The Multiverse blog

The 3Cs Defined

Our 3Cs framework simplifies and reinforces what we look for in our candidates:

1. Capability: Demonstrates the required hard/technical skills and competencies necessary to do the job well, exhibits the ability to think critically and handle complex tasks effectively, and possesses the necessary knowledge and experience to excel in their role.
2. Coachability: Shows a willingness and eagerness to learn, develop, and grow in their role, accepts and implements feedback effectively, utilising it as a tool for improvement, and demonstrates self-awareness and the ability to reflect on personal growth.
3. Character: Displays resilience, discipline, work ethic and a positive attitude in the face of challenges, proven history of driving towards long term goals and a relentless pursuit of excellence, acts with integrity, ethical conduct, and a collaborative spirit, embraces diversity, and contributes to a positive work environment.

Key principles of the 3Cs

1. Simplicity scales: This framework cuts through ambiguity and offers a clear guide to what we seek in a candidate. It’s designed to be easily understandable and utilised by our hiring team and our candidates.
2. Values alignment: It's fundamental that the people we bring onboard align with our core values and contribute positively to our work culture.
3. Data driven: The 3Cs enables a measurable and equitable hiring process for every candidate, and drives continuous improvement in our hiring strategy over time.

Through the 3Cs framework, our goal is to attract and hire people who will thrive in our unique environment. The characteristics we've identified reflect a balance of critical skills, leadership potential, growth mindset, and shared values: the foundations for a successful career at Multiverse.

If the 3Cs resonate with you, we’d love to receive your application. We’re on a mission to provide equitable access to economic opportunity, for everyone. Join us?

Data science may be a good fit if you have technical skills and enjoy spotting patterns. It's also an in-demand specialism that commands above-average pay.

A 2021 study estimated there were at least 178,000 unfilled data specialist roles in the UK. Cut to 2023, and the availability of employees with these skills still doesn't match the demand. Aside from being a high-growth role, the average Data Scientist's salary is on the higher end — even for entry-level roles. If that all sounds appealing, keep reading to learn more about this field.

What is data science?

Data science aims to uncover meaningful insights from data. As a discipline, it uses mathematics, statistics and computer programming practices to achieve this. People who work in data science must also understand technology like artificial intelligence (AI) and machine learning algorithms.

Companies rely on data science specialists to help them gather, clean and organise data so they can make sense of it. Organisations can then access the data insights needed to improve decision-making and enhance products. Data science also helps companies solve complex problems and find growth opportunities otherwise hidden in raw data.

What is a Data Scientist?

A Data Scientist is a practitioner within the data science field. Data Scientists are similar to Data Analysts. But how these professionals use data is different.

Like Analysts, Data Scientists spot key trends, patterns and anomalies in business data. However, Data Scientists use more advanced mathematical and programming techniques. So, they may build custom programmes to automate data collection rather than rely on pre-built solutions.

Data Scientists also help uncover which questions companies must ask to solve problems and encourage growth. They then use data to help companies find answers to these questions. Because the role is technical and business-focused, Data Scientists must be technically and commercially minded.

A Data Scientist's responsibilities may include:

• Collecting, cleaning and organising raw data
• Using coding languages like Python to edit and analyse data
• Creating predictive models to forecast future business outcomes
• Building programmes that automate data gathering and processing
• Communicating data insights to non-technical colleagues
• Understanding business pain points, challenges and priorities
• Using data analysis methods to answer business-critical questions
• Keeping up with industry standards, tools and emerging technology like AI

Why does data science matter?

The World Economic Forum (WEF) forecasts Data Scientists will land in the top 10 fastest-growing roles from 2023 to 2027. But we don't need to look to the future to know that data science matters — data science skills are already in demand.

Research by The Department for Culture, Media and Sport (DCMS) shows 48% of UK businesses are "recruiting for roles that require hard data skills." Machine learning, programming, and advanced statistics are examples of the required skills. All of these are heavily involved in the data science landscape.

Given the business potential, the demand for data science skills within the workforce isn't surprising. Organisations can leverage data science to drive innovation, create efficiencies and encourage growth. And with ever more data at their fingertips, companies need people who can tell the stories it holds.

Business benefits aside, rapid technology advancements, alongside the sheer volume of data now available, mean data science is no longer a 'nice to have.' To stay competitive, organisations must be more innovative with how they use data. Many are already bridging this gap by investing in data science specialists, tools and infrastructure.

What is the data science process?

The data science process is a series of steps that help you solve a problem, gain insights and improve business outcomes using data. There are different types of data science processes — the one you use will depend on the project, task and company preferences.

CRoss Industry Standard Process for Data Mining (CRISP-DM) is a common type of data science process. To use CRISP-DM, you'll typically follow these steps:

1. Business understanding: You work with colleagues and stakeholders to determine business needs and goals. Finding better ways to market to specific customers is an example of a business goal.
2. Data understanding: You gather the data you need or already have and identify what needs to happen to it next. You may need to clean the data first, for example.
3. Data preparation: You decide how best to prepare the data for modelling. That could mean dividing your data into specific categories.
4. Modelling: You choose the modelling techniques you'll use based on the nature of the problem.
5. Evaluation: You review the performance of the models in line with the original business goal.
6. Deployment: You add the models into a production environment, including integrating them into existing systems. You also show stakeholders how to access any results.

Data science tools

Many tools can help you with data science tasks, and the ones you use will change depending on the nature of the task. That said, Microsoft Excel, Python, and Tableau are examples of everyday data science tools. Let's take a look at each.

