Matthew Willie: Data to AI Maturity

Hear from Matthew Willie, the CEO and Founder of 9X5, as he delves into the significance of data within organisations and discusses how organisations can cultivate a data-driven culture.

Data Visualisation: Communicating Insights through Compelling Visuals

In today’s digital age, data has become the lifeblood of businesses across the globe. However, the sheer volume of information can be overwhelming. To make sense of this data deluge, organisations turn to data visualisation, a powerful tool that transforms complex data sets into clear, comprehensible visuals. In this article, we will explore the significance of data visualisation and how it plays a pivotal role in conveying insights effectively.

Unleashing the Potential of Data Visualisation

Data visualisation is more than just creating pretty charts and graphs. It is the art of translating raw data into visuals that tell a story. By harnessing the power of visual elements, data visualisation simplifies intricate data and enables viewers to grasp the underlying message quickly. Let’s delve into why this matters.

Enhancing Decision-Making

In the business world, informed decision-making is paramount. Data visualisation empowers decision-makers with a clear understanding of trends, patterns, and outliers. By presenting data in a visual format, it becomes easier to identify opportunities and threats, ultimately leading to smarter choices.

Engaging Stakeholders

In an era where attention spans are dwindling, captivating visuals are the key to engaging your audience. Data visualisation can transform a dull presentation into an engaging experience. It ensures that your message not only reaches your audience but also resonates with them.

Simplifying Complexity

Data can often be overwhelming, filled with numbers and statistics that seem indecipherable. Data visualisation simplifies complexity by breaking down information into digestible portions. This makes it accessible to individuals who may not have a deep understanding of the subject matter.

Promoting Transparency

Transparency is a cornerstone of trust in today’s business environment. Data visualisation allows you to present information in a transparent manner. When stakeholders can see the data and the insights it holds, it builds credibility and fosters trust.

The Elements of Effective Data Visualisation

To harness the full potential of data visualisation, it’s essential to incorporate certain elements that ensure your visuals are both impactful and informative:

Choose the Right Visualisation Type
The type of data you are working with should dictate the visualisation method you choose. Whether it’s bar charts, pie graphs, or heatmaps, selecting the appropriate format is crucial for effective communication.

Keep it Simple
Less is often more in data visualisation. Avoid cluttering your visuals with unnecessary elements. A clean and simple design will enhance the viewer’s understanding.

Use Colour Wisely
Colour can be a powerful tool, but it should be used sparingly and purposefully. Colour coding can help highlight key points or trends within your data.

Add Context
Context is vital for understanding data. Provide explanations, labels, and captions to ensure that viewers comprehend the significance of what they are seeing.


In conclusion, data visualisation is not just about creating eye-catching graphics; it’s a strategic tool for conveying insights and driving decision-making. In a world where data is abundant, the ability to transform it into meaningful visuals is a skill that can set you apart. Embrace the power of data visualisation, and you’ll find yourself not only communicating more effectively but also making better-informed decisions.

Implementing Artificial Intelligence and Machine Learning in Higher Education

In today’s rapidly evolving world, technology has become an integral part of our daily lives. Higher education institutions are recognising the importance of embracing technological advancements to enhance teaching and learning experiences, streamline administrative processes, and drive innovation.

One of the most significant technological advancements in recent years is the rise of Artificial Intelligence (AI) and Machine Learning (ML). AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, while ML focuses on enabling computers to learn from data and improve performance over time.

Understanding Artificial Intelligence and Machine Learning

Artificial intelligence encompasses a broad range of techniques and technologies that enable machines to exhibit intelligent behaviour, such as speech recognition, decision-making, and problem-solving.

Machine learning is a subset of AI that allows computers to learn and make predictions or decisions without being explicitly programmed. It involves the development of algorithms that learn from data and adapt their behaviour based on patterns and insights.

Artificial Intelligence and Machine Learning are closely related, with ML being a crucial component of AI. ML algorithms enable AI systems to analyse vast amounts of data, identify patterns, and make informed decisions or predictions.

