and advance their careers.

Creating a SMART career plan

Follow the SMART framework as you shape your career development plan. This roadmap should include these elements:

• Specific: Set clear, short-term goals for consistent growth.
• Measurable: Identify specific metrics and milestones to track your progress.
• Achievable: Create a realistic plan that won’t interfere with your existing job, family responsibilities, and other commitments.
• Relevant: Choose upskilling activities that directly contribute to your career goals.
• Time-bound: Establish fair deadlines to meet milestones and goals.

Update your resume or CV frequently as you complete learning experiences and projects. This practice will allow you to track your skill development and stay prepared for unexpected job openings.

Why Multiverse is the best way to upskill in 2024

A Multiverse apprenticeship is the most effective way to upskill and future-proof your career. Our innovative programs combine practical experience with personalised coaching and industry-recognized certifications.

Apprenticeships receive hands-on training in high-demand fields, including:

• Advanced Software Engineering - Deepen your knowledge of cloud computing, data engineering, machine learning, and other advanced topics.
• Data Fellowship - Strengthen your data analysis and visualisation skills.
• AI for Business Value - Apprentices learn how to leverage AI responsibly to drive business outcomes and accelerate their careers in the workforce of tomorrow.

These flexible learning pathways allow professionals to upskill without sacrificing their current job or work-life balance. Depending on the programme, apprentices often receive several hours of protected learning time each week and apply their skills by completing relevant projects for their employers. They also get personalised coaching and mentoring to support their professional development.

A Multiverse apprenticeship can lead to rapid career growth. One in three learners get promoted during their apprenticeship or within six months of completion. Additionally, 52% of learners saw a salary increase since starting the program.

Accelerate your professional growth

Upskilling has become a necessity in 2024 as industries race to adapt to emerging technologies and changing market demands. Professionals must continuously expand their hard and soft skills to meet new challenges and stay relevant.

U.K. workers can pursue many upskilling opportunities, including online courses, certifications, and volunteering. Consider Multiverse’s upskiller programs if you’re looking for a structured, career-advancing upskilling experience – with no cost to participants. Apply today to get started.

In the U.K., 21% of businesses analyse digitised data to gain new insights, and this percentage will continue to rise as more companies embrace data-driven decision-making. To streamline data analysis, many businesses turn to Microsoft Power BI. This advanced business intelligence tool synthesises data from various sources and extracts actionable insights.

Despite the growing reliance on Power BI, many employees lack the knowledge to use the tool effectively. The Multiverse Skills Intelligence Report 2024 found that 55% of professionals have no Power BI skills. This skills gap can affect productivity and prevent businesses from getting the most out of their data.

Learning Power BI will help you gain in-demand data analysis skills and open new career opportunities. This guide offers an in-depth overview of Power BI, its functions, and practical steps to start using this versatile tool.

What is PowerBI?

Power BI stands for business intelligence and refers to several Microsoft products and services designed for data analytics and visualisation. It lets users gather raw data from multiple sources and turn it into interactive dashboards and reports. Professionals can use this business analytics solution to gain actionable insights from almost any data type and make strategic decisions.

Say, for instance, a retailer sells perishable products and wants to reduce the amount of waste. They can use Power BI to analyse current stock levels, customer preferences, and sales trends. The platform helps identify slow-moving products and forecast demand. Based on these insights, the retailer can optimise its inventory orders to minimise waste while meeting customer needs.

Power BI is a suite of business intelligence solutions that allows users to analyse data and share reports across devices. This comprehensive solution is comprised of several visualisation products and services, including:

• Microsoft Power BI Desktop - Install this free Windows application on your computer to create data models, visualisations, and reports. It enables you to connect to multiple data sources, combine them into cohesive data models, and represent information as stylish graphics.
• Power BI Service - This cloud-based platform is designed for streamlined publication and distribution. It lets you visualise your data and share interactive dashboards and reports from your browser. You can also collaborate on reports with other Power BI users.
• Power BI Report Server - This on premises report server lets you analyse and visualise data on your local devices and network instead of the cloud. Companies often use this platform to analyse sensitive data and share insights securely.
• Power BI Mobile Apps - Microsoft offers several Power BI apps for IOS and Android devices. These apps allow users to connect to data and view dashboards and reports from mobile devices.

Core features of Power BI

Power BI is a flexible business intelligence tool with many sophisticated capabilities and features. These functions allow Data Analysts and other professionals to perform advanced analytics and create interactive reports.

Data visualisation

Data professionals often work with complex and vast datasets. Even the most experienced Analysts may struggle to understand the connections and trends within raw data. For instance, you may wonder how to make meaning from thousands of customer reviews.

Microsoft Power BI is an accessible and convenient solution that lets you visualise your data. The platform uses data analysis and artificial intelligence (AI) tools to interpret datasets and transform them into interactive graphics.

Here are a few types of visualisations you can generate with Power BI:

• Area chart - A variation of the line chart that uses shading to reveal the magnitude of change over time
• Bubble chart - Represents data points as bubbles with different colours and sizes
• Shape map - Uses shading to compare data across regions on a map
• Smart narrative - Combines visualisations with explanatory text
• Table - Displays data in rows and columns

These data visualisations allow you to spot patterns, trends, and anomalies at a glance. You can use these tools to uncover fresh insights that frequently go unnoticed with traditional data analysis methods.

For instance, transforming customer reviews into a shape map allows you to analyse regional trends effectively. This visualisation can help you identify areas with higher and lower customer satisfaction. Based on these data driven insights, your team can develop targeted strategies to improve the customer experience in these areas.

Data visualisation also enables you to share your findings with non-technical stakeholders. These audiences typically understand colourful graphics more readily than dense spreadsheets or technical reports. Presenting data visually lets you capture their attention and communicate insights more effectively.

Integration with multiple data sources

Microsoft’s advanced integration capabilities are among the top reasons to use Power BI. You can seamlessly connect data to Power BI from hundreds of sources, including:

• Amazon Redshift
• Azure
• Excel
• Google BigQuery
• Salesforce
• SQL databases

These integrations let you access data from external sources without converting it to another format or writing complex queries. Additionally, these connections enable you to synthesise and analyse data from multiple platforms to answer complex research questions.