Microsoft Excel

From simple analysis and reporting to finding complex insights from multiple datasets, Excel is an asset to any data scientist's toolkit. Excel's pivot tables and formulas are also handy for quick data exploration. Another useful feature of Excel is the ability to automate smaller, manual tasks like data cleaning.

Python with libraries

Python is a versatile programming language commonly used in data science. Data Scientists use Python to wrangle, cleanse and edit data. You can also use it to develop business insights based on statistics. You can use a Python library like Pandas for data manipulation and analysis. Meanwhile, other libraries like Seaborn and Matplotlib help with data visualisation.

Tableau

Tableau is a popular Business Intelligence (BI) tool. Data Scientists use it to connect to different data sources and share insights throughout a company. You can also use BI tools like Tableau to visualise and present data insights in ways that non-technical colleagues can understand.

Data science use cases

From driving efficiencies to data analysis, here's how UK companies use data science to impact business outcomes.

Creating efficiencies

Morgan Sindall Infrastructure delivers some of the UK's most complex and critical infrastructure. The organisation recently partnered with Multiverse to launch the Infrastructure Data Academy. The purpose is to upskill Morgan Sindall Infrastructure's workforce, focusing on data science skills like analytics, AI and predictive modelling.

Sarah Reid, Managing Director of the Morgan Sindall Infrastructure Highways business unit, says the data upskilling project "enables the business to become a digital-first organisation, creating efficiencies through new technology investments to further develop our culture around using data in everyday operations."

Example: A key challenge for a company working with infrastructure could be optimising highway groundwork with longevity top of mind. By leveraging data insights from past projects and building predictive modelling, the company could extend the lifespan of highway infrastructure. Doing this creates efficiencies by lowering the number of major repairs or reconstruction projects needed over time.

Data analysis

The Maritime and Coastguard Agency (MCA) has partnered with Multiverse as part of a government drive to boost data skills across the civil service. The MCA aims to train its workforce to "make better use of data" — a key focus will be data analysis upskilling.

"There is a growing understanding in government of just how much data analysis can add to an organisation – in all aspects of the business," says Charis Doidge, Head of Data Science and Analytics at the Maritime and Coastguard Agency. Charis cites building the MCA's in-house capabilities and driving efficiency as two benefits of better use of data through analysis.

Example: A company like the MCA could use data analysis to assess the performance of its search and rescue operations. To do this, the company would analyse data from past incidents. It could then find areas for improvement, including ways to reduce response times to emergencies at sea.

Uncovering data narratives

When you know how to interpret and handle data, you can analyse it more effectively. A more innovative approach to data analysis allows you to uncover patterns and insights that would otherwise stay buried in unstructured or unconnected data. These insights help you find and present meaningful data stories.

Example: Let's say you work for a retail company that wants to create better online customer experiences. To help with this goal, you use online engagement data to run a customer journey analysis. The data reveals which touchpoints lead to conversions and where customers leave the site. You can then use the insights to help you optimise what's working and improve what isn't. The result will be more custom experiences for your customers—and more conversions for your company.

Data science skills

If a data science career path sounds appealing, you should focus on developing specific skills. These include working on your technical, mathematical and statistical abilities. But remember, you also need a strong commercial focus. Let's take a closer look.

Technical ability

You'll need technical ability to work in data science. That means using SQL (Structured Query Language), Microsoft Excel, and data visualisation techniques. You'll also need computer programming skills, including knowing programming languages like Python.

Mathematical understanding

Understanding maths and statistics is crucial for data science. You'll need to know linear algebra, statistics and probability. With this knowledge, you can analyse and interpret complex datasets to uncover patterns. A mathematical understanding will also help you build predictive models and test ideas.

Commercially minded

Data science isn't just technical. How you communicate your findings to other stakeholders matters, too. To have a successful career in data science, you'll need to know and use the entire data science process to meet business needs. You'll also need to be able to share your data insights with non-specialists in a way they can understand.

What do data scientists make? Data scientist salary in the UK

The average Data Scientist salary in the UK is around £55,420 per year (Talent). But what you earn as a Data Scientist fluctuates depending on skill level and experience. That said, entry-level positions are higher than average compared to other professionals.

An entry-level Data Scientist can make around £42,500 on average per year. That's over £7,500 higher than the typical annual earnings in the UK. Then, as you progress in your career and gain more experience, you could be on track to earning up to £80,000 a year.

Aside from skills and experience, your location may also affect how much you make as a Data Scientist. For example, in a city like London, the cost of living is higher, so salaries are higher. According to Indeed, the five highest-paying cities for Data Scientists in the UK are:

• London
• Reading
• Birmingham
• Edinburgh
• Manchester

Here’s a breakdown of the UK's top-paying cities for Data Scientists.

Data science courses: best way to learn data science

Online courses, university and professional apprenticeships are three popular ways to learn data science — here's what you need to know about each.

Online courses

Some Further Education (FE) colleges may offer part-time online data science courses or qualifications. Platforms like Coursera provide the same, but they might not have qualifications or accreditations. You may also need to self-fund these courses if your employer doesn't fund professional development.

University

University is another way to learn data science. If you choose this option, you'll want to study for a degree or postgraduate qualification in a relevant subject like maths, statistics, or data science. But university is undeniably expensive. The combined cost of tuition fees and accommodation costs in England is around £50,000. You're also not guaranteed a relevant data science role when you graduate.

Professional apprenticeship

What if you don't want to pay nearly to earn a degree-level qualification or for continuing education — but you do want to progress within your data science career? In that case, consider a tuition-free alternative to university, like an apprenticeship.

