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  • Writer's pictureNicky Finlay

How to improve data literacy as an SME

Our recent survey of data usage within SMEs highlighted that whilst 74% of businesses stated being data-driven was ‘very important’ to their business, over a third recognised that a key challenge to using data was insufficient skills or expertise in data analysis.

For SMEs wanting to build their team's data skills, often the focus is on the ability to understand, analyse, and interpret data effectively and accurately but we believe that there are a few other things to focus on to build your overall ability to use data and become more data literate as a business.

To help you and your teams achieve this we have pulled together an overview of 8 clear areas of focus on to build a lifelong learning approach for your team.

Critical Thinking:

We have put this first on the list as it really should transcend all the skills and knowledge required to be widely data literate.

Data is used every day to answer questions and provide the evidence to form decisions, but if the data isn’t clearly showing you insight it can be easy to bypass and revert to using gut intuition or not making any changes.

It's important to think critically about what you are trying to achieve when working with data. This means don’t be purely led by what the data says, question assumptions, and be aware of potential biases or mistakes in the data which helps avoid making wrong conclusions or decisions based on incomplete or misleading information.

Data Awareness:

This simply means understanding what data you have in your business, and where it comes from. Knowing what data is collected in your business, its sources, formats, and types will help to determine what you can start to use.

Great first places to focus on are sales, finance, or customer data, but these are just three areas. Stock control, HR, and digital and social channels data expand data landscapes and can be equally important in your business.

It can be very useful to map out your data ecosystem to really understand what is being collected and stored in your business. And then work with key stakeholders across your business as to how you could use the data to improve processes and decision-making.

Data Collection:

Once aware of what data you have (or haven’t got) it is important to have a strategy on how to gather the right data. Focus on all customer interactions including call centre and digital, internal and external surveys or appending information from reliable sources.

Garbage in Garbage out - collecting good quality data is important to build trust in the ongoing use of data. Ensuring accuracy and relevance so it can be stored, processed and used easily, and is also collected to ensure GDPR compliance and prevailing legislations.

Data Evaluation:

Once you have collected data, it's important to check if it's trustworthy and reliable. Evaluating the data for any biases or errors in the data that could affect the results or understanding of the information.

For instance, if you want to compare customer satisfaction for different products, you want to avoid being guided by one person's view, so ensure you have robust and representative data samples for each product being evaluated.

Data Analysis:

This is about understanding and making sense of the data. Looking for trends and insights over time can be really important for your business when developing longer-term plans but also to identify and flag up opportunities that you might not see working in the day-to-day.

It is not necessary to train as a data scientist to be able to analyse data, but it does involve understanding basic concepts to summarise data to be able to tell a story. It is important to have the ability to summarise data through averages, percentages, time series, correlations, and being able to use spreadsheets and data visualisation software to bring the data to life and support decision-making.

Data Interpretation and Problem Solving:

After analysing the data, look for patterns, trends, or relationships in the data that can give meaning and valuable insights that can help answer questions or solve problems.

It can be quite common for people to get lost in data and spend a lot of time analysing it but not providing insight. A great tip here is to focus on how the data is going to be used use of the data helps shape the interpretation. For instance, if you want to know the best time of day when the best time of day is for booking service appointments, interpret time series analysis to find the answers.

As a manager, you can help your team by spending time articulating the question to focus on. And if necessary always set your own question to avoid going down rabbit holes.

Data Communication and Storytelling:

This is an essential part of becoming more data-driven as a business. We are all busy and no one will have time to go through hundreds of charts.

Having the ability to effectively communicate data and insights to others through visualisations, reports, and presentations are critical elements to becoming more data literate. Prioritising the key points and recommendations using charts, graphs, and narratives to present data in a clear and understandable manner.

The goal is to present the data in a way that is easy for others to understand, engage with, and take action from.

Ethical Considerations:

When dealing with data about our customers or employees (1st party data), being conscious of the ethical use is essential. This includes respecting people's privacy, keeping data secure, and using it responsibly. Anyone handling any data (data controllers and processors) must follow rules and laws that protect people's information. Ensuring there is a good understanding of GDPR across the business and clear guidelines as to how it is used at all stages of data from initial collection through to ongoing storage and use of the data would help teams become proficient in handling data within the guidelines.

Other ethical considerations would need to be on the use of AI and data retention to be more sustainable. Raising awareness that each bit of data has a carbon footprint, considering what is needed, for how long, for what purpose, etc to ensure non-essential data is regularly removed.

Lifelong Learning:

The Data landscape is continually changing, with new approaches, tools, and techniques constantly emerging. So, it's important to have a mindset of continuous learning, staying curious and adapting to the changing data landscape. Following blogs like ours and other experts in the market, attending events and digesting case studies are a few ways to stay on top of market trends.

In conclusion

Fostering data literacy is not just a matter of understanding statistical concepts or mastering tools; it's a holistic approach that encompasses a number of elements that we have discussed e.g. critical thinking, awareness, reliable data collection, analysis, interpretation, and evaluation.

For SMEs, investing in these areas is not only a strategic imperative but a pathway to unlocking the full potential of data. The ability to navigate the intricate landscape of data empowers individuals to make informed decisions, solve complex problems, and drive innovation.

In an era where data ethics and sustainability are paramount, incorporating these considerations into the fabric of data literacy ensures responsible and impactful data use. As the data landscape evolves, the journey toward becoming data-literate becomes a continuous learning experience, and those who embrace this mindset will thrive in a world where data is a dynamic and indispensable asset.

So, whether you're mapping your data ecosystem, analysing trends, or crafting compelling data story-telling, remember that true data literacy is a journey, not a destination—a journey that equips individuals with the skills to navigate the ever-changing currents of data in both their professional and personal spheres.


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