For example, you could combine Google Analytics and Salesforce data to understand how your marketing efforts affect web traffic. This integration lets you determine which marketing campaigns drive the most visitors to your website, deepening your understanding of customer behaviour.

Real-time analytics

Microsoft Power BI enables real-time data streaming and analytics through interactive dashboards. Users can choose from several real-time semantic models, including:

• Push semantic model - Power BI automatically creates a new database to store real-time data as it gets pushed to the platform. This is the only model that lets users create reports and visualisations with the data.
• Streaming semantic model - Power BI stores the data in a temporary cache instead of building a database.
• PubNub streaming semantic model - Power BI reads an existing data stream from PubNub without storing data.

These models let businesses gain real-time insights from Power BI dashboards and make faster decisions. Organisations can analyse a wide range of real-time data, such as sensor readings and sales transactions. For instance, a factory could monitor manufacturing equipment sensors in real-time to detect anomalies and identify machines that need preventative maintenance.

Types of reports and dashboards in Power BI

You’ll likely need to compile and share your results with business leaders, managers, and other stakeholders. Power BI’s publication and distribution capabilities make it easy to share insights across your organisation.

Power BI reports present data through dynamic visualisations. Users can interact with these graphics by clicking buttons, filtering the data, zooming in, and more. These features allow viewers to drill down into the data and gain fresh insights.

You can use Power BI to build many types of reports, including:

• Digital marketing reports
• Financial summaries
• Key performance indicator (KPI) tracking
• Operational reports
• Sales analysis reports
• Spend analysis reports

Additionally, Power BI users can create custom dashboards for different teams and departments. Each dashboard has a single webpage that uses data visualisations — commonly known as “tiles” — to tell a cohesive story. These tiles are pinned from various reports, which viewers can access by clicking the visualisations. You can also add images, text boxes, and videos.

Dashboards are ideal for focused data analysis. They allow you to select the specific information you need to answer questions or gain insights about business operations.

For example, you could create an interactive dashboard that analyses employee productivity by tracking metrics like attendance and task completion rates. Managers could use this dashboard to gain insights into factors affecting efficiency and identify areas for improvement.

Steps to get started with Power BI as a beginner

Learning to use Microsoft Power BI may seem intimidating, especially if you don’t have extensive data analytics experience. However, this tool has many user-friendly features, so you can master the basics in just a few hours.

Download and install Power BI Desktop

Get started by installing Power BI Desktop on your computer. This free self service data analysis tool allows you to connect your data and build dynamic reports.

First, visit the Microsoft store or website to download Power BI Desktop. Once the application is installed, launch it and arrive at the home page. You may want to click “Intro–What is Power BI?” to complete a 21-minute tutorial on the Microsoft website. You can also sign up for a Power BI account to access more features.

Connect your data

Microsoft Power BI Desktop users typically import data from external sources, such as cloud platforms and Excel files. Click “Get data from another source” to view all your options. For instance, you can connect data from an Access or MySQL database.

Don’t have any data ready to use? Click “Learn with sample data” to create reports and visualisations with preloaded datasets. This convenient feature lets you start building your skills immediately without gathering real data.

Create your first visualisation

Once you’ve connected a data source, you’re ready to build a visualisation. Use the Navigator pane to select the data you want to include in your visualisation and click Load.

Next, select the type of visualisation you want to create and use the drag and drop interface to add fields from your data. For instance, you can create bar charts or line graphs to visualise sales trends. You can also insert buttons, explanatory text boxes, and other elements.

Explore Power BI tutorials and learning resources

Power BI has many built-in learning resources to build your confidence and skills. Click the Help tab in the top menu to access guided learning tutorials, training videos, and documentation. Additionally, this tab includes links to the Power BI blog and the Power BI forums.

These resources can help you gain more advanced skills and troubleshoot issues. For instance, you can learn how to create machine learning models, customise security features, and embed Power BI dashboards in websites.

Use cases of Power BI in industries

Here are a few reasons to use Microsoft Power BI in different industries.

Retail

Microsoft’s BI reporting and data visualisation tool has many applications in the retail world. Companies can use Power BI to track sales trends across different products and regions. These insights allow them to predict customer demand and optimise supply chains.

For example, Walmart uses Power BI to monitor customer preferences, inventory levels, sales, and other data. The retailer uses these insights to deliver personalised marketing and decrease stock outs, improving the overall customer experience.

Healthcare

Power BI allows healthcare organisations to analyse patient data and enhance care. For instance, INTEGRIS Health uses Power BI to monitor caregiver performance and reduce the risk of patient injuries. This tool also allows healthcare organisations to analyse clinical activities, employee productivity, and other metrics to improve operational performance.

Finance

Finance teams use Power BI to analyse financial data, create reports, and track KPIs. Metro Bank is one institution that relies on this tool extensively. The company uses Power BI to analyse online transactions, track customer complaints, and optimise staffing for peak activity times.

Master Power BI with Multiverse

Microsoft Power BI is an indispensable data analytics and reporting tool across industries. Its value and versatility comes from leveraging multiple data sources to create comprehensive reports that deliver data driven insights. Companies use this tool to analyse and improve customer service, marketing, supply chains, and other essential business functions.

While Power BI is relatively user-friendly, learning to use all its features and functions effectively takes time. Multiverse’s upskilling programs can help you learn how to navigate Power BI and leverage this tool in your career.

Multiverse offers several training programs related to Power BI, including Data Fellowship and Data & Insights for Business Decisions. These free programmes allow you to learn advanced data analytics concepts and tools. You’ll also gain hands-on experience completing real data projects for your employer, which may prepare you for more advanced roles.

Ready to join them and level up your skill set? Complete our quick application to get started.

The partnership will deliver AI and data programmes for over 50 professional services staff, as part of a drive from the University to bolster areas including student recruitment and student experience while developing a team of AI and data literate colleagues through at-work upskilling.

Training is funded by the apprenticeship levy and delivered by Multiverse, a tech company that specialises in high-quality training through applied learning. Multiverse has trained more than 16,000 apprentices in data and digital skills since 2016.

Enrolled employees have been assessed on their suitability for five of Multiverse’s programmes, with an assessment carried out for each person based on existing skill level, seniority and role within the university.

Programmes include the 13-month ‘AI for Business Value’ level 4 apprenticeship, which will help learners to identify business value gains that can be achieved through using AI and how to execute AI projects responsibly.