Unlike university, you don't need to self-fund the programme — it's totally free for learners with no strings attached. And depending on the apprenticeship level of study, you can earn a degree equivalent qualification.

To top it off, you'll learn job-ready skills through an on-the-job training programme that fits around your work schedule. So, even if you're a few years into your career, you can keep working full-time while you learn without taking a career break.

Learn data science

To succeed in data science, you should learn to use Excel, Python and BI tools like Tableau. You'll also need technical, mathematical and business skills. With a Multiverse apprenticeship like the Advanced Data Fellowship programme, you can expand upon the data science skills you already use on the job for free. You'll also get paid your regular salary to learn and apply your new abilities in your day-to-day role.

To get started, simply create a Multiverse profile. Our team can then double-check your eligibility and discuss apprenticeship options with you.

We built the first version of our values back in 2016 and they served us very well. At that point, there were 8 values and they formed a deeply personal and impactful part of the growth of Multiverse. But as we crossed 600 people and gathered pace for a succession of funding rounds, we knew it was time for our values to evolve, ensuring they were ready to drive us forward to where we will be in the future.

Today we have 6 values, and they are:

We seek to level the playing field and are guided by fairness as a fundamental principle. It is our North Star, reflected in our mission to create a diverse group of future leaders and governs what we do and don’t do at Multiverse.

We treat those around us with respect and kindness. We never assume bad intent and judge people based on their actions, not on assumptions about their motivation. We act with integrity and think about the impact our actions have on others and strive to be the best version of ourselves.

We are direct but kind in our feedback and provide it regularly because we want to help people grow and reach their full potential. We believe skills are developed and built and see setbacks as opportunities to adapt and grow. No one should be the same person they were 3 months ago.

We take responsibility for what happens around us and we do the right thing, not the easy thing. We work with great care and skill, sometimes at an uncomfortable speed, energising those around us by the standards we hold.

We understand the fundamentals and facts first, and build our approach from there. We are guided by integrity in the decisions that we make and are solutions oriented. We will not be constrained by what has been done before or tradeoffs we don’t need to make.

Our mission is serious, and so is our approach to the work we do. But everyone deserves to work in an environment where they can have some fun, share some laughter, and feel unafraid to be themselves.

Our values are embedded in our culture and ways of working - allowing us to make the right decisions for Multiverse. From who we hire, to how we talk about career progression and development, and importantly, setting the tone for everyone as we continue in our mission to provide equitable access to economic opportunity, for everyone.

Do these values align with your personal values? We're hiring. 

What is your playbook for success?

The team matters more than anything to me, and surrounding ourselves with incredibly talented people is the key to success. It all comes down to recruiting the right people, developing those folks and then executing with excellence, both at the individual level and as a team.

What’s your leadership style?

I’ll address style but first I want to call out that I’ve been fortunate to work under some of the greatest sales leaders out there and I’ve learned a huge amount from them. I’ve taken elements of their approach that work really well, and reflected on what I would do differently - adaptability is critical for my leadership approach.

My experience extends itself to our team in two ways: first, my number one priority is to coach and support the team so that individuals can be wildly successful in their jobs at Multiverse. It’s a supportive, collaborative style that carries trust. When you’re with a group of winners, like we are, this collaborative style is the best approach. Second, I want our team members to develop here at Multiverse in a way that “future-proofs” their careers. This means they become so great at their profession at Multiverse, that they command their ideal opportunities in the future.

So a big part of my leadership style is to approach situations and people with empathy, carrying a coaching mindset, while ensuring an enjoyable environment - my sense of humor, for better or worse, comes out often! With this mindset, I find I’m far more likely to unlock potential in the people and ideas around me.

Why did you join Multiverse?

I’ve been part of some very special journeys at the likes of Udacity, MongoDB, and Zscaler and what all of those companies had in common was the caliber of the team around me, and the opportunity to solve critical problems for some of the biggest organizations in the world.

At Multiverse, we have an incredible team, a huge market opportunity and we have a mission that matters. We are transforming lives in our ability to upskill and reskill talent, and we’re providing equitable access to economic opportunity, for everyone. There are not many places where you can have such a meaningful and powerful impact that serves both your professional and personal why.

Tell us about the culture at Multiverse

Reflecting on my first 6 months as CRO, it’s clear that we have a lot to celebrate in our culture. We have an environment where folks can learn and develop their careers faster than anywhere. We have a big commercial opportunity, we’re serving a mission-critical market and we’ve got some of the best sales talent in the market. Our teams work together and take pride in getting better every day - the proof is in our sales productivity and growth rates.

While all of those circumstances are fortunate, my priority is to enhance our culture further. Our culture of excellence isn’t changing, but it’s really important to me that we have fun while we’re achieving these amazing things. One of our values is “we don’t take ourselves too seriously”, and I’m keen to live by this value so that we can all enjoy this journey by succeeding and having fun together.

What are your 2024 goals?

2024 is huge for us. We’re doubling the size of our sales team and investing in our RevOps and Enablement teams to continue our focus on excellence, learning and development. And we’re going to capture more of the market, at a faster pace - so now is the perfect time to be joining our team.

If you’re looking for a big commercial opportunity, a mission that matters, and an environment where you can learn, earn, develop and have fun, then Multiverse is the place for you. Apply here.

As part of our employee benefits program, every Multiverser receives two paid volunteering days per year on top of their holiday leave allowance. Through this benefit, we’re enabling our team to make a positive contribution to society, and fostering personal growth.

