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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.

As more businesses experience the benefits of data-driven decision-making, the demand for advanced data solutions has soared. Tableau plays a central role in addressing this need. This popular business intelligence tool helps professionals transform raw data into actionable insights. Business leaders can use these findings to cultivate a data culture prioritising evidence-based strategies.

Mastering Tableau can improve your data analysis skills and unlock new career opportunities. This guide examines how the platform empowers businesses and professionals to make strategic decisions. We’ll also highlight practical use cases and the career benefits of learning Tableau.

What is Tableau?

Tableau is a powerful visual analytics platform that allows users to analyse data and convert it into accessible visualisations. Users can explore datasets in real time, detect trends, and organise information in stylish dashboards. These capabilities can reveal unexpected insights and help businesses make more informed decisions.

Tableau’s advanced features and intuitive interfaces have contributed to its global popularity. As of June 2024, the Tableau Community has over four million members who share advice and resources. Some users rely on Tableau to analyse data for top businesses, while others use the platform for academic research or personal projects. Regardless of your goals, Tableau has the necessary tools to manage data and visualise complex information.

Why use Tableau?

Tableau has an approximately 15% market share in the fiercely competitive data analytics space. It offers several advantages that make it a preferred choice for many business users and upskillers aiming to build their analytical abilities.

First, Tableau has an intuitive drag and drop interface to streamline the design process. This convenient feature allows users of all skill levels to build sophisticated visualisations without prior programming knowledge. For example, you can create a complex chart by dragging data fields into rows and columns. To rearrange the visualisations, simply drag and drop the elements to new locations.

Additionally, Tableau allows users to build customised and interactive dashboards. These displays showcase multiple visualisations in a centralised location. Viewers can interact with the data by clicking individual charts and graphs, applying filters, and adjusting date ranges. These features increase engagement by allowing the audience to explore datasets and trends.

For example, Steven Wexler used data from the United States Census Bureau to create a Tableau dashboard titled “Are you over the hill?” Users can adjust a slider to select their age and view how many Americans are younger and older than them. They can also filter the chart by gender for more tailored insights into the country’s age distribution. This playful dashboard personalised demographic trends by making it relevant to the audience’s life stages.

Tableau also offers real-time data analysis and reporting. Users can build data pipelines that collect information from multiple data sources. For example, you might gather data from Google Cloud and your website. Tableau’s powerful engine organises this information into a structured database, visually maps it, and extracts meaningful insights. Additionally, Tableau enables users to create reports automatically and share their findings quickly.

Top applications of Tableau

Tableau is a highly versatile data analytics platform with many practical applications. Here are three ways this tool can provide insights and help businesses develop a data driven culture.

Data visualisation for business intelligence

Business Analysts frequently need to process and understand large datasets containing confidential information. For example, they might analyse thousands of financial transactions to detect fraud.

Tableau simplifies this complex data and extracts actionable visual insights. For instance, Business Analysts might use Tableau and R to mine data from financial records and detect irregularities. They can also create customised dashboards to compare historical trends and benchmark performance.

A data driven organisation can use Tableau to make strategic choices rapidly and seize emerging opportunities. Businesses can also use this business intelligence tool to respond to crises promptly and calculate risk.

Usage across industries

People often assume that only finance and tech companies use Tableau, but that’s not true. Many industries adopt this tool for efficient data analysis and management.

Healthcare organisations use Tableau to analyse and visualise patient data and operational metrics. These applications enable them to improve both patient care and operational efficiency. For example, Guy’s and St Thomas’ Charity uses Tableau to interpret geographic and patient data. The platform provides valuable insights into the relationships between demographics, places, and health issues. The organisation uses these findings to help address childhood obesity and other prevalent health disorders in the UK.

Additionally, Tableau’s data analytics capabilities allow retailers to gain critical insights about customer and employee behaviour. For instance, Tesco’s Customer Engagement Centre uses Tableau to analyse employee productivity and proactively monitor training needs. The platform also enables the grocery chain to analyse handwritten feedback from training course attendees and identify areas for improvement.

Mobile capabilities and collaboration features

Tableau offers many collaboration tools to promote accessibility and teamwork. For example, Tableau Server and Tableau Cloud allow teams to share data and collaborate in a centralised workbook. You can also create automated subscriptions to send regular updates to your team.