Artificial Intelligence and Machine Learning have already made significant advancements in various industries, including healthcare, finance, transportation, and entertainment. These technologies have demonstrated their potential to drive efficiency, enhance decision-making, and improve user experiences.

The Potential of AI and ML in Higher Education

Personalised Learning Paths
AI and ML algorithms analyse students’ learning patterns, preferences, and strengths to develop individually tailored learning paths. This individualised approach promotes engagement, improves retention, and maximises learning outcomes.

Intelligent Tutoring Systems
Intelligent tutoring systems leverage Artificial Intelligence and Machine Learning to provide students with personalised feedback, guidance, and support. These systems adapt to individual learning needs, identify areas of improvement, and offer targeted resources and interventions to students and their teachers.

Adaptive Assessments
AI-powered adaptive assessment systems use Machine Learning algorithms to tailor assessments to individual students. These assessments adapt in real-time based on the student’s responses, providing a more accurate and comprehensive evaluation of their knowledge and skills.

Student Enrolment and Registration
Artificial Intelligence and Machine Learning automate and streamline student enrolment and registration processes, reducing the administrative burden and enhancing efficiency. Chatbots and virtual assistants powered by AI provide instant support and guidance to students during enrolment.

Timetable Scheduling
Scheduling courses and timetables for students and faculty can be a complex task. AI algorithms analyse various factors, including course availability, student preferences, and resource constraints, to generate optimised schedules that minimise conflicts and maximise utilisation.

Predictive Analytics for Student Success
AI and Machine Learning techniques analyse historical data and student performance indicators to predict students at risk of dropping out or facing academic challenges. Early identification allows institutions to provide targeted interventions and support, improving student success rates.

Automated Data Collection and Processing
Artificial Intelligence and Machine Learning automate data collection and processing tasks, reducing the time and effort required for researchers. This automation enables faster and more accurate analysis of large datasets, facilitating breakthrough discoveries and insights.

Pattern Recognition and Insights
Machine Learning algorithms excel at identifying patterns and trends within complex datasets. In higher education, these algorithms are used to uncover hidden insights, such as factors influencing student performance or emerging research topics.

Predictive Modelling in Research
AI and Machine Learning enable predictive modelling, which aids researchers in making accurate predictions or simulations. This capability contributes to various fields, such as climate change, economics, and healthcare.

Challenges and Ethical Considerations

Data Privacy and Security
Implementing AI and ML in higher education necessitates careful attention to data privacy and security. Institutions must establish robust protocols to safeguard student and faculty data, ensuring compliance with relevant regulations and ethical standards.

Bias and Fairness in AI and ML Algorithms
Furthermore, Artificial Intelligence and Machine Learning algorithms inadvertently perpetuate biases present in the data they are trained on. Institutions must address biases, ensure fairness, and strive for transparency in algorithmic decision-making to avoid perpetuating inequalities.

Skills Gap and Workforce Readiness
The successful integration of AI and ML in higher education requires a workforce equipped with the necessary skills. Institutions need to invest in faculty training and development programs to ensure educators are proficient in leveraging these technologies for effective teaching and learning.

Ensuring Ethical AI Use in Education
Higher education institutions must establish ethical guidelines and frameworks to govern Artificial Intelligence and Machine Learning. Responsible and transparent practices are essential to promote trust, fairness, and accountability in the application of these technologies.

For more resources and information on incorporating Artificial Intelligence in higher education, including good practice advice, visit the TEQSA resources page.

Implementing AI and ML in Higher Education Institutions

Unsure where to start? Our Analytics Capability Readiness will help you identify the steps required to leverage the benefits of AI and Machine Learning in your organisation.


Artificial Intelligence and Machine Learning have the potential to revolutionise higher education by enhancing learning experiences, optimising administrative tasks, and advancing research capabilities.