Here are some of the things Multiversers get up to on their volunteering days:

“On my Multiverse volunteer days this year, I engaged in outreach work, inspiring young children to explore creative coding from an early age. I firmly believe in fostering creativity, critical thinking, problem-solving skills, and an appreciation for technology in our digital world. The joy on the faces of 5-6-year-olds as they successfully delved into coding was remarkably rewarding. This experience not only allowed me to contribute to the local community but also underscored the significance of nurturing these skills for a brighter and more innovative future.”

“Since joining Multiverse in 2021, I have used my volunteer days to serve as a panellist at International Women's Day events where I speak about my experiences as a female scientist while pursuing my PhD. I also highlight that with a growth mindset, how people can pivot to new careers, such as being a Data Coach at Multiverse!

“Unfortunately, I experienced some unpleasantries during my PhD as a woman of colour. Serving on these panels is important to me as the cause — breaking biases and empowering women — is one that is very close to my heart. After all, what does a scientist look like? I say it can look like anyone.”

“This year I took part in two volunteering days that were both career days in schools, at my old sixth form speaking to the Year 10 and Year 12 students about the power of apprenticeships and alternative routes to university. As well as the kids engaging with the Multiverse apprenticeship model, I think it was a great opportunity to meet someone who was in their shoes not so long ago, undecided about going to university.

“I feel strongly about this because back when I was in sixth form, there was so much pressure to apply to university, and if you didn’t, it seemed like you had failed. However, giving students the opportunity to understand that there’s more to life than the university pathway is something I’m passionate about; the alternative routes to learning and a career are endless, you just need to find what works for you.”

“I am a lifelong stutterer, and earlier this year a local stuttering advocate invited me to speak at a press conference at the California State Capitol in support of a resolution in the assembly to recognize stuttering awareness week.

“This was deeply meaningful for me to participate in. Growing up, I never thought I would be able to speak at press conferences, because I was embarrassed about my stutter. My interest in education actually started because of teachers earlier in my life who pushed me beyond the fear and stigma, helping me learn how to use my voice. I am grateful Multiverse supported me in pursuing this special opportunity.”

By offering all our employees volunteer days, we ensure that every Multiverser's personal values, and their desire to positively contribute to society, are recognized, supported, and celebrated as part of our company culture.

Looking for a culture like this?

We’re hiring: https://jobs.ashbyhq.com/multiverse?utm_source=vdb

Data-driven insights empower leaders to solve inefficiencies and drive increased value through measurable innovation and cost reduction. But building a data-informed culture isn’t easy.

Today, 70% of transformation initiatives fail, with each unsuccessful attempt draining resources, impacting morale, and increasing risk. So how can you build a data strategy that drives value and stands the test of time?

In this article, we’ll walk through the foundations of a successful data strategy and share insights into the latest best practices, including practical ways to align your data strategy with your business goals and increase organisational buy-in for a winning approach that takes you far into the future.

What is a data strategy?

A data strategy is a plan or framework that guides the way an organisation collects, stores, manages, analyses, and utilises data to achieve its goals and objectives. It involves defining the objectives of data usage, identifying the types and sources of data that will be collected, establishing data governance policies, defining data quality standards, and determining the technology infrastructure and tools needed to support the data strategy at a day-to-day level.

What are the types of data strategy?

There are multiple types of data strategy, including defensive data strategies focused on enhancing cybersecurity and data compliance, data integration strategies aimed at eliminating data silos, and data monetization strategies for identifying opportunities to generate revenue or create value from existing data assets.

While each type of data strategy is important, businesses are becoming increasingly focused on implementing a holistic data strategy that encompasses a variety of business goals, supported by cross-functional partnership and collaboration across the organisation.

Examples of data strategies will differ based on an organization’s specific business goals. Whatever the objective, the key is to make sure the data strategy and business strategy align.

To implement a successful data strategy, many leading organisations are focusing on three key areas — people, process, and technology.

What are the benefits of a data strategy?

A well-defined data strategy is important for making informed decisions, improving operational efficiency, identifying business opportunities, and gaining a competitive edge in a fast-paced digital era.

To remain competitive, leaders must have a data strategy that helps them face external disruptions, like economic uncertainty and the rise of AI, while meeting the growing internal demand for data-driven decision making.

Here are some of the core benefits of a modern data strategy:

• Data capabilities at every level — employees in every department can access data, use data tools and systems, ask the right questions, and collaborate effectively.
  
• Greater speed and efficiency — teams and individuals are empowered to efficiently process and visualise data, reducing time per data task.
  
• Empowered data teams — existing data scientists and analysts have more time to enhance their knowledge and develop advanced skills.
  
• Increased capacity — when business teams are empowered to self-serve, data teams can spend more time supporting strategic initiatives, reducing reliance on external support.
  
• New opportunities to drive business value — employees can use data to identify opportunities to increase productivity, decrease costs, improve the customer experience, and grow new revenue streams.

Common data strategy pitfalls

Despite the many benefits of a data strategy, businesses are finding it difficult to achieve lasting change, with only 24% of companies saying they have successfully created a data-driven culture.

There’s a common temptation for businesses to test out various elements of their data strategy through short-term transformation projects focused on utilising emerging technology, like machine learning (ML) and artificial intelligence (AI).

However, the emphasis on process and technology often comes at the expense of the people who use these tools and workflows in their day-to-day work. Research suggests 7% is the minimum “tipping point” required to achieve the positive return on investment (ROI), yet most companies engage just 2% of their workforce in transformation efforts.

To achieve the above benefits, your data strategy must include clear steps for engaging your workforce at every level.