Work on your visual analytics projects from any location with Tableau Mobile. This mobile application lets you explore data from your tablet or phone, even without an internet connection. Automated authentication and other cybersecurity features protect sensitive data while you use the app.

Getting started with Tableau

Tableau makes it easy to start building and exploring data visualisations. This tool integrates seamlessly with many data platforms, including:

• Cloud-based systems like AWS and Dropbox
• Customer relationship management tools, such as HubSpot and Salesforce
• Spreadsheets from Microsoft Excel and Google Sheets
• SQL databases

These integrations allow you to import existing data into Tableau and start analysing it in minutes.

Tableau also has unique data blending capabilities, enabling you to combine information from different sources for more in-depth analyses. You’ll connect Tableau to two or more data sources, define their relationships to each other, and extract insights. The platform presents the results from each source in the same visualisation, so you can quickly compare the data.

Advanced features in Tableau

As you gain confidence and experience with Tableau, expand your skills by trying these sophisticated features.

Predictive analytics and AI integration

Advanced Tableau users can leverage artificial intelligence and predictive analytics tools. For example, the AI-powered Tableau Agent uses natural language processing to perform complex calculations and suggest ways to explore data. The platform also uses linear regression to develop predictive models based on existing data. These features provide deeper insights and accelerate the data journey.

Tableau extensions

While Tableau offers a broad range of features, you’re not limited to its built-in functionalities. Third party partners provide additional tools to extend the platform’s capabilities and develop industry-specific solutions. For instance, Synchronised Refresh enables real-time collaboration by refreshing shared dashboards for all users, while EasyDesigns lets you customise your dashboards with dynamic imagery.

How to use Tableau for your career growth

At first glance, Tableau might seem like a niche tool, especially if your current role doesn’t involve data visualisation. However, employers increasingly seek job candidates who can use Tableau to tell engaging stories about data.

Analytics ranks sixth on LinkedIn’s 2024 Most In-Demand Skills List, and the job board lists over 2,000 job openings in the UK requiring Tableau expertise. These statistics highlight the tool’s growing importance in the job market. However, the Multiverse Skills Intelligence Report found that 55% of workers lack familiarity with PowerBI and Tableau skills, leading to a critical skills gap.

You can help meet this demand by learning how to use data analytics tools effectively. Luckily, there are plenty of resources to help you master Tableau and pursue new career opportunities.

Careers that use Tableau

Professionals across industries rely on visual analytics to guide decision making and drive success. Here are two roles that frequently use Tableau and other modern business intelligence tools.

Data scientist

A data scientist uses advanced programming techniques and statistical methods to gain insights from data. They use these findings to help business leaders answer questions and make informed decisions.

The typical responsibilities for data science roles include:

• Gathering information from private and published data sources, such as employee attendance records and housing data
• Cleaning raw data and organising it into a usable format
• Analysing data to detect patterns, trends, and anomalies
• Training machine learning algorithms to interpret big data and build predictive models
• Creating visualisations with Tableau and other self service analytics tools
• Developing accessible and clear reports

According to Glassdoor, Data Scientists earn an average salary of £48,362 in the UK. However, pay can vary significantly by region. For example, Data Scientists in London earn £60,164 annually on average, though this elevated salary typically reflects the city’s higher cost of living.

Data analyst

A Data Analyst collects and analyses information from various data sources, from Google Analytics to tax records. Like Data Scientists, they use the insights they gain to address business problems and support decision-making processes. However, these professionals typically don’t use advanced data analysis methods like predictive modelling.

Here are a few tasks often performed by Data Analysts:

• Finding appropriate data sources
• Collaborating with Business Analysts, Data Architects, and other experts to build data pipelines
• Preprocessing and structuring data
• Using statistical methods and software to interpret data
• Visualising data with Tableau
• Communicating insights to non technical users

Glassdoor reports that Data Analysts in the U.K. earn £34,187 per year on average. In London, the average salary for Data Analysts is £41,211.

Skills development in data visualisation

Strengthening your data analytics skills can boost your career prospects and help you transition into new analytics-related roles.

Start by developing Tableau proficiency. Visit the website to start your free trial of Tableau desktop. This offer lets you spend 14 days exploring the capabilities and applications of the visualisation tool.