The Data Fellowship (standard or advanced) will upskill employees in data analysis and data science, while the Business Transformation Fellowship will help Keele University to deliver strategic initiatives with an agile mindset and drive change in an evolving digital workplace.

In addition to establishing a culture of AI and data literacy across the university, Keele hopes support its future strategy through the automation of manual processes and the use of newfound skills to identify cost-and-time-saving opportunities.

According to Multiverse’s Skills Intelligence Report, the education sector is most impacted by a lack of data skills, with 38% of employees’ time working with data spent unproductively, compared to the average of 30% across 18 other sectors.

Tom Wilcock, Director of Transformation for Professional Services at Keele University, said: “Keele University is always looking to invest in enriching initiatives that improve our students’ experience. The new partnership with Multiverse will allow us to do just that by upskilling over 50 of our exceptional professional services personnel.”

Multiverse is a tech-first institution that combines work and learning to unlock economic opportunities for everyone. It works with more than 1,500 organisations to close critical skill gaps in the workforce in AI, data and tech, through a new kind of apprenticeship.

Gary Eimerman, Chief Learning Officer at Multiverse said: "Our recent report shows that education is the hardest hit sector when it comes to the data skills gap. Keele University’s investment in AI and data training will close this gap, empowering staff with key skills to deliver the best outcomes for Keele and its students.”

This blog will walk you through a curated set of prompts designed to help AEs gather essential insights, align offerings, and ultimately create compelling sales narratives that resonate with clients - also known as the 3 Whys. From understanding the business landscape to crafting the perfect pitch, these prompts are your roadmap to successful client engagement and will help you get on the road to becoming AI native.

The Prompts:

Prompt 1a: Company Landscape, Mission and Objectives

"Please provide a summary of the key industry themes and landscape that [PROSPECTIVE COMPANY] operates in. Particularly share the trends and phrases used in industry press to describe the obstacles and opportunities broadly in the [YOUR COMPANY’S INDUSTRY}.

Prompt 1b:

“Capture the company's mission statement or vision. Then, create a table that outlines the key business objectives for [Business URL]. Ensure that the table highlights why these objectives are critical for the company’s success and how they align with its long-term vision, stakeholder/shareholder and customer value. Include references to [YOUR COMPANY’S USP]."

• Purpose: Helps AEs understand the business landscape/context, strategic vision and goals of the client, providing a link for identifying relevant challenges [YOUR COMPANY] may solve for.

Prompt 2: Identifying Key Challenges and Root Causes

"Based on the company’s strategic goals outlined in the attached document/above, identify three major challenges for each that could prevent [Business URL] from achieving these objectives. For each challenge, detail the underlying root causes, including organisational, technical, or market-related factors. Ensure you consider potential risks associated with these challenges."

• Purpose: Guides AEs to focus on specific challenges that hinder the company’s strategic goals, helping to uncover deeper issues and precipitating thinking around use cases and skill/knowledge/behaviour gaps [YOUR COMPANY] may solve for.

Prompt 3: Aligning [YOUR COMPANY] Capabilities

"For each of the key challenges identified in the previous prompt, suggest how [YOUR COMPANY]’s solutions can help address these challenges. Provide examples from the attached [PROOF POINTS/CASE STUDIES] document of how similar solutions have been implemented or could be implemented. Highlight the potential impact on the company’s strategic objectives and start to align these to commercial impact around Revenue, Cost/Risk mitigation or Cost avoidance."

• Purpose: Enables AEs to directly align [YOUR COMPANY]’s offerings with the client's needs, demonstrating how these solutions can resolve key challenges that impact top/bottom line.

Prompt 4: Risk Analysis and Metrics

"Create a detailed risk analysis for each of the challenges identified, considering both the risks of not addressing the challenges and the potential pitfalls of proposed solutions. Include metrics that could be used to track the success of these solutions. Refer to the attached document for any relevant examples or insights."

• Purpose: Encourages AEs to think critically about the risks and metrics associated with proposed solutions, ensuring they can articulate the value and mitigate concerns.

Prompt 5: Structuring the 3 Whys Narrative

"Using the information gathered from the previous prompts, structure a concise '3 Whys' narrative that could be presented to stakeholders. This should include:

1. Why Anything? - Explain the importance of addressing the identified challenges.
2. Why Now? - Urge the need for immediate action, supported by data and trends.
3. Why [YOUR COMPANY]? - Highlight why [YOUR COMPANY] is uniquely positioned to solve these challenges and the expected business impact."

• Purpose: Helps AEs create a compelling narrative that resonates with the client’s executives, ensuring that the solution is seen as timely and necessary.

Guidance for Implementation

These prompts should be used iteratively throughout the sales process, starting from the discovery phase and building up to the final proposal and presentation. By following this structured approach, account executives can effectively uncover the root causes of buyer problems, align [YOUR COMPANY]’s offerings with client needs, and present a compelling case that drives decision-making.

Tip: Encourage AEs to tailor the language and examples in the prompts to the specific client industry and context, leveraging data and insights from the attached document to add credibility and relevance to their proposals.

By leveraging the prompts outlined in this guide, AEs can ensure they are addressing the core challenges their clients face, aligning solutions that offer tangible value, and constructing persuasive narratives that drive decision-making. Remember, the key to successful prompt engineering lies in its iterative application throughout the sales process. Tailor your language and examples to the client's specific industry context to maximize relevance and impact.

Master these prompts to streamline your sales process, increase efficiency, and transform every client conversation into a strategic advantage – happy selling!

Why? We know this because of the lessons learned from digital transformation.

One of the common reasons digital transformation initiatives fail is a lack of consideration for the "people dimension." Over the last 10 to 15 years businesses have learned and appreciated the importance of bringing people along with new technology.

The same principle applies to AI, and we see many similarities today with those early days of digital transformation. For example, business leaders see a chance for enhanced performance and growth with over two-thirds of leaders believing AI will improve productivity and customer experience (69%).

But challenges come with change management – and lessons from the past should be considered.

Workplace culture – built for its people – is crucial for success with AI, but there’s no switch to turn it on immediately. It needs nurturing, with time, effort and consistency.

In this article, we’re going to explore what a strong workplace AI culture looks like, and some suggested steps on how to establish it.

What is workplace AI culture?