5 elements of a successful data strategy framework

From building organisation-wide data management practices to fostering data access and cross-functional collaboration, there are many key components of a strong data strategy.

Let’s explore some of the core elements for a data strategy framework that breaks down costly data silos and paves the way for effective use of data across the organisation.

1. A unified ‘big picture’ vision

A good data strategy must be relevant to the business — otherwise, it simply won’t last.

To engage a greater percentage of your workforce, start by defining an ambitious future vision that includes every team, function and department.

Your data strategy vision may include:

• A clearly defined “Why?” to articulate benefits to both the business, plus tactics for engaging employees across different functions, geographies, and backgrounds.
• A strong answer to the question, “What’s in it for me?” that speaks directly to the individual goals and ambitions of every member of the organisation.
• A defined end state with clear milestones and outcomes to be achieved before you can call a strategic initiative “finished”.

2. Executive buy-in

Successful transformation requires strong alignment across all levels, starting at the top. Transformations are 5x more likely to succeed when senior leaders model the changes they’re asking employees to make.

However, large-scale data strategy success often feels out of reach, even for the organisation’s most visionary leaders. Of the 85% of senior leaders who have been involved in at least two major transformations in the last five years, a whopping 67% have experienced at least one underperforming transformation during this time.

Chief Data Officers (CDOs) can’t do it alone. Early problems arise when leaders disagree on the urgency of the data strategy and the proposed solution, or when they weren’t fully bought in from the start.

Here are some ways to increase executive buy-in:

• Align your data strategy to the wider business strategy.
  
• Establish clear goals backed by qualitative and quantitative data.
  
• Determine relevant business objectives and key performance indicators (KPIs).
  
• Measure your progress at each stage to maintain buy-in after initial launch.
  

Make it easy for your executive team to connect the dots between your data strategy and business strategy. Then ask for a firm commitment from the C-Suite.

3. Well-defined data architecture

In today’s digital age, there is plenty of buzz about technology and the various approaches to data architecture. But your tools are only as good as the people who use them. Without clear guidelines and a data-confident workforce to follow them, organisations end up investing in technology that yields little return on investment (ROI).

To improve the ROI on your technology investments, create a well-defined data architecture to underpin your data strategy.

Here are some key areas to consider:

• Data storage — including storage formats, backup strategies, archiving plans and any relevant requirements for real-time analytics and operations
• Data integration — including guidelines for moving raw data from data warehouses to business intelligence (BI) applications to increase analytics performance
• Data access — including guidelines for data collection from various data sources, and steps to streamline data governance without excessive user controls
• Data compliance — including strong data security and data privacy practices to protect your organisation’s data

By taking the time to create a detailed data architecture, you can alleviate the pressure on your senior data team and use data to support a variety of business use cases across the entire organisation.

4. Clear success metrics

If you’re launching a new data strategy, keep in mind that post-launch is a crucial window of opportunity for increasing the pace of activity.

To maintain momentum for your data strategy, it’s important to share regular reports on the value delivered:

• 
  Work fast to turn ideas into actionable roadmaps and back them up with key milestones that are less than a few months out.
  
• Establish common goals across teams.
  Define the metrics you’ll use to track your progress at each key stage.
  
• Update your digital transformation roadmap to include quarterly goals.
  
  

By aligning your data strategy with your core business processes, you’ll be better positioned to break existing silos and actively identify end-to-end issues and opportunities. With a clear view of what is and isn’t working — and a well-structured system for measuring your success — you and your employees will also be more likely to stay the course.

5. Commitment to skills transformation

When it comes to executing an effective data strategy, you can go much farther as a team. Yet research shows that only 25% of employees believe they have the knowledge and skills required to use data effectively. To identify these issues before they become a roadblock:

• Conduct a skills gap analysis to quantify your data skills gap, pinpoint your current strengths, and identify your future data skills needs.
  
• Quantify the cost of skills gaps, including inefficiencies or delays to key strategic projects.
• Calculate the financial benefits of closing them, such as new efficiencies and revenue-generating opportunities.

Change isn’t easy, but it starts with a firm commitment to building a culture of learning – giving employees the confidence to access, interpret, and use data insights to drive decision-making. Here are some key actions to consider:

• Provide learning opportunities to existing employees via data upskilling and reskilling to create data champions at every level.
  
• Open up alternative hiring routes for entry-level data roles, such as apprenticeships, building a robust hiring pipeline and increasing the capacity of senior data specialists.
  
• Track the business impact of skills programs as employees use their skills to identify new cost-saving and revenue-generating opportunities.

A strong data strategy will consistently reveal new opportunities to make a bigger downstream impact, while driving full-speed ahead toward the greater business strategy.

With a data-confident workforce, there is no limit. As your organisational data capabilities continue to grow, so does the potential to reach even higher.

The next stop on your data strategy roadmap

An effective data strategy empowers you to use your company’s data for the benefit of your customers, your business, and every individual within it.

Get our free data-driven digital transformation playbook, and learn  nine essential tactics  to increase your strategic success.

Staff across all functions of the university will be invited to enrol on the Data Academy, where they will study skills including analytics, AI and predictive modelling through apprenticeships.

The training will be delivered by Multiverse, a tech company focused on high-quality education and training through applied learning. Multiverse has trained more than 11,000 apprentices in areas such as software engineering and data analytics.

Two programmes will be offered on the academy. The 13-month Data Literacy programme covers the core technical skills required to transform data into insights, as well as softer skills like building narratives and presenting findings.

Meanwhile, the 15-month Data Fellowship will give apprentices the skills to clean, analyse and model data, and tell data stories to non-specialists.