Once you’ve exhausted your free trial, the platform offers several affordable pricing options for individuals. For instance, Tableau Viewer gives you access to published dashboards and visualisations designed by other Tableau users. However, you can’t create or modify dashboards.

By contrast, Tableau Explorer and Tableau Creator let you build and manage new dashboards. Tableau Creator has more functionalities, so it works best for upskillers seeking hands-on experience with the platform. More advanced packages like Tableau Server are designed for enterprises and require at least one Creator licence to deploy data visualisations. Consider asking your current employer to provide access if you want the most comprehensive features.

Other in-demand data visualisation skills include:

• Data storytelling: Use data to tell engaging and persuasive narratives
• Programming proficiency: Data professionals often use Python and R to interpret data
• SQL: A programming language used for data management and processing

Learning resources and certificates

Tableau might seem daunting at first, but the platform offers a wealth of free resources to help beginners master the basics. You can watch the free introductory video sequence, explore Tableau Starter Kits, and ask questions in the Tableau Community.

While these self-learning resources provide foundational Tableau knowledge, many people prefer the guidance of a structured curriculum. Multiverse’s upskilling programme allows professionals to study advanced data science concepts, such as data management, data visualisation, and natural language processing.

Upskillers learn to analyse data with Tableau, traditional BI tools, and programming languages. They also gain hands-on experience by completing real projects for employers and develop a diverse portfolio.

Master data science tools

Tableau is one of the most powerful data discovery tools. This platform allows businesses to analyse almost any data, from Instagram comments to sales metrics. Organisations can use the insights they gain from this platform to gain a competitive edge and develop out-of-the-box solutions.

As more businesses embrace data driven decision making, the demand for Tableau skills will continue to grow. Expand your data science knowledge with Multiverse’s upskilling program. You’ll position yourself for new opportunities by building future-proof skills like advanced analytics, data visualisation, and machine learning. Our upskillers also gain practical experience and receive individual career mentorship.

Take the next step on your exciting data journey by completing our quick application.

As you get ready, you can check the weather on your smartwatch and adjust your smart thermostat from your phone. When you drive to work, your connected car recommends the best route to avoid traffic congestion. Later, you might switch on your smart security system before going to bed.

According to IoT Tech News, 99% of UK adults have at least one smart device, and households have an average of nine IoT-enabled devices. Many businesses also use smart devices to monitor and improve their operations. Smart sensors allow manufacturers to track equipment performance in real time, while smart devices let doctors monitor patients from anywhere.

As the Internet of Things expands, the demand for skilled IoT professionals has soared. LinkedIn features UK job postings related to IOT in healthcare, sustainability, tech, and other industries.

This comprehensive guide covers everything you need to know about the Internet of Things, its applications, and future trends.

What is the Internet of Things?

The Internet of Things refers to networks of physical objects that communicate with each other and with computers through the internet. These interconnected devices share data and allow users to remotely control and monitor their environments.

A brief history of IoT

The “Internet of Things” was coined by Kevin Ashton in 1999, but this technology predates the term by two decades.

In the early 1980s, a group of enterprising Carnegie Mellon University students created the first modern IoT device. They equipped a Coca-Cola vending machine with a computer board that communicated with the ARPANET, a precursor to the internet. The students used this groundbreaking device to monitor the quantity and temperature of Coke in the vending machine.

Today, consumers and businesses use many types of IoT devices, including:

• Autonomous vehicles
• Pet tracking devices
• Smart fitness mirrors
• Smart home security systems
• Smart lights
• Smart washing machines
• Smartwatches
• Vacuum and mop robots

How does the Internet of Things work?

The Internet of Things can sound complex. You might wonder how devices can exchange data or how IoT can help users.

Let’s say you install a smart home security system from Ring. You can mount wireless security cameras around the outside perimeter of your house, while alarm sensors get placed inside on doors and windows. These IoT devices connect to the internet for continuous monitoring.

You can access and control these devices remotely through a smartphone app. Say an outdoor camera detects motion in your backyard. The device sends a notification to your phone, and you open the application to view live footage from the camera. If you spot an intruder, you can sound the siren remotely to startle them and contact the police through the app.

The Ring system also allows you to automate tasks. For example, you can schedule wireless flood lights to turn on at 7 pm before you leave work. Tech-savvy users can also integrate their Ring system with Slack, Evernote, and other platforms. You could create an automatic workflow that sends a Slack notification or pauses your iRobot vacuum when someone rings your video doorbell.

