Data culture is the glue that will help businesses reap the benefits of artificial intelligence (AI).
Without it, a lack of clear vision, skills, and data literacy will hold back growth – with companies unable to turn an exponential explosion of data into a competitive advantage.
By 2030, GDP could increase by as much as 26% from AI productivity gains, according to PWC. This expansion will only come if workers have the skills to input clean data into AI models.
It means companies with a strong data culture will have the upper hand as AI adoption takes hold.
In this article, we’ll explore what a data culture is and the practical steps for building one from our data experts.
What is a data culture?
Data culture is where data is deeply integrated into all aspects of an organisation’s operations and decision-making, with every individual fluent in what data means for their role.
The ingredients of a strong data culture include:
- Data-driven decision-making: A commitment to make decisions based on data rather than intuition or guesswork.
- Widespread data literacy: Any employee, at any level, can read, understand, create, and communicate data.
- Data governance and trust: Solid data governance frameworks ensure data quality, security, and compliance – creating trust for anyone using it.
- Data accessibility: There’s transparency in how data is collected, processed, and used. There are no silos, with data readily accessible to anyone.
- Data is seen as a strategic asset: There’s a clear understanding of how data contributes to success and competitive advantage.
In a strong data culture, the average employee lives and breathes data within their day-to-day tasks. Managers use data to inform decisions. And senior leaders underpin the wider business strategy with data.
Why build a data culture? Benefits and examples
Nearly nine out of ten (88%) business transformation initiatives fail to achieve their original goal, according to Bain & Company. For many companies, this is because they lose focus on maintaining and developing their new capabilities.
A data culture overcomes this, with teams ready to take on new tools and change their ways of working. Benefits include:
- Greater productivity and operational efficiency: Employees can easily process and visualise data, saving time on every data task. Examples include using predictive analytics to optimise the supply chain or improving inventory management by accurately forecasting demand.
- No more ring-fenced data teams: With widespread data literacy, employees can self-serve insights, reducing the load on internal data specialists – who in turn gain time back to focus on more complex initiatives in the overall data strategy.
- New opportunities: When encouraged to work with a data-first mindset, employees can accelerate the speed of projects and uncover new revenue stream opportunities through advanced insights – bringing new ideas and products to market faster.
Five steps to build a strong data culture
Once you’ve identified your current state, be bold in your ambition. A strong data culture is not the destination, it’s a journey. Here’s how to bring everyone along the way:
1. Align a data culture with your business goals
Start with a clear rationale for your data culture. Assess the internal data capabilities and employee skills you would need to establish one. Set out the benefits for the business as a whole, as well as the benefits for individual functions and role types.
2. Spot skills gaps and spread data learning across all levels
Assess your training needs by identifying data skills gaps. A skills matrix is a simple framework to map out your state of play, helping you target learning opportunities for all employees at any seniority level. Building data capabilities at all levels of the org chart means everyone takes a stake in supporting culture change, rather than creating silos.
3. Help employees understand the value of data
When employees see the value data can create, more will look at how their data skills can be applied to improve their roles. When a data culture takes hold, this mindset supports data-driven decision-making. Managers and leaders will act on real insights rather than hearsay, making decisions more targeted and impactful.
4. Create space to share ideas and best practices
Cross-functional data projects and creating Centres of Excellence (COEs) can help to build good data practices across the workforce. By offering opportunities for teams to collaborate with data, knowledge sharing and data-driven efficiencies break out of silos.
5. Measure the impact of your data culture
Transparency and reporting back progress to the whole business creates a feedback loop grounded in data, showing success and keeping everyone bought in. One example is CBRE, which measured the time saved on run-rate processes and calculated the overall time and financial savings for the business.
The need for upskilling in a strong data culture
Across the workforce, data skills are in high demand and short supply. According to our Skills Intelligence Report, 25 days of productive time are lost to data skills gaps annually. More than half (57%) of workers have no – or just basic – Excel skills. Some 86% have no Python skills.
Upskilling is one way to bridge this gap: by expanding skills and knowledge to better meet the demands of evolving job roles. It’s a route that helps your existing workforce make the most of vast internal and external datasets, readying them for the rise of artificial intelligence.
When coupled with continuous learning, these training tactics can help employees make the most of data, supporting a strong data culture.
Start shaping your workplace data culture with Multiverse
Need more advice on building a thriving data culture?
Multiverse can help. Learn more about our range of employee training and data upskilling programmes.
