Talent

The data skillset is often thought of as highly technical and specific, focusing on advanced mathematics and programming languages. However, while it helps to have experience in those fields, it is absolutely not necessary for most employees. Employees can still have a big impact with data through key fundamentals.

The key data fundamentals that every employee should have are: 

• Understanding the difference between qualitative and quantitative data

• Reading data through various visualization formats (pie chart, bar graph, line graph, scatter plots, data table) 

• Drawing conclusions and making decisions using data

• Using data to communicate the significance of conclusions

The result of a strong Data Culture is the muscle to make better decisions; that is the goal for your employees. These key skills are essential to achieving those goals. Additional skills that can support this goal, but are not fundamental, are: defining relevant metrics, collecting new or additional data, and data analysis. 

To get a workforce with these data skills, it is important to not just recruit individuals that already have these skills but to also develop these skills in all your employees. Data training should become a part of the normal onboarding process for new employees. Data training sessions should cover these fundamentals but also explain elements of your Data Culture you want your employees to know, like what are the key measures for the team, what are the data tools available for their specific team, and what are the data communication norms. 

I highly recommend ongoing development of these skills as well. As folks change teams, get promoted, or things simply change, additional data skills may be useful. Employees may want to get into the more traditionally technical parts of a data skillset like SQL or statistics. If you need help with your Data Training program, Cindy Lin Consulting provides training services and can help you and your team develop and deliver the right training for your team. Please contact us if you are interested.

Curiosity

As your team members get good at reviewing and comprehending data, they also need to get curious. Curiosity is the reason you go to the data. Asking why or how a situation came to be and being diligent about using data uncovers new insights into the situation. The good thing is that people are naturally curious, so this principle is more about shaping that curiosity to ask the best questions and condition how people respond to that curiosity. You want folks to prioritize answering the questions that have the biggest potential to deliver relevant information and gather data that is not anecdotal from a small handful of experiences, but representative of the overall situation. If you were doing a study on workplace discrimination, you wouldn’t just ask the three most senior people in the organization about their experiences. Remember, the loudest voices are not always painting the fullest picture.

I have found the most effective questions fall into one of two types: (1) why is this happening and (2) how can this be improved? The first question should lead folks down a path of understanding what is driving a behavior, situation, or outcome. The second question should lead folks to examine the alternatives to improving a behavior, situation, or outcome. Many different questions will arise when examining a situation that you want to understand in more depth, so practicing prioritization will be powerful. If it seems like there are a lot of questions that would be useful, figure out which would help the most with achieving the goals that the organization is all aligned towards. 

Second, answering questions should use data that is representative of the overall population. Anecdotal information like first hand accounts can be useful in creating some context but should not be the only thing that is used. For example, if I ran a restaurant and saw that drink sales dropped, I may ask a customer or two why they did or didn’t order drinks. But I would highly suggest against using only that information to make decisions. There may be other factors at play like certain foods being served (soup versus fries), sales by time of day, and even the weather outside. Perhaps it’s been heavily raining and so hot drink sales are up but they are priced lower than cold drinks. Relevant data to examine here would be drink types ordered compared with weather patterns to determine if there is a correlation. 

Empowerment

Ultimately, you want to empower your employees. Here I mean empowerment in two ways: (1) employees are empowered with the skillset, tools, and frameworks to get at data easily and (2) employees are empowered to use those skills, tools, and frameworks to answer their questions. The first item is what the rest of the Data Culture Framework has focused on; like clear goals the entire organization is aligned to, tools appropriate for their job, and the talent to use those tools. The second is about instilling individuals with the belief that they are capable in their own abilities. 

Creating empowered individuals is probably the hardest thing to do amongst all the other principles in the Data Culture Framework, but it is achievable! I encourage leaders to help their team members through the curiosity process: 

• Figure out what questions are useful in creating insight around a situation. Remember to focus on the why and how. 

• Narrow those questions down to the ones that will create the most insight. 

• Sometimes, understanding the actionable decisions that can be made if these questions are answered is also helpful in narrowing down to the key questions.

• Identify the data that will help answer those questions. 

• Gather and analyze data, developing answers to those questions.

• Take action or make decisions impacted by these answers. 

This can happen with the most basic of situations, like a bad customer interaction. Why are they unhappy, how was the situation handled, how can it be improved? You can look at past interactions with that customer or overall customer interactions on similar issues to identify what works best and then resolve to change behavior in future interactions. Over time, individuals will find that their jobs are made easier by using these steps, and will start to embody them on their own. As long as they are supported by a strong Data Culture that permeates throughout, they will be empowered to take ownership of the decision-making that generates significant positive impact for the entire organization. 

Finishing Up

To review, the Data Culture Framework is made up of three layers, Organizational, Departmental, and Individual. At the organizational layer, data must be trustworthy and used to create alignment across the organization through goal setting. Leaders should lead with storytelling, explaining the underlying causes when data changes, all while standards are put in place for accessing and analyzing data. At the departmental layer, relevant and specific tools should be developed for each team and be clearly integrated into department level processes enabling a community of team members that communicate with data. And at the individual layer, data talent is actively recruited and developed, encouraging curiosity to identify important questions and, through their strong data skillset and the surrounding Data Culture, empower them to get the answers to those questions. 

This isn’t the end of the Data Culture blog series. We will be back with more, including tips to implementing a strong Data Culture, examples of communities that effectively use data together, and how a strong Data Culture might look in different types of organizations like startups vs. large companies or public companies vs. non-profits. 

By Cindy Lin

Edited by Jason Rubinstein