An IoT network like the Ring security system offers many benefits. Connected devices provide peace of mind by allowing you to monitor and control them from afar. They also increase convenience and help people with disabilities navigate the world more independently.

Top applications for IoT in business and consumer markets

You might assume that only tech firms use the Internet of Things, but that’s not the case. This innovative technology impacts individuals and businesses across virtually all industries. Here are some of the most popular IoT applications and their benefits.

Business applications

Companies can use IoT devices to streamline and improve business processes. Common applications include:

• Smart manufacturing - Many manufacturers equip their machinery with computer vision systems, smart sensors, and other IoT devices. These tools collect vast amounts of data and provide insights into the equipment’s performance. Businesses can use this information to predict maintenance issues and improve quality control. For instance, Rolls-Royce uses IoT sensors to track manufacturing processes in its smart factories.
• Supply chain management - Businesses can use IoT devices to track inventory across supply chains and improve logistics. IoT sensors allow supply chain partners to track a shipment’s movements and condition. Additionally, many retailers use smart shelf technology to monitor in-store inventories and automatically order products when stock runs low.
• Healthcare - Internet connected devices allow medical professionals to monitor patients remotely. For example, wearable devices like glucose monitors and smartwatches collect real-time health data and transmit it to healthcare providers. Doctors can use this information to develop personalised care plans.
• Retail - Many stores use industrial IoT devices to improve customer experiences and optimise operations. For instance, Tesco is piloting a scan-free self checkout system that automatically detects products in customers’ carts. This technology enables users to pay for their items without scanning them, saving time and eliminating self checkout frustrations.
• Energy management - UK utility companies use smart grids to improve sustainability and reduce waste. These grids include communication networks, metres, systems, and other devices. Utility companies use these IoT technologies to gather real-time data on energy consumption and optimise energy usage.

Consumer applications

Tech companies have developed a broad range of affordable consumer IoT devices, such as:

• Smart home devices - Individuals can use IoT devices to transform their homes into interactive tech hubs. Smart thermostats and lights allow users to create the perfect environment from their smartphones. Similarly, smart security systems let homeowners monitor their property remotely for enhanced safety. Consumers can even use smart fridges to check their butter’s expiration date or see if they have milk at home while grocery shopping.
• Wearable devices - Fitness trackers and smartwatches let consumers carry IoT technology wherever they go. These objects track activity and health data, such as heart rate and number of steps taken. This information helps users set fitness goals and monitor their overall well-being. Many wearable devices also connect with smartphones to receive notifications and texts.
• Connected cars - Vehicles increasingly use IoT systems to protect drivers and improve performance. For instance, IoT devices can collect and transmit data about fuel consumption, tire pressure, and other critical metrics. This information helps service centres make more accurate diagnoses and recommend predictive maintenance. Meanwhile, infotainment systems let drivers control smart devices at home and access traffic data. The popularity of connected cars will likely continue to grow due to the recent passage of the Automated Vehicles Act, which will enable self-driving vehicles to drive on British roads beginning in 2026.
• Personal health devices - Many IoT devices empower users to monitor their health without frequent doctor visits. For example, smart scales track weight loss, while smart pill dispensers remind users to take their medications regularly.

IoT and edge computing

Edge computing is a distributed computing framework that processes and stores data as close to the source as possible. This approach contrasts with traditional cloud computing, which centralises data management in remote facilities.

IoT systems often use edge computing to process data on local devices or as close to the edge of the network as possible. This method reduces latency, allowing IoT devices to provide outputs almost instantly. This efficient data transfer is especially beneficial for devices that need to detect stimuli quickly, such as autonomous vehicles and medical monitors. Combining IoT and edge computing also saves money by reducing bandwidth usage.

Edge computing is still an emerging technology, with many applications still in the early stages. For example, BT Group recently launched a Narrowband Internet of Things (NB-IoT) system in the UK. This network will enable the creation of smart cities by connecting to smart street lighting, underground water sensors, and other IoT devices. By using edge computing to process data locally, the system will increase energy efficiency and detect infrastructure issues earlier.

Top companies in the IoT space

Many businesses are dedicating significant resources to developing innovative IoT devices. Here are a few industry leaders driving advancements in connectivity technologies.