Workplace AI culture is the integration of artificial intelligence technologies into an organisation's operations, processes, and employee interactions. In a strong workplace AI culture, teams will constantly consider how AI can and should be used within the business, shaping the overall work environment and company values.

How to establish a strong AI culture

Building an AI culture requires careful planning, clear communication, and a commitment to responsible practices and continuous improvement.

Every employee needs to understand how AI is relevant to their role, how they can use it effectively, and crucially, how to use it responsibly. And, for leaders, it’s about fostering and nurturing this culture by regularly considering how AI plays into their business strategy.

Here are our five steps to building a successful workplace AI culture:

1. Understand your level of AI readiness

Around seven in ten (69%) of business leaders believe their organisation will need different workforce skills to stay competitive in 2030 – according to Multiverse’s ‘Preparing for the AI revolution’ report.

In the same study we found nearly half of leaders (48%) say their business currently has significant skills gaps in key functional areas. Mapping these gaps to inform your approach can be included in an AI readiness assessment.

By looking at people, processes and technology you can understand areas of focus for establishing your workplace AI culture.

2. Build a network of AI champions in a ‘hub and spoke’ model to enable experimentation

Structure is important to help everyone understand their stake in AI – as well as to track progress on how AI is being used.

It becomes easier for an organisation to show this in practice when using a hub and spoke model. A team of champions around the organisation (the spokes) can channel information back to a strategic AI lead acting as the ‘hub’. The strategic AI lead must have authority and enough proximity to senior leadership to align the business’ strategic goals with the application of AI.

The supporting network of AI champions is effectively the frontline of change management. By placing these subject matter experts in each business function, it’s clear to every team who they need to speak to when they have an AI question. This group can act as the eyes and ears on the ground – spotting opportunities and working with the right people to develop a business case for AI.

3. Set positive expectations with clear AI policies and guardrails

Risk is an ongoing concern for leaders looking at AI – particularly data security.

Only around 21% of businesses have established workplace policies around employee use of Generative AI according to McKinsey. So, being clear about what information can be shared on ChatGPT, for example, helps everyone understand the appropriate use cases for AI and rules of engagement.

As well as marking out the red lines, communicating clear boundaries means the average worker can understand the spaces where they can innovate and experiment with AI – setting a positive culture rather than a restrictive one.

4. Empower continuous learning and AI upskilling

Advancements in AI are happening at a lightning pace – it’s why 83% of businesses are moving quickly to implement workforce skills development on AI.

Building a culture of learning into your AI culture encourages everyone to understand the places they can improve, and access opportunities to grow.

Factoring in time for learning as part of everyday roles means there is space for experimentation.

Clear communication on your plans for AI implementation needs to go alongside any training and support to help employees adapt.

5. Measure impact – and share what you’ve learned

Measurement is important to support the experimentation element of your AI culture. Similarly to those early days of digital transformation, there can still be lots of hypothesising about the impact AI may have as a whole or on individual processes.

Being clear with your employees on the impact you want to achieve, and the metrics you’re focused on improving, arms teams with the information to assess opportunities and make the case for future AI investments.

Take your first step in building a strong workplace AI culture

Book a consultation with our team of experts, who can help you to build a strong workplace AI culture.

A go-to-market (GTM) strategy allows companies to position their products effectively and stand out from competitors. This plan offers a structured framework for marketing and sales teams navigating complex product launches. It also includes performance metrics to help businesses measure their performance and swiftly adjust.

Businesses in all industries need skilled professionals to create effective go-to-market strategies. Upskillers can help meet this demand by learning relevant skills, such as business analytics and market research. This guide covers the key components of a go-to-market strategy, use cases, and careers involving GTM execution.

What is a go-to-market strategy?

A go-to-market strategy is a comprehensive road map for bringing a product or service to market. It outlines how a business positions and promotes its new offering to engage the target audience.

Businesses use GTM strategies in several scenarios, including:

1. Releasing a new product in an existing market
2. Expanding a current product's reach by entering a new market
3. Updating an existing product to appeal to a new target market

Suppose a software as a service (SaaS) company plans to launch its top-selling event management platform in a new market. Their go-to-market plan could include market research to pinpoint target audiences and understand their event planning needs. These insights allow the marketing team to create focused and tailored messages. The GTM strategy may also include an industry analysis to evaluate competitors and highlight the platform’s distinct features.

GTM strategies offer many benefits for businesses. These plans enable companies to carefully outline every aspect of product launches. Marketing and sales teams use these clear blueprints to work toward common goals and create consistent messaging. For example, Sales Representatives may refer to GTM strategies when they give product demonstrations to ensure they address specific customer needs.

GTM strategies also help organisations focus on high-impact activities. Say, for instance, a SaaS company researches its customers’ preferred communication channels. They might discover that their target audience is highly active on social media but rarely engages with email marketing. Based on this finding, they could prioritise influencer partnerships and social media campaigns to reach customers more effectively. This strategic focus can save significant resources and help companies make a strong impact immediately.

Core components of a GTM strategy

Developing a go-to-market strategy may sound complicated, but you don’t need to create an elaborate 50-step plan. A solid GTM strategy includes these four key elements.

Target market identification

An effective GTM strategy starts by defining the target audience. After all, you can’t develop a focused marketing and sales plan if you don’t know who your ideal customers are and how to reach them.

Here are a few proven strategies to identify your target audience:

• Ask consumers directly: To truly understand your existing or potential customers, go straight to the source. You can engage them with focus groups, one-on-one interviews, and surveys. These methods allow you to gain first-hand insights into customers’ interests and needs.
• Conduct a competitor analysis: Use customer reviews, industry reports, and other resources to learn about your competitors’ client bases. This strategy can help you identify similar audiences interested in your product or service launch. Alternatively, you might uncover market gaps that established companies have overlooked, which you can capitalise on to uniquely position your offering.
• Use social media listening tools: Platforms like BuzzSumo and Keyhole let you monitor conversations related to your product or service on social media. For example, you could track specific hashtags to identify customers who might be interested in your offerings and understand their needs.

Once you’ve identified a broad target audience, divide them into more specific segments based on similar demographics, interests, and other characteristics. This process enables you to tailor your marketing efforts more effectively and maximise your impact.