However, the successful implementation of these technologies requires addressing challenges related to data privacy, bias, skills readiness, and ethical considerations. By embracing Artificial Intelligence and Machine Learning responsibly and ethically, higher education institutions harness their transformative power and drive innovation in teaching, learning, and research.

Embrace the future of higher education with Artificial Intelligence and Machine Learning – Contact us to see how 9X5 can assist you in starting your journey today.

Evaluating the Effectiveness of Business Initiatives using Data

Evaluating the effectiveness of business initiatives is crucial for achieving success in any organisation. One of the most effective ways to do this is by using data to inform decision-making. By implementing a data-driven approach, businesses can gain valuable insights that can help them make better decisions and improve performance.

Key Reasons Why Evaluating the Effectiveness of Business Initiatives using Data is Important

  1. Measuring Performance:
    Data analytics allows businesses to measure the effectiveness of their initiatives and strategies. By setting performance metrics and tracking progress, businesses can determine whether their efforts are producing the desired results. This helps to identify areas for improvement and make necessary adjustments.
  2. Identifying Trends:
    Data analytics enables businesses to identify trends and patterns in their operations. This information can be used to make more informed decisions and take proactive measures to address issues before they become major problems. By identifying trends early on, businesses can stay ahead of the curve and avoid costly mistakes.
  3. Making Informed Decisions:
    By using data to inform decision-making, businesses can make more informed choices about which initiatives to pursue and how to allocate resources. This helps to maximise the effectiveness of business operations and drive growth.
  4. Improving Communication:
    Data analytics also facilitates better communication between different teams and departments within an organisation. By sharing data insights and analysis, teams can collaborate more effectively and make decisions that align with the overall goals of the business.


In conclusion, evaluating the effectiveness of business initiatives using data is essential for achieving success in any organisation. By leveraging the power of data analytics, businesses can measure performance, identify trends, make informed decisions, and improve communication. These benefits can help businesses stay competitive and achieve long-term growth.

Why Your IT Department Needs a Business Intelligence Strategy

In today’s rapidly evolving business landscape, data has become a crucial asset for companies of all sizes. With the increasing amount of data generated by businesses, it has become imperative for companies to have a robust business intelligence (BI) strategy in place to make informed decisions. While most companies recognise the importance of BI, many fail to realise the significance of involving their IT department in this process. In this article, we will discuss why your IT department needs a business intelligence strategy and how it can benefit your organisation.

Why Do You Need a Business Intelligence Strategy?

  1. Ensures Data Quality and Security
    First, one of the primary responsibilities of the IT department is to ensure the quality and security of data within the organisation. By involving the IT department in the BI strategy, companies can ensure that their data is accurate, consistent, and reliable. IT professionals have the technical expertise and knowledge to implement data governance policies and ensure that data is stored and managed securely.
  2. Streamlines Data Integration
    Second, data integration is a critical component of any BI strategy. It involves combining data from various sources and transforming it into a format that can be easily analysed. The IT department is responsible for managing the company’s IT infrastructure and has the expertise to handle data integration tasks. By involving the IT department in the BI strategy, companies can streamline data integration processes, reduce errors, and improve data accuracy.
  3. Enables Efficient Data Management
    Third, effective data management is essential for the success of any BI strategy. The IT department can play a significant role in this process by implementing data management policies, creating data models, and designing data architectures. By involving the IT department in the BI strategy, companies can ensure that data is managed efficiently, and the organisation has access to the data they need to make informed decisions.
  4. Facilitates Effective Use of BI Tools
    Finally, the IT department has the technical expertise to evaluate and implement BI tools that are best suited for the organisation’s needs. They can also provide training to users on how to effectively use these tools to analyse data and make informed decisions. By involving the IT department in the BI strategy, companies can ensure that their investment in BI tools is maximised, and employees have the necessary skills to use these tools effectively.