Global tech giants

Several large tech companies have invented revolutionary IoT applications and devices.

Google has developed Google Nest to give consumers more control over their homes. This comprehensive automation system includes smart doorbells, security cameras, speakers, and thermostats. Approximately one in four smart home users in the UK use Google Nest.

By contrast, Amazon’s AWS IoT platform targets commercial clients. It gives businesses the tools to create scalable IoT applications and manage data. For example, Centrica partnered with AWS to develop its Hive smart home devices.

Microsoft Azure also offers an IoT platform for organisations. Companies can connect millions of devices to the Azure IoT Hub and manage data efficiently. Microsoft has also supported the expansion of other IoT ecosystems. In 2024, for instance, the company announced that it would become an equity investor in Vodafone’s managed IoT platform as part of a $1.5 billion (roughly £1.138 billion) deal to expand internet connectivity solutions across Europe and Africa.

UK-specific IoT companies

UK companies have also pushed the boundaries of IoT innovation across many industries.

BT Group has made significant contributions to the Internet of Things. For example, the company recently filed a patent for a computer-implemented security method to prevent cybersecurity attacks on IoT devices. BT has also partnered with Nokia and MediaTek to trial 5G Reduced Capability (RedCap) technology. This cutting-edge technology simplifies 5G IoT devices to conserve battery life and decrease bandwidth requirements.

Arm Holdings is another UK-based company driving innovation in the global IoT market. The company is known for adding artificial intelligence (AI) and machine learning capabilities to IoT devices to improve performance and efficiency. For example, it has designed innovative IoT chips that aim to power AI on even the smallest IoT devices.

The future of IoT and careers in the industry

The Internet of Things industry has experienced rapid growth in the past decade, and this expansion shows no sign of slowing down. The annual revenue generated by the UK IoT market is projected to nearly double in the next five years, rising from £3.88 billion in 2024 to £6.36 billion in 2029.

Several factors are driving this expansion, including:

• Smart city initiatives - Several UK cities are building IoT networks to drive progress and support growing populations more effectively. For instance, Manchester’s Bee Network uses smart technology to integrate a sustainable transport system comprising buses, trams, bicycles, and walking routes.
• 5G innovation - In 2023, the UK created a £40 million fund to help create 5G Innovation Regions. These areas will build stronger wireless networks to accelerate IoT deployments.
• Artificial intelligence - The Artificial Intelligence of Things (AIoT) is one of the latest innovations in the IoT industry. This term refers to consumer and industrial IoT devices with AI capabilities. These smart devices use AI to collect data and make strategic decisions without human intervention. For example, smart cities use AIoT devices to automatically adjust street lights based on traffic flow, reducing energy consumption.

Common job titles and roles in IoT

The growth of smart devices will lead to new career opportunities for people with IoT and AI skills. Here are five in-demand roles to explore.

IoT Solutions Architect

An IoT Solutions Architect designs and implements IoT devices that address business needs. They work closely with stakeholders to develop tailored solutions that fit the organisation’s existing infrastructure and goals.

This role requires a combination of interpersonal and technical skills, including:

• Communication - Understand stakeholder needs and explain IoT concepts to diverse audiences
• Problem-solving - Develop innovative solutions to business challenges
• Programming languages - Proficiency in JavaScript, C++, or Python

Some IoT Solutions Architects become Product Managers and oversee the development of IoT devices. Others pursue careers as IoT Consultants or Chief Technology Officers.

IoT Software Developer

An IoT Software Developer builds and maintains IoT platforms and software. Some professionals specialise in industrial devices, such as pressure and proximity sensors for machinery. Others develop consumer devices like smart thermostats and clothing.

To pursue this career path, you’ll need these essential skills:

• Hardware knowledge - Understand how to integrate physical devices and software to design cohesive IoT systems
• Machine learning - Use machine learning algorithms to process and analyse IoT data
• UX design - Create accessible, user-friendly interfaces for IoT devices

IoT Product Manager

An IoT Product Manager oversees every stage of IoT product development, from ideation to maintenance. They collaborate with stakeholders to define the product vision and establish technical specifications. They also coordinate with IoT Software Developers to manage the product lifecycle and troubleshoot issues.