Finally, develop ideal customer profiles for each segment. The personas should include age, income level, occupation, hobbies, and other relevant details. You can even give them memorable names, such as Sustainable Sophie for an eco-conscious teen. These profiles will help you visualise your target customers more vividly and create highly personalised content.

Value proposition and messaging

Every go-to-market strategy needs a strong value proposition. This statement summarises the unique advantages of your product or service. In other words, it answers the crucial question, “What makes my offering the superior choice compared to the competition?”

A compelling value proposition aligns with your target audience’s pain points. Use surveys and other types of market research to collect data about their challenges and needs. Say, for instance, customers report that they can’t find healthy meal kit delivery services with recyclable packaging. Your value proposition could address this issue by highlighting your meal service’s nutritional value and eco-friendly materials.

A value proposition can also give you a competitive advantage by distinguishing your product or service from others on the market. Refer to this statement as you develop marketing campaigns to make sure you consistently spotlight your brand’s unique features.

Distribution channels

Customers tend to gravitate toward specific distribution and sales channels. Many people prefer the convenience of e-commerce platforms and mobile applications. Others relish the adventure and in-person interactions provided by physical retail stores.

Research your target audience’s preferences so you can choose appropriate distribution channels that fit their shopping habits and behaviours. McKinsey & Company, the Harvard Business Review, and other market research firms frequently share insights into consumer trends and channel usage. You can also conduct focus groups and surveys to gain direct feedback from your customer base. By catering to these preferences, you can expand your reach and increase sales.

Pricing strategy

Even the most loyal customers won’t support a business if they view its pricing as outrageous or unfair. Avoid this issue by establishing competitive and strategic pricing for your products and services.

There are many factors to weigh when developing a pricing strategy for your go-to-market plan, including:

• Competitor pricing: Research what your competitors charge to understand what the market will bear. Use this knowledge and your unique value proposition to determine if your prices should match, undercut, or exceed theirs.
• Manufacturing costs: Calculate the total cost of producing your product or service, including equipment maintenance, labour, and raw materials. This cost is the minimum you should charge to cover expenses, though most businesses add a markup to guarantee that they’ll turn a profit.
• Market demand: The level of demand for your product or service will affect how much you can charge. You might set a higher price if you have no competition or customers are clamouring for your offering. By contrast, you may need to lower your pricing if demand is low or you’re catering to budget-minded consumers.

Types of go-to-market strategies

There’s no one-size-fits-all approach to creating a go-to-market strategy. Businesses can use several techniques to plan their product launch and reach potential customers. Here are three popular methods.

Sales-led GTM strategy

As the name suggests, the sales team drives the action for a sales-led GTM strategy. They help shape the overall strategic plan and use sales techniques to generate revenue.

In this go-to-market model, the sales team drives market entry by actively pursuing leads and building customer relationships. They focus on high-touch, consultative selling. For example, a Sales Representative could provide product demos to engage potential customers and nurture leads.

A sales-led GTM strategy allows businesses to deliver more personalised service throughout the customer’s journey. This attentive approach can improve customer acquisition and retention rates, leading to long-term growth.

Product-led GTM strategy

The product takes centre stage for this go-to-market strategy. This technique aims to make the offering so appealing that it attracts attention organically.

The product-led GTM strategy focuses on delivering an exceptional customer experience at every stage. For example, a business may test and refine its software extensively to improve accessibility and user-friendliness. The sales team could also create onboarding resources to help customers learn how to use their new purchases quickly. This strategy can significantly improve customer satisfaction and boost retention rates.

Account-based marketing

Account-based marketing targets specific high-value customers with personalised marketing and sales efforts. Businesses use this method to build lasting relationships with key accounts and secure large deals.

Marketing and sales teams use many strategies to appeal to major accounts, including:

• Create personalised content for each account
• Engage with an account’s social media content
• Invite account leaders to participate in podcasts or webinars
• Network with account managers at industry events

An account-based marketing plan allows businesses to focus on wooing a few major clients instead of engaging a broad audience. This strategy conserves resources and may reduce the customer acquisition cost.

Steps to build an effective GTM strategy

Follow these steps to organise and streamline the go-to-market process.

Conduct market research

Understanding the state of the market will help you make informed go-to-market decisions.

Start by analysing marketing trends to learn about emerging opportunities and potential challenges. Consult professional associations, thought leaders, and market firms for the latest data and research.

You should also analyse your customers and competitors. Tools like Ahrefs and Semrush provide insights into other companies’ search engine optimization (SEO) strategies. You can study their keyword usage, backlinks, and other tactics. This knowledge will help you develop a competitive digital strategy and build brand visibility. Additionally, customer testimonials and surveys can help you learn about your potential customers’ needs.

Develop customer segments

An effective marketing strategy recognizes the individuality of your customers. However, you don’t have to create marketing materials from scratch for each client. Segmentation lets you personalise your marketing without overwhelming your staff.

Sort customers into groups based on shared traits and tailor your marketing for each segment. For instance, a woman’s sporting goods company might partner with influencers to create engaging social media content for teen girls. By contrast, adult women may prefer simple email newsletters.

Create go-to-market messaging

A strong go-to-marketing strategy includes tailored messaging that resonates with each customer segment.

Begin this process by creating a consistent brand voice across marketing channels. This approach builds brand familiarity and makes your offerings more memorable.

Next, research each segment’s interests and pain points. This knowledge will help you develop personalised content that explains how your product or service will improve their lives. You can also use A/B testing to assess different variations of marketing materials and improve your content over time.

Align teams and set KPIs

Developing and executing a go-to-market strategy doesn’t happen in a vacuum. Encourage your marketing, product, and sales teams to collaborate for the best outcomes. You can promote cross-departmental facilitation by organising joint strategy sessions and group workshops. These events let all team members contribute to the go-to-market strategy and work toward shared goals.

Finally, gather and analyse key performance indicators (KPIs) to track GTM success. Relevant metrics include:

• Conversion rate: The percentage of users who perform specific actions, such as ordering a new product or subscribing to a service
• Customer acquisition cost: The average amount spent to gain a new customer
• Customer retention rate: The percentage of clients who keep using a product or service over a given period
• Engagement level: How frequently customers interact with your content
• Return on investment: The revenue generated by a go-to-market strategy versus how much a business spends on it

These KPIs will help you identify your successes and correct course if your GTM strategy isn't going as planned.