In conclusion, involving the IT department in the BI strategy is crucial for companies that want to make informed decisions based on data. IT professionals can ensure that data is accurate, secure, and managed efficiently, and they have the technical expertise to implement BI tools that best meet the organisation’s needs. By involving the IT department in the BI strategy, companies can maximise their investment in BI, improve data accuracy, and gain a competitive advantage in the marketplace.

Moreover, if you’re a small business owner, you might think that implementing a BI strategy is only for large enterprises. However, with cloud-based BI tools, small businesses can also benefit from BI by gaining insights into their operations, customers, and competition. Investing in BI is an investment in the future of your business, and involving your IT department in the process can help you achieve success.

Power-Up Your Marketing Analytics with These Business Intelligence Tips and Tricks

In today’s fast-paced business world, marketing analytics is critical for businesses looking to gain a competitive edge. It provides insights into customer behaviour, preferences, and trends, allowing businesses to tailor their marketing efforts and improve their ROI. However, without the right tools and strategies in place, marketing analytics can be overwhelming and challenging to manage. This is where business intelligence (BI) comes in. BI tools and techniques can help businesses unlock the full potential of their marketing analytics data. In this article, we’ll explore four ways to power up your marketing analytics with BI tips and tricks.

Our Marketing Analytics Tips and Tricks

  1. Streamline Data Integration
    Data integration is the process of combining data from different sources into a single, unified view. It is a crucial step in the marketing analytics process, as it allows businesses to see the big picture and make informed decisions. However, data integration can be complex and time-consuming, especially for businesses with multiple data sources. BI tools can simplify this process by automating data integration and ensuring data accuracy. By streamlining data integration, businesses can save time and resources and make better use of their marketing analytics data.
  2. Visualise Your Data
    Data visualisation is a powerful BI technique that can help businesses make sense of their marketing analytics data. It involves presenting data in a visual format, such as graphs, charts, and maps, to help users identify patterns and trends quickly. Data visualisation can also help businesses communicate their findings to stakeholders more effectively. By using data visualisation tools, businesses can gain insights into customer behaviour, campaign performance, and market trends. This can help them make data-driven decisions and improve their marketing ROI.
  3. Embrace Predictive Analytics
    Predictive analytics is an advanced BI technique that uses statistical algorithms and machine learning to analyse historical data and predict future outcomes. In marketing, predictive analytics can help businesses forecast customer behaviour and identify opportunities for growth. For example, predictive analytics can help businesses identify which customers are most likely to churn, allowing them to take proactive steps to retain them. By embracing predictive analytics, businesses can stay ahead of the curve and make informed decisions based on data-driven insights.
  4. Focus on Data Quality
    Data quality is essential for accurate and reliable marketing analytics. Poor data quality can lead to incorrect conclusions and misguided decisions, which can harm a business’s bottom line. BI tools can help businesses maintain data quality by providing data governance and data cleansing features. Data governance ensures that data is accurate, consistent, and reliable, while data cleansing removes duplicate or outdated data. By focusing on data quality, businesses can ensure that their marketing analytics data is accurate and actionable.


In conclusion, business intelligence can help businesses power up their marketing analytics and gain a competitive edge. By streamlining data integration, visualising data, embracing predictive analytics, and focusing on data quality, businesses can unlock the full potential of their marketing analytics data. As a small business owner, it’s important to consider the benefits of BI and incorporate it into your marketing analytics strategy. By doing so, you can make informed decisions, improve your ROI, and stay ahead of the competition.

Why Small Businesses Should Invest in Business Intelligence and Analytics Tools

Small businesses often have limited resources, making it challenging to compete with larger corporations. However, technology has levelled the playing field, and business intelligence and analytics tools have become affordable and accessible for small businesses. In this article, we will discuss why small businesses should invest in business intelligence and analytics tools.

Why Do I Need Data Analytics?

