Blog posts // Data gathering, Other
Data-driven teams – how they are developed and common pitfalls!
6 months ago
By Julia Sehr
As organizations strengthen their ability to collect more data, it becomes important to have people who understand large volumes of data and can ask the right questions about all the data. Striving to become a data-driven organization is an ambitious goal for any company because it means that objective facts, rather than subjective preferences, should be the basis for the most critical decisions made by leaders. But you have to start somewhere, and in this blog post, I will go through three tips on how to develop and utilize data-driven teams to make more well-informed decisions, as well as point out the most common pitfalls of data-driven teams so that you can avoid them.
For us at TIC, the connection between goal management and data-driven teams is crucial to ensure that the organization’s overall goals and strategies are achieved efficiently. This is because data-driven teams use objective data and analysis to establish and monitor their goals, creating a strong link between actions and results. By integrating data-driven methods into the goal management process, teams can effectively use data to identify trends, patterns, and opportunities that can help optimize their performance. Most importantly, by promoting data-driven teams, decisions (regardless of their nature) will be based on relevant data and well-crafted analyses, reducing the risk of subjectivity and incorrect assumptions and thus increasing the likelihood of achieving their goals.
Three tips on developing data-driven teams:
Promote Critical Thinking
While much of today’s discussions about data focus on the role of technology and AI, it is actually the human side of the equation that will be the most significant differentiator. As organizations strengthen their ability to collect more data – not so much in terms of quantity but rather quality – the most important thing is to have people who can question the data.
It’s human curiosity and critical thinking that are needed to identify the most significant problems that AI and data can help solve, and this process starts with you.
Invest in Education
All too often, there is a gap between what managers and organizations say they value – such as innovation, soft skills, leadership acumen, and data-driven decision-making – and the resources they allocate to enable these things. The consequences are evident: if you want your team to embrace or at least keep up with the current data revolution and approach their work in a more evidence-based way, you must educate them.
This doesn’t mean everyone needs to become data scientists, but it does mean taking advantage of the vast array of virtual resources available within and outside organizations. For instance, many top universities and leading data-driven companies offer free online courses on AI, data visualization, and data science. So the primary investment isn’t money but time. And, of course, the organization must provide incentives for people to invest the time needed to understand the developments.
Hire the Right People
When it comes to training in quantitative, data-driven, or fact-based reasoning skills, there is solid evidence for the competencies that predict individuals’ propensity to learn and demonstrate these skills.
First and foremost, this depends on their level of general intelligence or cognitive ability, which is the single best predictor of a person’s ability to solve tasks and acquire knowledge in any skill domain. Think of it as a general measure of mental capacity or cognitive processing speed. More specifically, individuals with higher levels of quantitative or numerical ability (a subset of general intelligence) will find it much easier to absorb all training related to data analysis. Regardless of the expertise or knowledge base they already have, they will learn faster and better.
At the same time, there are other psychological traits that determine whether individuals will learn to think more empirically and quantitatively. For example, those who are more open to new experiences and who are diligent tend to have more curiosity and a desire to engage in the type of training and learning required to become more data-driven.
While it is possible to enhance these psychological traits through education and development, it is incredibly difficult, if not impossible, to radically change them. So if you want your team to be data-driven, it may be best to recruit people who already have these traits in the right measure and then help them further develop their skills.
Common Pitfalls with Data-Driven Teams
As mentioned above, it’s only fair to make you, the reader, aware of the other side of the coin, i.e., some of the risks of data. As we move towards more data-driven teams, it’s also important to be aware of certain pitfalls and challenges that come with this. Keep in mind that there may be:
Bias and incorrect assumptions:
While data can be valuable in decision-making, it can also contain bias and incorrect assumptions. If the team is not aware of these potential pitfalls, it can lead to drawing erroneous conclusions or making decisions based on faulty grounds.
Data quality deficiencies:
Data-driven teams rely on reliable and accurate data to make informed decisions. If there are deficiencies in data quality, such as incomplete or inconsistent data, it can lead to incorrect conclusions or decisions that are not well-founded.
Overreliance on data:
Being data-driven doesn’t mean relying solely on data and ignoring other aspects, such as experience, intuition, and expert knowledge. It’s essential to complement data with other sources of information to get a more complete picture and avoid missing important factors or opportunities.
Difficulty interpreting and communicating data:
Understanding and interpreting data requires certain knowledge and skills. If the team lacks the necessary analytical skills or the ability to communicate data results in an understandable manner, misunderstandings or a lack of trust in data-driven decisions can arise.
Data overload risk:
Having access to a large amount of data can be advantageous, but it can also become overwhelming if the team can’t handle and analyze it effectively. Getting stuck in analysis paralysis or losing focus on relevant data can decrease team productivity and decision-making capabilities.
To avoid these pitfalls, it’s important to be aware of them and work on developing data-driven competencies within the team, including critical thinking, proper education, and skills. So when it comes to data-driven teams, I want to leave you with the most critical conclusion: ensure that data quality is reliable, and there is a balance between using data as a basis for decisions and leveraging other sources of information and expert knowledge – the kind only employees possess!
About the author
Experiences from Business Performance Management from Swedish clients such as Grant Thornton, GreenCarrier, Kunskapsskolan, Hydac, and others, as well as American company Team D3
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