Challenges and solutions in go-to-market strategies

While go-to-market strategies offer many benefits, they also raise a few challenges.

Poor coordination can derail the best marketing plan. Keep all teams on the same page with consistent and regular communication. For example, you might organise a weekly group meeting to share updates and concerns.

Targeting the wrong audience is another common pitfall. Your team might spend weeks designing an elaborate marketing campaign, only to be met with crickets from consumers. Prevent this issue by researching your target audience thoroughly. You can also test your messaging on smaller groups before investing in a full-scale product launch to make sure your content resonates.

Examples of go-to-market strategies

Explore successful go-to-market strategies from different companies and industries for inspiration.

Slack

Slack uses a product-led go-to-market strategy to grow its customer base. The creators of the communication platform conducted preliminary tests to gain user feedback and improve their product. They also created training resources to help busy professionals learn Slack quickly. These features made the product irresistible for many companies and fueled Slack's rapid growth.

Salesforce

Salesforce has developed a sales-led GTM strategy. The customer relationship management (CRM) platform uses content marketing to establish its authority and deliver customer value. Additionally, Salesforce creates tailored marketing campaigns to promote its products to different customer segments, increasing sales.

HealthLink Dimensions

HealthLink Dimensions uses account-based marketing to promote its data services to hospitals, insurance companies, and other organisations. Sales Representatives use e-gifting as a personal touch to win over account managers, while the marketing team develops omni-channel marketing campaigns for key accounts. This GTM strategy increased the company’s customer acquisition rates by 234% in approximately one year.

Go-to-market strategies in careers

Companies hire many professionals to develop and implement their go-to-market strategies. Here are three career paths related to this popular strategic approach with salary data from Indeed.

Marketing Director

Average base salary: £72,000

A Marketing Director manages the marketing team as they create and execute GTM strategies. Their responsibilities include coordinating with leadership teams, managing the marketing budget, and overseeing campaign development.

Product Manager

Average base salary: £54,501

A Product Manager oversees the entire product development lifestyle, from conception to post-launch support. They collaborate with marketing and sales professionals to define and communicate the product's unique value proposition. Additionally, this expert contributes to the development of the GTM strategy by performing market research and planning product launches.

Sales Manager

Average base salary: £41,001

A Sales Manager shapes the sales strategy and ensures the overall go-to-market plan aligns with the business goals. They also mentor the sales team, monitor performance, and help Sales Representatives achieve performance goals.

Expand your marketing and sales knowledge

A strong GTM strategy can make the difference between a successful product launch and a disappointing flop. The right plan allows businesses to hit the ground running with a well-defined target audience, competitive pricing, and tailored sales strategies.

Developing an effective go-to-market strategy requires strong interpersonal skills and a thorough understanding of market dynamics. Gain the necessary knowledge with Multiverse’s free upskilling programmes. You’ll build future-proof skills while working for your current employer, so you won’t have to worry about pausing your career.

Upskillers study artificial intelligence, business analytics, digital marketing, and other in-demand fields. This content will prepare you to create and implement competitive GTM strategies in any industry. You’ll also receive personalised coaching to help you plan your career path and navigate the job market.

Take the next step in your career journey today by completing our quick application. The Multiverse team will reach out to discuss next steps.

So, it’s no surprise that 65% of respondents to McKinsey’s latest global survey say their organisations are regularly using GenAI. It’s also driving demand for new workforce skills – last year saw a 2,000% surge in roles demanding generative AI skills, with organisations of all stripes keen to tap into the vast potential productivity benefits.

However, even with most businesses deploying AI in some capacity, only 13% of employees have been offered AI training by their employers.

Successfully implementing AI in the workplace is not as simple as buying a popular tool and expecting employees to adapt. To get the most value from these technologies, workforces need skills – both technical and soft.

But as it stands, there’s a significant lack of AI skills in the workplace. According to our research, almost half of leaders (45%) point to AI as their most significant skill gap.

If businesses want to leave the experimentation phase and begin to define their unique AI use cases, they’ll need employees who can use AI productively and with minimal risk.

Here are the top 10 skills we believe employees need to effectively implement artificial intelligence in the workplace:

1. Data engineering

A crucial early step in any AI implementation journey is building and maintaining robust data infrastructure. This is responsible for collecting, storing, and processing the large volumes of data AI needs to be trained on.

As such, organisations need employees with data engineering skills. They help organise and clean data, so the datasets fed to AI models are high-quality and relevant. This means the models deliver the most reliable insights, and also helps ensure data integrity, which is important for regulation compliance.

2. Data analysis and visualisation

Once you have access to clean data, it needs to be interpreted to extract meaningful insights. Data analysis skills help employees identify trends, patterns, and correlations within complex datasets so they can make data-driven business decisions.

But it’s equally important for a variety of stakeholders to be able to understand data insights. Data visualisation skills go hand-in-hand with data analysis, helping employees convert raw data into graphical representations – such as charts, graphs, and dashboards – that make it easy for others to digest at a glance.

3. Data science and programming

To go from insights to action, you need data science skills. These allow staff to develop, deploy and maintain AI systems as businesses begin building their own unique AI solutions.

Programming skills are also vital. The capacity to create efficient and scalable code in languages such as Python, C++ and Java is key when it comes to integrating AI models into existing business systems and workflows.

4. Risk management and ethics

Once a business starts implementing AI models, it needs employees capable of creating comprehensive risk management frameworks. These skills will help ensure the long-term success of AI projects by supporting employees to better identify, assess and mitigate risks, such as data breaches and algorithm biases.

However, AI initiatives will only truly be sustainable if the business continues to use it responsibly. Employees should also know how to uphold privacy and accountability, as well as minimise bias within the models they work with.

5. Planning and stakeholder management

Successful AI initiatives are connected to larger organisational objectives. This is why every business needs a plan – or several – for implementation. Training employees on how to set realistic milestones, identify potential challenges and create contingency plans is critical from idea to execution.

Alongside planning skills, stakeholder management is an important factor in the success of any AI project.

Ideally, all stakeholders should be aligned when working on AI projects, but this isn’t always the case. Skills in stakeholder management can help foster clear lines of communication between execs, employees, customers and regulators. This way, concerns can be quickly addressed and expectations managed.

6. Business analysis

One common challenge for the AI strategy leaders we speak to is ensuring that AI solutions are designed and implemented to directly solve specific business problems.