  1. Improving Decision-Making
    Small businesses often face numerous challenges and must make informed decisions to survive and grow. Business intelligence and analytics tools can help small businesses access valuable insights that can guide them in making better decisions. By using data to identify trends and patterns, small businesses can make informed decisions about inventory management, marketing strategies, and other critical aspects of their operations. For example, a small business can analyse sales data to determine which products are selling well and which ones are not. This data can then be used to adjust the inventory, create targeted marketing campaigns, and improve customer satisfaction.
  2. Reducing Costs
    Small businesses often operate on a tight budget, making finding ways to reduce costs essential. Business intelligence and analytics tools can help small businesses identify areas where they can save money. By analysing data, small businesses can identify inefficiencies in their operations and find ways to reduce costs. For example, a small business can analyse shipping data to determine the most cost-effective way to ship products to customers. They can also analyse data from their supply chain to identify opportunities to negotiate better prices with suppliers.
  3. Enhancing Customer Experience
    Small businesses need to provide excellent customer service to compete with larger corporations. Business intelligence and analytics tools can help small businesses provide better customer service by analysing customer data. By analysing customer data, small businesses can identify trends and patterns in customer behaviour. This data can then be used to create targeted marketing campaigns, personalised offers, and other initiatives that can improve customer satisfaction.
  4. Staying Competitive
    Small businesses need to stay competitive to survive and grow. Business intelligence and analytics tools can help small businesses stay competitive by providing valuable insights into their operations and the market. By analysing data, small businesses can identify industry trends and benchmark their performance against their competitors. This data can then be used to adjust their strategies and stay ahead of the competition.


Small businesses often face numerous challenges and must make informed decisions to survive and grow. Business intelligence and analytics tools can help small businesses access valuable insights that can guide them in making better decisions. By improving decision-making, reducing costs, enhancing customer experience, and staying competitive, small businesses can use business intelligence and analytics tools to their advantage.

How to Choose the Right Business Analytics Tool for Your School

In today’s data-driven world, educational institutions are under increasing pressure to make informed decisions quickly. To achieve this, they need access to reliable data and powerful analytics tools that can help them make sense of this information. But with so many business analytics tools on the market, how do you choose the right one for your school? In this article, we’ll discuss some of the factors to consider when choosing a business analytics tool and take a look at Domo as a potential solution for your school.

What to Look for in Analytical Tools

  1. Ease of Use
    One of the most important factors to consider when choosing a business analytics tool is ease of use. The tool you choose should be intuitive and user-friendly, so that staff can quickly learn how to use it without requiring extensive training. Domo is known for its user-friendly interface, making it easy for staff to access the data they need and generate reports with just a few clicks.
  2. Integration Capabilities
    Another factor to consider is integration capabilities. The tool you choose should be able to integrate with other systems and applications that your school is already using, such as your student information system or learning management system. Domo’s cloud-based platform is designed to integrate with a wide range of systems and applications, making it a versatile choice for schools, it also has thousands of ready-to-use connectors to ensure integration of all your data sources.
  3. Scalability
    As your school grows and your data needs expand, you need a business analytics tool that can scale with you. Domo is a cloud-based platform that can scale to accommodate even the largest data sets, ensuring that your school always has access to the information it needs to make informed decisions.
  4. Customisation
    Every school is unique, and the business analytics tool you choose should be able to accommodate your school’s specific needs. Domo offers a high degree of customisation, allowing you to create customised dashboards and reports that meet your school’s specific requirements.
  5. Security
    Finally, when choosing a business analytics tool, security is a critical consideration. Your school’s data must be protected from unauthorised access and breaches. Domo offers robust security features, including data encryption, user authentication, two-factor authentication, and access controls, ensuring that your school’s data is always protected and never falls into the wrong hands.


To conclude, choosing the right business analytics tool is critical for educational institutions. When considering factors like ease of use, integration capabilities, scalability, customisation, and security, Domo emerges as a strong contender. With its user-friendly interface, integration capabilities, scalability, customisation options, and security features, Domo can help schools make informed decisions based on reliable data.

The Rise of Data Analytics: What Job Seekers Need to Know

Data analytics has emerged as one of the fastest-growing fields in recent years. As companies continue to collect and generate vast amounts of data, there is a growing need for professionals who can make sense of this information and provide insights that can drive business decisions.