The Data Academy was first launched last year, and ten members of staff are currently completing apprenticeships. Goldsmiths hopes that the additional investment in the academy will empower its staff to use data for better decision making and time savings; and ultimately making the student experience smoother.

David Minahan, Chief Information Officer at Goldsmiths, says ‘“Our five year digital transformation plan aims to develop a fully integrated data estate, enabling all of our students and staff to have personalised data dashboards and for Goldsmiths to benefit from data analytics and data driven decision making. The Goldsmiths Data Academy in partnership with Multiverse will provide the technical and data skills we need to achieve these aims”.

Matt Wedlake-Millecam is an accommodation administrator at Goldsmiths and applied to the Data Academy to find ways to streamline the accommodation services offered to students.

He said: “The most valuable aspect of the programme has actually been thinking about the way that I approach problems. The apprenticeship has definitely encouraged thinking proactively rather than reactively about the way that I do things.

“Thanks to the programme, I’m able to save hours of work by processing data automatically rather than manually. That time can be spent on ensuring student enquiries are answered quickly, therefore, improving the student experience."

Multiverse delivers world-class training in a wide range of qualifications in tech, data and engineering. Apprentices benefit from coaching with an industry expert and are supported by a thriving community with events, socials, mentoring and leadership programmes.

A typical Data Analyst salary is around £8,000 more than the average full-time UK wage. And Data Analyst is fast becoming one of the crucial jobs of the future.

Keep reading to learn more about the role and how to make data analysis a core component of your career — whether you're an entry-level professional or a fledgling junior analyst.

What is a Data Analyst?

A Data Analyst collates, cleans, and reviews raw data. In some roles, Data Analysts may use data to solve business problems and make decisions. In others, they may share insights about trends or anomalies to help their colleagues create data-driven solutions to business problems.

In either case, a Data Analyst should focus on making it easier for colleagues and stakeholders to understand complex data.

Responsibilities

A Data Analyst’s responsibilities include:

• Finding, cleaning, and organising raw data
• Spotting trends, patterns, and irregularities in data
• Making raw data easy for non-specialists to understand
• Presenting and reporting upon data insights
• Helping Business Analysts build data-led processes and systems
• Keeping up with industry standards, tools, and emerging technology like Artificial Intelligence (AI)

Skills

Data Analysts will typically have the following skills:

• Mathematical understanding: Linear algebra, statistics,and probability are crucial for data analysis.
• Problem-solving: You find ways to solve project and technical challenges.
• Technical ability: You use SQL (Structured Query Language), Microsoft Excel, and data visualisation techniques.
• Database basics: You can manage and navigate databases using database management systems (DBMS).
• Business acumen: You know and use the entire data analytics process to meet business needs.
• Communication: You can share data insights with non-specialists through verbal, visual, and written communication.

How to start (or grow) your Data Analyst career

To become a Data Analyst, you’ll need to understand data analysis basics, SQL, Excel, and more. Here’s how to get a head start — or get ahead — on your Data Analyst career path.

1. Master data analysis basics

To become a Data Analyst, you must first learn the basics of data analysis. Learning the basics won’t just help you start your career path. It will also help you answer different questions and develop more advanced techniques. Plus, you’ll have a solid foundation to build upon should you want to progress in your career.

When it comes to data analysis, understanding the two primary forms of data analytics — qualitative and quantitative — is a great place to start. You’ll want to learn what they are and their differences. Here’s a quick lesson at a glance.

Quantitative analytics uses data you can measure with numbers like costs, revenue, and projections. Meanwhile, qualitative analytics uses data you can’t measure with numbers, like data gathered from customer satisfaction surveys.
You can use qualitative and quantitative data to answer different business questions. Quantitative data will help you answer ‘how much,’ ‘how often’ and ‘how many’ queries. In contrast, qualitative data can help you discover how customers interact with a company and the ‘why’ behind specific interactions.
Pro tip: To take this further, start learning specific data analysis methods like ‘cohort analysis’ (quantitative) or ‘discourse Analysis’ (qualitative).

2. Analyse real data

The best way to learn any new skill is to go beyond theory and put learning into practice. In the case of data analytics, this means experimenting with real data sets in a controlled environment.
Think of it this way: when you first start bowling, using the bumpers means you stay in the lane as you learn. That helps you build confidence and get better over time. The ‘bumpers’ here are existing data sets, which you can use to practice different analysis techniques.
The data is real, but it has no direct consequences to an employer, which means you can experiment without ‘breaking’ anything. You can use this to build your confidence, learn to spot trends and practice more advanced data analysis.
Pro tip: To get started, download and use free governmental data sets from the Office of National Statistics (ONS). You can also find similar open data published by the central government.

3. Learn SQL

As a Data Analyst, you’ll frequently interact with databases and database management systems. So understanding the ‘language’ they speak is a must. SQL is a type of programming language that you can use specifically for relational databases.

Data Analysts will typically use SQL for querying, updating, and deleting data. This includes tasks like selecting all records from a table, updating existing data, or managing access to database objects. You can also use SQL to add quality control measures, assuring data upload quality.

Pro tip: Aside from having proficiency in SQL, you may need to learn other programming languages like Python, R, and Java. But this depends on the specifics of your role. If you’re new to SQL and programming languages, YouTube tutorials are a great entry point. Alternatively, you can find free beginner courses on sites like Codecademy.

4. Master Excel

Data Analysts use Microsoft Excel for data manipulation, simple analysis, and reporting. Excel's pivot tables and formulas are valuable tools for quick data exploration. The right Excel knowledge makes it easier to clean and organise data.