Employees with business analysis skills help ensure AI solutions are grounded in business needs and directly linked to desired outcomes, such as process optimisation or cost reduction. By assessing pain points and workflows, businesses can align AI solutions to problems and deliver the most successful AI initiatives.

7. Solution design

To gain the most value, it’s rarely a case of selecting an AI tool straight off the shelf. Custom-built solutions enable organisations to get more from AI, with use cases specific to their business needs.

Ideally, the employees using an AI solution in their everyday tasks should be involved in its design. But without training, this can be challenging to navigate.

Skills in solution design support employees to build tailored AI use cases based on their business analysis. They can seamlessly embed AI into existing workflows and identify new opportunities to scale AI initiatives, ensuring that AI solutions deliver sustained value as business needs change.

8. Machine learning

Machine learning (ML) skills help empower employees to create and implement models, analyse data, and evaluate their performance. Together, these streamline business processes and minimise the amount of tedious work for humans.

One step further is deep learning – a subset of ML – which uses multiple layers of neural networks to model complex patterns in datasets. ML skills can help businesses develop unique AI initiatives for image and speech recognition, natural language processing (NLP) and predictive analytics.

9. Cloud infrastructure

As you begin to roll out more AI initiatives, it will become increasingly important to have reliable, flexible access to the cloud’s vast computational power and storage.

Cloud infrastructure skills can help businesses better manage usage and enhance accessibility and collaboration across the entire organisation. And, as many cloud platforms have AI tools built in, employees with these skills can be instrumental in progressing a business’ AI efforts.

10. Strategic thinking and leadership

It’s not enough to only develop AI literacy among employees – business leaders should also understand AI initiatives. That way, they can strategically guide projects to make sure they are aligned with long-term goals.

By creating a compelling vision for AI and securing buy-in from stakeholders, effective leaders can foster an internal culture that embraces AI.

Employee training can accelerate AI progress

Demand for AI skills will likely continue to outpace supply in the near future. The competition for talent is fierce, but it doesn’t always need to be sourced externally.

Leveraging training opportunities to improve existing employees’ AI literacy not only removes the stress of recruitment, but also demonstrates the business is invested in the development of its current staff.

Once a workforce has the right mix of skills to get the most from AI, businesses will be able to deliver impactful change while improving or maintaining a competitive edge.

To get started on your workforce upskilling and reskilling journey, check out our AI training solutions for businesses.

This shift toward data-centric operations highlights the crucial importance of selecting appropriate database management systems (DBMS). Businesses have two options when designing modern applications: SQL vs NoSQL. Each type of database has a place in modern tech stacks, but they serve different purposes. SQL databases allow businesses to manage structured data, while NoSQL databases excel at handling more diverse kinds of information.

This comprehensive guide lays out the key differences between SQL vs NoSQL databases to help you decide which one fits your business and data needs. We’ll also explore practical applications of SQL and NoSQL and career paths that use these databases.

What is SQL?

Structured Query Language (SQL) is a domain-specific language used to build and manage relational databases. Tech professionals use this programming language to handle a broad range of tasks, such as inserting, updating, and deleting data.

SQL organises data into tidy tables with different columns and rows. Each column represents a specific field of the data, while each row contains associated values for those fields. SQL relies on predefined schemas to place every datapoint in the appropriate spot within these tables.

Say, for instance, you build an SQL database to store contact information for potential leads. Each column could represent a different type of information, such as email addresses and the source of the lead. Meanwhile, each row would contain data for a single lead. Here’s a basic visualisation:

Traditional relational databases typically contain multiple tables with defined relationships. For example, your lead nurturing database could also include tables tracking your interactions with each prospect, their purchasing habits, and scheduled follow-up calls. This approach allows you to store all relevant data in a centralised database and maintain consistent records.

However, SQL databases can only handle structured data that fits into a table. This fixed schema means you can’t use this type of database to store unstructured data that lacks a predefined format. For instance, an SQL database wouldn’t handle audio recordings of sales calls or photos of the leads effectively.

Common uses for SQL

Businesses in all industries use SQL databases to manage structured data. These versatile systems are easy to build and have many practical applications.

Retailers often use SQL databases to streamline inventory management. The systems can sort products into different categories, record their locations inside physical stores, and track stock levels. When a specific product’s inventory runs low, the database can notify staff or automatically reorder the item. This approach helps retailers maintain consistent inventories with minimal human intervention.

Enterprise resource planning (ERP) is another popular application of SQL databases. Organisations use ERP systems to manage finances, human resources, and other core business operations in a centralised platform. Many ERP platforms are built on SQL databases, which can store and process vast quantities of data. For example, an SQL database can manage employee benefits data, track payroll, and generate reports.

What is NoSQL?

You might assume that NoSQL is the antithesis of SQL, but that’s not the case. This abbreviation stands for Not only SQL, which means this type is designed to complement SQL databases, not replace them.

NoSQL databases use a flexible schema to manage non relational data instead of rigid, predefined tables. This approach allows them to handle a wide range of data types, including:

• Unstructured data: This information doesn’t have a specific, pre-defined format. As a result, it doesn’t fit into conventional tables and can be challenging to organise. Examples of unstructured data include customer reviews, Instagram posts, and web pages with varying formats.
• Semi-structured data: This kind of data has some consistent traits but isn’t structured enough to fit into a table. It typically has metadata, enabling hierarchical data storage. For example, emails are semi-structured data because they have subject lines, sender addresses, and other defined elements. However, the content of emails varies widely, so they don’t fit in SQL databases.
• Structured data: Like SQL databases, non relational databases can store structured data, but they often use more flexible formats.

Types of NoSQL databases

NoSQL database systems use many different models to handle data, such as:

• Document databases store data in adaptable, semi-structured formats, such as JavaScript Object Notation and XML. Each document can have a unique structure, but they’re stored in a similar manner for speedy data retrieval.
• Key value databases assign a unique key — or identifier — to each value. The databases use these key value pairs to organise and retrieve data.
• Wide-column databases store data in column families, which group together related columns. Every column family can contain an unlimited number of columns with different data types. This highly flexible structure allows wide-column databases to store and manage vast quantities of information.
• Graph databases organise data in complex networks of interconnected nodes and edges. This structure allows them to query and analyse relationships between associated data points. For example, a supply chain management application could use graph databases to trace the connections between distributors, manufacturers, and stores.