What You Need to Know

  1. Skills Required
    To succeed in data analytics, you need to have a strong foundation in statistics, data management, and data visualisation. You should also be familiar with programming languages such as Python, R, and SQL, as well as data analysis tools like Domo and Power BI. Soft skills like critical thinking, problem-solving, and communication are also essential in this field.
  2. Types of Data Analytics Jobs
    There are different types of data analytics jobs, including data analysts, data scientists, business intelligence analysts, and machine learning engineers. Data analysts are responsible for collecting, processing, and analysing data to identify trends and patterns. Data scientists go beyond this by building predictive models and algorithms. Business intelligence analysts focus on using data to support decision-making, while machine learning engineers develop algorithms and models that enable computers to learn from data.
  3. Industries Hiring Data Analytics Professionals
    Data analytics professionals are in high demand across many industries, including healthcare, finance, e-commerce, and marketing. Healthcare organisations use data analytics to improve patient outcomes and reduce costs. Financial institutions use it to detect fraud and identify investment opportunities. E-commerce companies use it to personalise customer experiences and improve conversion rates. Marketing teams use data analytics to measure campaign effectiveness and optimise customer acquisition and retention.
  4. Certifications and Education
    While a bachelor’s degree in a relevant field like computer science, statistics, or mathematics is typically required for entry-level positions, many employers also value industry certifications. Certifications like Certified Analytics Professional (CAP), SAS Certified Data Scientist, and Microsoft Certified: Azure Data Scientist Associate can help job seekers stand out in a crowded job market. It’s also important to continue learning and staying up-to-date with the latest trends and technologies in data analytics.


Data analytics is a fast-paced and exciting field with many opportunities for growth and career advancement. Job seekers who possess the right skills, education, and certifications can thrive in this field and make a meaningful impact on the organisations they work for.

So what’s stopping you from pursuing a career in data analytics?

How to Build a Successful Career in Data Analytics

Data analytics and business intelligence are two of the most promising career fields in today’s job market. With the explosion of data-driven decision making across industries, companies are seeking professionals who can analyse and interpret data to make strategic business decisions.

Our Tips and Tricks

If you’re looking to build a successful career in data analytics or business intelligence, here are some tips to help you get started:

  1. Develop your technical skills
    To succeed in this field, you need to have a strong foundation of technical skills such as data analysis, database management, statistical analysis, and programming languages like Python or SQL. There are many online courses and certification programs available that can help you develop these skills.
  2. Gain industry-specific knowledge
    While technical skills are important, it’s equally important to have industry-specific knowledge. You should have a good understanding of the industry or industries you’re interested in working in, as well as the types of data that are important in those industries. For example, if you’re interested in working in manufacturing, you should have knowledge of machine run-times and maintenance reports.
  3. Build a portfolio
    One of the best ways to demonstrate your skills to potential employers is by building a portfolio of your work. This could include examples of data analysis projects you’ve completed, reports you’ve created, or dashboards you’ve built. A strong portfolio can help you stand out from other candidates and demonstrate your value to potential employers.
  4. Network
    Networking is key to building a successful career in any field, and this is no exception. Attend industry events, join professional organisations, and connect with other professionals on social media platforms like LinkedIn. You never know when a connection may lead to a job opportunity or valuable career advice.
  5. Stay up-to-date
    Data analytics and business intelligence are constantly evolving fields, with new tools, technologies, and techniques emerging all the time. To stay relevant and competitive in the job market, you need to stay up-to-date on the latest trends and developments. Attend industry conferences, read industry publications, and take online courses to continue developing your skills and knowledge.


Building a successful career in data analytics requires a combination of technical skills, industry-specific knowledge, a strong portfolio, networking, and a commitment to staying up-to-date with the latest trends and developments. With dedication and hard work, you can build a rewarding and fulfilling career in this exciting field.