To become a Data Analyst, focus on developing these Excel skills:

• Aggregating and joining data
• Pivot tables, visualisations, and data cleaning
• Using AI within Excel to work more efficiently

Pro tip: As with SQL, plenty of YouTube videos online can help you get started with the spreadsheet software. Then, Microsoft offers Excel video training, too. You could also download free governmental data sets and experiment with formulas and pivot tables within Excel.

5. Practise data visualisation

Being a Data Analyst isn’t just about the insights you find. How you show your findings to colleagues and stakeholders also matters. Data visualisation can help you make complex data accessible to non-specialists—a vital part of the Data Analyst’s role.

You can create various data visualisations (i.e., graphs and charts) in Excel. A business intelligence tool like Tableau can also help you explore and communicate insights from data. PowerBI is another popular data visualisation tool that can take you beyond basic graphs and charts.

Pro tip: If you have Microsoft 365, you can experiment with data visualisation in Excel as part of your subscription. PowerBI and Tableau aren’t free software, but PowerBI is the most cost-effective. You can also practice basic data visualisation techniques for free using something like Google Sheets.

6. Rehearse presenting your findings

Aside from data visualisation, your presentation skills are crucial to help non-specialists understand data and insights. Some people are naturally more confident and excel when presenting. But even if that’s not you, don’t worry—presenting is a skill you can hone with practice.

To practice presenting your findings, start a personal data visualisation project. That will give you data to explore, insights to visualise, and information to share. You can present these insights in blog posts, reports, or mock presentations to family, friends, or tutors.

When you present, explain the insights you found (i.e., trends) and how you found them (i.e., your analysis process). You should also explain why your findings matter and how they impact the project.

Pro tip: Remember, your audience is non-technical and unfamiliar with data. So speak clearly without jargon and use data visualisation to break down your findings. If you want to take it beyond charts and graphs, experiment with storytelling to make your conclusions more engaging.

7. Become an apprentice with Multiverse’s Data Fellowship

Without the proper support, becoming a Data Analyst — or upleveling your data skills — can take so much longer than it has to. Choosing a university path can cost you up to £9,250 per year just for tuition fees. Not to mention the opportunity costs of taking a career break to attend a full-time programme if you're already working.

The good news: If you're a junior Data Analyst looking to progress into mid or senior-level roles, earn a promotion, or just increase your data savviness, Multiverse can help. Through our Data Fellowship or Advanced Data Fellowship, junior or new Data Analysts can earn a nationally recognized qualification on the job without having to put their careers on hold. Also, because Multiverse partners with employers to sponsor programme costs, our apprenticeships cost nothing for learners.

Our data programmes help learners deepen expertise of highly sought-after technical skills, including Python, machine learning, data governance, and more. If you're interested in learning more, fill out our quick application form.

Pro-tip for career starters: Multiverse also offers apprenticeship programmes for entry-level learners and data enthusiasts. But unlike our upskilling programmes for junior Data Analysts, you have to be accepted by an employer, not Multiverse, to do such a programme.

This path would allow you to complete a valued Multiverse apprenticeship in conjunction with your responsibilities in a new role at a company in the UK. 

The bottom line: If you're looking for your first job in data, consider searching and applying for apprenticeships in England here.

8. Build a portfolio of data analysis work

A portfolio of your data analysis work demonstrates your capabilities at every stage of your Data Analyst career. You can improve your portfolio whenever you work on a new project or advance throughout your career. Using Multiverse programmes as an example, let’s put building a portfolio into practice.

As a Multiverse Apprentice, we’ll work with you to build a strong portfolio. As an aspiring or junior Data Analyst, you might start a Data Fellowship apprenticeship. You’ll gain a portfolio of work full of real-life projects showing you understand database fundamentals and have technical skills. Your portfolio will also demonstrate relevant experience for a new role or a career progression.

You could move onto the Advanced Data Fellowship programme if you want to progress and upskill as a Data Analyst. You’ll qualify with a body of work demonstrating your capability with advanced analysis methods and ability to deliver real-world business results.

Pro tip: Besides gaining a portfolio, both programmes offer industry-recognised certifications, further showing your competence.

9. Apply for Data Analyst roles

If you’ve worked on developing relevant skills and feel confident in your abilities, it could be time to start applying for Data Analyst roles. To simplify the application process, one of the first things you should focus on is developing your CV. If you’ve never crafted a CV, you can use a CV template to help.

Here are a few expert tips to help you get started:

• Aim to use keywords from the job description—especially for the tools, skills, and programming languages. You can add these to your CV summary, work experience, and skills sections.
• Use bullet points and shorter sentences to help recruiters scan your CV at a glance.
• Try to stick to one page, two pages max.
• Using an image of yourself on a UK CV is uncommon and can distract from your skills and experience. The safest option is to avoid doing so.
• Proofread your CV for spelling and grammar errors before applying for a job.

How long does it take to become a Data Analyst?

How long it takes to become a Data Analyst depends on your chosen path, so let’s compare the apprenticeship vs. university route.

If you choose to study a Bachelor of Arts (BA) in Computer Science at university, you could spend four years in a lecture hall. That’s before you even start your job search, apply for Data Analyst vacancies, get an interview, and (hopefully) land your first role.

In contrast, when you study a relevant apprenticeship, you become a Data Analyst from the start of your programme. It’s worth noting that different apprenticeship levels have varying completion times. That said, you can get the equivalent of a full Bachelor's degree in as little as three years, depending on your course structure.