Here are a few essential skills for IoT Product Managers:

• Customer engagement - Work closely with clients to understand their needs and deliver IoT products that meet their expectations
• Leadership - Direct cross-functional teams and shape the product development strategy
• Market research - Analyze IoT industry trends, identify gaps, and develop competitive products

Data Analyst for IoT

Data Analysts build pipelines to collect data from IoT devices and transform it into meaningful insights. They use these findings to help business leaders make data-driven decisions.

For example, a Data Analyst might analyse sensor data from smart factories to forecast equipment failures. This analysis allows the organisation to develop a proactive maintenance plan, improving efficiency and safety.

If you want to become a Data Analyst, focus on developing these skills:

• Data visualisation - Use Google Charts, Tableau, and other software to translate complex datasets into accessible charts and maps
• Python and R - These programming languages allow you to perform advanced statistical analysis
• Structured Query Language (SQL) - Build, manage, and extract information from structured databases

IoT Security Specialist

The consumer group Which? estimates that smart devices can experience over 12,000 hacking or scanning attacks per week. An IoT Security Specialist implements cybersecurity measures to protect IoT devices from these threats. These protocols help safeguard sensitive data and maintain device integrity.

Essential skills for an IoT Security Specialist include:

• Communication - Educate non-tech experts about IoT cybersecurity best practices
• Penetration testing - Use ethical hacking tools to try to infiltrate IoT systems and identify security weaknesses proactively
• Risk assessment - Detect potential vulnerabilities in IoT systems and develop strategies to mitigate these risks

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The new apprenticeship will equip staff members with increased technical skills and broader knowledge to more confidently navigate the data landscape and use data better within their jobs.

The intention is that this more effective use of information across the company will help improve handling of disruption, enhance customer feedback analysis, streamline information processing and help the prioritisation and delivery of service improvements shaped directly by passenger feedback and performance data.

The apprenticeship marks the latest step in Greater Anglia’s drive to upskill its workforce via learning and apprenticeship opportunities, which it offers to all its employees regardless of age or where they are in their careers. The company, which operates trains across East Anglia, has seen over 260 employees complete apprenticeships across all areas of the business since 2017.

This new apprenticeship began this summer. Greater Anglia initially enrolled seven staff from its Safety, Security & Sustainability, Commercial and Train Service Delivery departments. A second cohort will begin the specialist training later this month from Greater Anglia’s Retail Systems, Train Service Delivery, Procurement, Safety, Security & Sustainability and Engineering departments.

Training is being delivered by Multiverse, a tech company delivering high-quality training through applied learning. Multiverse has trained more than 16,000 apprentices in data and digital skills since 2016.

Following a skills assessment by Multiverse, a total of six employees will undertake the Data Insights for Business Decisions programme. This will equip the group with the technical skills and knowledge to confidently navigate the data landscape. Nine other employees will complete the 13-month Data Fellowship. This is a level-4 apprenticeship designed to upskill data-literate colleagues into high-performing analysts and data science professionals. The final course, which will be undertaken by two employees, is the degree-level Advanced Data Fellowship, giving them enhanced skills and tools to build data analytics capabilities within the organisation and use data to inform decision-making.

Mark McClure, Marketing and Digital Analyst, who is undertaking the level 4 data fellowship course, said:

“The apprenticeship has made me think about how we use data and will improve the reports I create.

“I have already learnt a lot within the first two modules regarding analysis and management and look forward to enhancing my skills in data visualisation.”

Leon Kong, Data Strategy Manager at Greater Anglia said:

“Data is integral to Greater Anglia’s aspirations to continually give people greater journeys.

“This partnership with Multiverse offers a valuable opportunity for our colleagues to deepen their data expertise, driving forward our capabilities and operational efficiency.”

Viktorija Novikova, Early Careers and Apprenticeships Manager at Greater Anglia said:

“The apprenticeship partnership with Multiverse opens up the world of improving our data use and making our colleagues data experts for the first time in a structured way.

“We offer apprenticeships to all colleagues that are relevant to their work as part of continuous career development and talent succession planning and this opportunity is the latest step in this. We are excited to see the results in the coming months from everyone on the course.”

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:

“This partnership marks an opportunity for Greater Anglia to lead across the industry, as it looks to utilise data for better service provision and environmental credentials.

"We’re excited to work with the team as we collaboratively integrate data skills into a business relied on by millions daily for environmentally conscious, reliable travel.”