Building Data Fluency Throughout Your Business

Data is one of the most important assets in a business. Data fluency is about being able to describe and interact with your data to bring value to the business, accurately and efficiently. Currently, only 58% of businesses think that their organisation has sufficient data skills to meet their current and future needs. Without fluency, data remains an untapped resource, and decision-making across the business is limited. To find out more, we spoke to Data Science + ML Trainer, Tom Udale.

Part 1: What is data fluency?

What do we mean when we talk about data fluency and why is it important?

“The first main part is describing data; employees should be able to identify the data within their area of the organisation and also be able to understand fundamental statistics about their data. And the second main area is interacting with that data. Employees should be able to identify issues with that data and know how to resolve them (or who to ask!)”

“This also means employees should be able to manipulate data to answer business questions and suggest ways to use their data to technical teams.”

While technical teams need to understand and be able to explore their data, for truly data-driven businesses, this also applies to business users.

 

Does data fluency need to be business-wide?

“Data science teams don’t have all the business knowledge of users. Each employee will have specific knowledge about their area of expertise, which allows them to identify opportunities within data.

The amount of data in an organisation is massive! A small team of analysts and data scientists won’t be able to keep track of all the data generated in a company, so having active data citizens is vital.”

This means that business users will be able to identify innovative and realistic opportunities. If business users aren’t aware of what can be done with data, they won’t raise it with the technical teams. Equally, users need to be aware of what makes a good data opportunity, either for analytics, advanced machine learning or AI.

Part 2: How to build data fluency

What are the challenges to building data fluency?

 

Building data fluency is a challenge that needs to be approached strategically and with best practices in mind. Here are four key attributes of successful data fluency development: 

1. Large-scale learning

Data fluency is a programme which needs to upskill whole organisations, which often have different schedules and restrictions on time. With proper planning, entire organisations can be prepared to use data effectively.

2. Specialized

The programme should be specific to different departments (Business, HR, Finance), with tailored use cases and exercises to show real-life examples and prepare employees with the most required skills for their role. 

3. At the right level

The programme should not focus too much, or spend too much time on technical implementation. Rather, it should focus on identifying business value, and understanding how data fluency can be used to make a difference.

4. Continuous training + upskilling

Instead of a one-off session, the programme should enable new employees on an ongoing basis, with a rolling programme to facilitate continuous improvement and skill retention.

 

How can training solve your fluency shortfall?

“Datatonic Academy can align training to business objectives around data specific to your business. Training can be further tailored to each business unit, with relevant examples and exercises, and can be delivered in both an on-demand or instructor-lead format allowing for flexibility around users’ other commitments”.

Furthermore, we can provide materials for ongoing in-house training, as well as create “super-users” within the organisation who can upskill colleagues, including new joiners.

Part 3: Leveraging data fluency to create AI + ML innovation in your business

It’s important to consider, once your team has been trained and upskilled: how can businesses measure a successful data fluency programme? And what is the impact we’ll see in the long run? 

There are three main areas that businesses who immerse themselves in a successful data fluency programme can expect to see:

 

1. Data Opportunities

With a strong data fluency programme in place, you would see more opportunities for analytics and ML to come to the technical team. This will lead to higher productivity and efficiency

2. Higher quality data

As business users who interact with datasets across the organisation grow in fluency, they will be able to identify issues with data and help technical teams resolve problems.

3. Data-first culture

If the entire organisation is aware of the value of data, and how that can be realised, then business challenges of all types will be addressed with the relevant data at hand. This means faster and more accurate decision-making throughout your company.

 

How can training accelerate your ML workflow and generate value for your business?

“Training doesn’t just have an impact at the foundational and cultural level within an organisation, realising the potential of AI, machine learning and generative AI all require a base level of knowledge, along with continuous upskilling and training.”

Strong data fluency and data foundations make it much easier for people to first identify new ways of using their data, and how this can be done with AI + ML, and then much easier to get started as the data is ready to use.

 

Conclusion

Datatonic Academy can provide training at all levels, from fluency to state-of-the-art ML techniques, from experimentation to MLOps and from on-premises to the cloud. As tools and technologies improve, help your workforce improve and stay motivated in AI and ML. See our upcoming public training events here. 

 

Find out more about Datatonic Academy and get in touch to book a free scoping session to kick-start your journey to building data fluency and secure 20% off any training courses booked before the end of January 2024.