How Training Teams Can Boost Data Literacy By Developing a Data Culture
While data’s importance has been thoroughly stressed by thought leaders in every industry, discussions of data literacy have lagged behind. The truth is that while innovators are envisioning a data-driven future, the workforce is still operating the same way it has for decades.
Dealing with data is seen as the job of data scientists, not average employees who generally have little to no formal training or education in utilizing data effectively. Additionally, academia has so far failed to keep up with enterprise demand for data literate graduates, meaning even younger, digitally native professionals often lack the competencies they need to utilize data effectively.
This disconnect between enterprise demand and workforce capacity is unsustainable in a world where ever-larger amounts of data need to be understood and processed, and where decisions need to be both made quickly and be supported by data.
Universities are unlikely to suddenly step up their data-literacy game, and even if they did, that would not address the critical shortage of data literacy skills among the existing workforce. Addressing this skills shortage will require training teams to rapidly upskill employees to be more comfortable and confident in their use of data.
But effectively upskilling data literacy requires training teams to lead the way and develop robust and data-driven internal workflows – what can be described as data culture.
What is Data Culture?
Data culture is a topic that spans many disciplines, but can be summed up as having three parts. An organization with effective data culture has:
- A mindset that decision-making should be driven by data.
- An expectation that all employees should possess data literacy skills, especially skills relevant to their work.
- An environment where employees are supported by the whole organization as they continuously improve their data literacy.
That might sound like it requires a radical departure from most teams’ established procedures. While introducing and maintaining data culture should not resemble business as usual, it should also not seek to replace existing organizational culture. The focus should be on empowering employees and decision-makers to make better decisions within the scope of their current workflow, keeping what works and improving it by supporting the process with the insights gathered from the data.
Implemented correctly, robust data culture can be a continuous source of improvement and value to the team and the organization. Let’s take a look at each of the components of data culture and see how it can be effectively put in place.
Committing to Making Data Driven Decisions
Assuming that your team has effective access to the data that it needs, committing to using that data is the first step towards developing data culture. Simply deciding to utilize data might seem obvious, but actually doing so can be difficult. That’s because making data-driven decisions is not the same as making decisions and only then backing them up with data.
Confirmation bias is a fundamental error that misdirects our thinking when we are dealing with data. We naturally seek out information that confirms what we already think, and ignore or de-emphasize information that challenges our assumptions. It is easy to reach a conclusion first and find a few data points that support that conclusion.
This kind of superficial use of data does little to add value to the decision making process – the decision is already made before the data is consulted, and if only a small amount of data supports it, it is unlikely to be changed. Careful analysis of all available data, with an open mindset, is the only way to allow data to drive decisions, and not the other way around.
Overcoming confirmation bias requires strong commitment and buy-in from all levels of the organization, but especially from key decision-makers. They need to probe datasets with questions to be answered, rather than conclusions to be supported, and accept results that might conflict with their prior beliefs. That’s a difficult mindset to develop, but it is a crucial foundation for an effective data culture.
Committing top-level decision makers to data-driven decision making is a first step. But it quickly leads to a dead end if the workforce is not prepared to support that decision-making process. This is where the next step comes in.
Identifying Roles and Competencies for a Data-Literate Workforce
The whole point of developing a data culture is to bring the workforce up to a higher level of data literacy so that they can support an effective data-driven decision making process. Not every employee makes those decisions directly, but every employee has a role in ensuring that decision-makers can rely on data to inform their work, and that anyone else who could benefit from utilizing data can do so.
Each employee’s relationship with data will look different, and that affects the skills they need to develop. For example:
- Employees working directly with raw data need database maintenance and information security skills to maintain data integrity.
- Managers need data visualization skills to effectively communicate data with their teams and their superiors.
- Strategic planners need familiarity with advanced analytical techniques to be able to base their strategy on data-driven models.
Understanding how different employees will interact with data, and identifying the competencies each employee needs, creates a roadmap for what skills need to be developed. It also avoids the temptation to apply unhelpful one-size-fits-all solutions that provide most employees with training that is irrelevant to their needs. The next step is to execute that roadmap and support each employee in making the most of data.
Developing Workforce Data Literacy
Training teams’ knowledge bases are essential to developing data literacy, whether within their own team or in a whole organization. Though it can seem like an immense initiative, all that’s needed to develop a data culture is for employees to be given the learning opportunities that they need to develop their data literacy skills and bring more value to the business – and isn’t developing skills exactly what training teams are already built to do?
As the department most familiar with delivering multimodal training and learning experiences, and the most experienced at evaluating and improving the effectiveness of professional development within the organization, training teams have a crucial role to play in supporting major innovations like the adoption of data culture. That role includes:
- Analyzing data literacy in your existing workforce. Most employees don’t have a degree in statistics or formal data analytics experience, and the practices they have picked up informally may be outdated or incorrect. Developing and carrying out a data literacy skills assessment will allow you to determine what capabilities your staff already have, and what areas you should focus your efforts on.
- Facilitating mentorship and collaboration. Data literacy’s complexity means it lends itself well to a mentoring approach. Leveraging data-literate staff by partnering with them to design training strategies built around mentorship is a great starting point.
- Identifying roadblocks to data literacy. Consider processes, systems, and technologies that may be hindering your ability to improve data literacy. For example, learning tech commonly contains pre-made reports which are easy to pull, but often lack context. Implementing software with more sophisticated reporting structures encourages employees to apply their skills and actively engage with data, rather than passively reading pre-made reports.
The long-term goal of this process is to develop a highly data-literate staff who flexibly support each other in all aspects of data utilization. Key to achieving that goal is simplifying access to data and providing data-literate employees with the tools they need to leverage their skills.
For a training team, that means developing a learning technology environment which provides the whole team with access to powerful functionality, without creating process-blocking complexity. A comprehensive training management platform can be the solution to ensuring that making the best use of training data doesn’t mean further expanding unwieldy tech stacks.
Ready to take the next step in adopting better data literacy? Understand the potential better data management and utilization brings to the training function. Get the guide.