What tools do data analysts use?

Here are the standard tools that Data Analysts use in their roles:

• Database management systems: This software helps users or applications access a database and work with the data. Some popular DBMS options include MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, and SQLite.
• Data visualisation tools: PowerBI and Tableau are two common software platforms. They help Data Analysts to visualise and present data-led insights in engaging ways.
• Microsoft Excel: Data Analysts use this spreadsheet software for data entry, calculations, and analysis. You can also create visual representations of data through charts and graphs.

Grow your career as a Data Analyst with Multiverse

Data Analysts must know SQL, Excel, data visualisation techniques, and a host of other skills. The Multiverse Data Fellowship programme covers all of this and more. Plus, you’ll earn an industry-recognised qualification and build a portfolio without putting your career on hold.

At Multiverse, we make it easy for you to get started. It takes less than 15 minutes to create a profile. Our team can then double-check your eligibility and discuss apprenticeship options with your employer.

Mastercard is working with tech start-up Multiverse to deliver the courses, which offer advanced part-time training to help apprentices get better at using data, whether that’s by learning how to analyse data sets, or using data to make better decisions.

Half of the employees taking the apprenticeships are aged over 40, reflecting the increasing demand for life-long learning and mid-career training.

The initiative also supports diversity across the company and creates opportunities for those that are traditionally underrepresented. Of those that joined the first cohort, almost two thirds are women, bucking the trend of traditionally male-dominated tech and data roles.

Kelly Devine, Division President, UK and Ireland at Mastercard, said: “Digital and data analytics skills are so important for our business, whether we’re using AI to detect fraud, designing the next generation of payments, or using data to solve problems. People often have preconceptions of apprenticeships, but half of our apprentices are experienced professionals, which shows how important it is to offer training and new skills at any age or life stage.”

Marybeth Altwig, Product Management Specialist at Mastercard commented:"I've had a really positive experience on the data programme and the skills and knowledge gained have enabled me to progress my career at Mastercard. I was able to move into a new role in product management where I use my new skills on a daily basis. I've also become more productive and efficient and have been able to focus on my development."

Josh Berle, Account Management Director at Mastercard, enrolled in the first cohort and said: “The apprenticeship is giving me the opportunity to learn something new and gain really useful skills that I use every day. It’s making my job more interesting as I can use data tools more effectively and gain useful insights.

“Having time to devote to professional learning has enabled me to focus on myself in a way that I know will help me throughout my career. It's about taking some time to invest in yourself in order to be able to develop more effectively for yourself, your company and your customers."

Peppa Wise, Vice President, Go to Market at Multiverse, added: “Getting access to the best jobs of the future will depend on having the right skills, and we know that people want to access the training that will unlock those skills.

“What Mastercard is doing, through apprenticeships, is breaking down the barriers for its people to access that world-class training. Apprentices will learn in-demand skills, fully funded and while they work. And they’ll continually apply their learning, driving results for Mastercard in the process.”

Earlier this year, Mastercard was named a “Best Place to Work 2023” by Glassdoor in their Employees’ Choice Awards in the UK.

Focussing on current and new emerging digital technology, the Infrastructure Data Academy will provide apprenticeship programmes focussed on analytics, AI and predictive modelling.

The training will be delivered by Multiverse, a tech company focused on high-quality education and training through applied learning. Multiverse has trained more than 10,000 apprentices in areas such as software engineering and data analytics.

More than 75 employees from Morgan Sindall Infrastructure have been selected in the first cohort. They will have the option to enrol on one of three Multiverse programmes: the 13-month Data Literacy programme covers the core technical skills required to transform data into insights.

The 15-month Data Fellowship programme delivers best-in-class training in data analysis, data wrangling, and will give apprentices the skills to clean, analyse and model data, and tell data stories to non-specialists.

Meanwhile, the degree-level Advanced Data Fellowship will train apprentices in areas like statistical testing, data ethics, predictive modelling and data security. At the end of the programme, apprentices receive a BSc (Hons) Digital and Technology Solutions (Data Analytics).

With employees recognising the future landscape of data and the importance in their roles, more than 90% expressed an interest in improving their data skills, when surveyed.

Sarah Reid, Managing Director of the Morgan Sindall Infrastructure Highways business unit,
said: “Developing our people is at the core of our business. The Infrastructure Data Academy is part of a programme that empowers individuals to grow their skills and take the next steps on their career pathway. It also enables the business to become a digital-first organisation, creating efficiencies through new technology investments to further develop our culture around using data in everyday operations.”

Peppa Wise, VP of GTM at Multiverse, said: “Morgan Sindall Infrastructure has recognised that empowering their people with vital skills in data is good for both their individual careers, and for the business overall. And the best way to develop these skills is through applied learning, that happens on-the-job, the real-world.”

Multiverse delivers world-class training in a wide range of qualifications in tech, data and engineering. Apprentices benefit from coaching with an industry expert and are supported by a thriving community with events, socials, mentoring and leadership programmes.

Morgan Sindall Infrastructure delivers some of the UKs most complex and critical infrastructure across six core sectors of energy, water, nuclear, highways, rail and aviation for public and private customers. Working on projects and long-term frameworks, we believe in connecting people, places and communities through innovative and responsible infrastructure. Our people are our business. Through their expertise, we harness innovative ideas and approaches that enable us to safely and responsibly design and deliver resilient infrastructure upon which we all rely. Morgan Sindall Infrastructure is part of Morgan Sindall Group plc, a leading UK construction and regeneration group with revenue of over £3 billion. www.morgansindallinfrastructure.com