Common uses for NoSQL

As data grows vaster and more complex, many businesses have turned to NoSQL databases to manage information. Here are a few areas where these databases excel:

• Big data analytics: NoSQL databases can scale horizontally to accommodate enormous and fast-growing datasets. They also integrate with big data platforms like Apache Spark to process this information in real time.
• Internet of Things (IoT): This interconnected network of devices generates highly variable data, such as sensor readings and wireless security camera footage. Non relational databases have the flexibility needed to accommodate this dynamic data.
• Social media platforms: Instagram, TikTok, and other social media channels must store and retrieve millions of photos, comments, user profiles, and other unstructured data. These platforms use NoSQL databases to efficiently manage this information so users can access it promptly.

Key differences between SQL vs NoSQL

SQL and NoSQL sound similar, but they have different structures and purposes. Here are a few key distinctions between SQL vs NoSQL:

Schema flexibility

SQL has a rigid schema structure consisting of predefined tables, columns, and rows. If a data point doesn’t fit into the established format, the database will reject it.

By contrast, NoSQL offers dynamic and highly flexible schemas. For example, a content management system could use a NoSQL database to manage many types of content, such as blog posts and videos, with drastically different formats.

Scalability

Traditional relational databases scale vertically by adding more data to a single server. This structure improves data integrity because information isn’t spread across many servers. However, the server’s capacity limits how much information the database can store.

NoSQL databases scale horizontally by distributing data across a network of interconnected computers or servers. Businesses can expand their capacity by adding more nodes to the network for nearly infinite growth. However, this distributed data handling can increase the risk of data breaches and other cybersecurity threats.

Data consistency

Both types of databases aim to preserve data consistency, but they have different priorities.

SQL focuses on complying with the four ACID principles:

• Atomicity: All database operations must succeed completely to count. This safeguard prevents partial transactions and rolls back failed operations.
• Consistency: Every transaction must meet predetermined rules.
• Isolation: Simultaneous transactions must occur independently and not impact each other, ensuring data validity.
• Durability: The database must permanently save completed transactions, even if the system fails.

These elements improve transaction management by maintaining data accuracy and consistency. They also reduce the risk of data corruption during critical transactions, such as bank transfers and medical record updates.

On the other hand, NoSQL databases prioritise flexibility and speed over strict consistency. These systems typically follow the CAP theory, which states that a database can only achieve two out of the three criteria:

• Consistency: Multiple nodes in the NoSQL network see the same data simultaneously
• Availability: Every request returns a response
• Partition tolerance: The system keeps operating if one node in the network fails

Which database should you choose? SQL vs NoSQL pros and cons

The type of database you choose will directly impact your application’s capabilities and performance. Here’s a few factors to consider as you compare options:

• Data structure: Consider your data complexity and consistency needs. SQL offers a more rigid structure and maintains data integrity, while NoSQL provides unparalleled flexibility.
• Performance and scalability requirements: Relational database management systems can handle complex data queries and transactions with ease. However, these SQL databases have limited capacity and process data in batches. By contrast, NoSQL databases scale horizontally and provide real-time data processing.
• Cost: The price of SQL vs NoSQL databases can vary widely depending on your infrastructure needs. Commercial SQL applications can be expensive, and you may need to invest in costly hardware to scale your database. Conversely, NoSQL systems often distribute data across multiple servers or cloud platforms, which can be more affordable in certain cases.

Pros and cons of Structured Query Language

SQL pros:

• Built-in data security features, including access control and user authentication
• Prioritises data consistency and integrity
• Requires minimal coding knowledge

SQL cons:

• Can only manage structured data
• Commercial SQL platforms can have high licensing fees
• Slower batch processing

Pros and cons of Not only Structured Query Language

NoSQL pros:

• Highly flexible and scalable
• Ideal for unstructured and semi-structured data
• Minimal maintenance requirements

NoSQL cons:

• Fewer educational resources due to its relative newness
• May sacrifice data consistency for speed
• May struggle to handle complex queries

Examples of SQL and NoSQL databases

Case studies can help you deepen your understanding of the most common types of databases. Look for examples from highly successful companies for inspiration.

For instance, Uber is powered by Docstore, a distributed SQL database built on MySQL. This database distributes data across multiple partitions made of MySQL nodes for optimal performance and scalability. This structure allows Docshare to process millions of requests per second.

This visualisation depicts how Apache Cassandra fits into Spotify’s personalization pipeline. Source: Spotify.

On the other hand, Spotify uses Apache Cassandra, a NoSQL database, to personalise playlist and song recommendations. The database has a flexible data model that allows it to handle vast amounts of real-time data from millions of users across different servers. Spotify uses this database to analyse user behaviour and offer custom music recommendations.

Jobs that use SQL and NoSQL

Many employers seek job candidates with SQL and NoSQL proficiency. Here are three roles that often use these skills and their average salaries based on data from Indeed.

Data Analyst

Average salary in the UK: £34,597

Average salary in London: £42,553

A Data Analyst collects, processes, and manages data. They use SQL and NoSQL to design and query databases. Other responsibilities include applying statistical methods to uncover patterns in data and derive actionable insights.

Database Administrator

Average salary in the UK: £45,417

Average salary in London: £56,007

A Database Administrator designs and maintains data architecture for organisations. This career requires a strong understanding of SQL and NoSQL for efficient data storage and management.

Software Engineer

Average salary in the UK: £46,504

Average salary in London: £56,458

A Software Engineer uses programming languages to develop and maintain software applications. They often integrate these products with SQL or NoSQL databases for efficient data storage and processing.

Develop database proficiency

SQL vs NoSQL is a constant debate for tech aficionados. These data structures allow businesses to manage and store data efficiently, but they have different characteristics and purposes. Research each option thoroughly before making a final decision for your application.

Gaining proficiency in these query languages can also help you advance your career. Many jobs require these skills in the tech industry and beyond.

A Multiverse apprenticeship can help you explore career opportunities and develop SQL, NoSQL, and data analytics skills. Our Data Fellowship program teaches you how to transform raw data into compelling stories and actionable insights. Upskillers study advanced concepts and gain hands-on experience by working for top employers.

Ready to launch your data career? Complete our simple application today, and the Multiverse team will get in touch.