- Use Cases
March 11, 2020
Reading time: 5 min
This blog contains tips and examples that can help you get started on a path to better data management, analysis, and sharing in the future. Climbing the data skills mountain is a difficult but worthwhile journey.
Organizations around the world today are attempting to make better use of their data. From open data to AI and smart cities, many organizations make efforts to deepen their ability to use data to improve services. However, a major obstacle to setting up a data sharing portal, doing analytics, or just using data more effectively is the lack of data skills. Improving data practice in an organization requires skills in data management, analysis, and communication, skills which are often perceived to be lacking, no matter the sector or context.
Building data skills in an organization can seem like climbing a mountain with the summit never in sight. There are always new buzzwords, varieties of techniques, and an unending amount of data to manage and analyze. However, you don’t need to reach the summit, or be a data scientist, to publish and use data effectively. Even in the most advanced organizations, foundational skills like data cleaning, basic analysis, and visualization can take you a long way toward meeting your goals.
In my past experience as Director of GovEx Academy at Johns Hopkins University, I worked with public sector organizations around the world to help identify, develop, and refine data skills to improve public sector outcomes. I found that taking a few simple steps can help you get the ball rolling on the way to deeper and broader data use in your organization.
This blog contains tips and examples that can help you get started on a path to better data management, analysis, and sharing in the future. Climbing the data skills mountain is a difficult but worthwhile journey, and by starting small and working smart you can improve your organization’s use of data as of today.
Any skill building effort in an organization should begin with an assessment of what skills currently exist. Knowing what skills you do and do not have provides the information needed to start building on strengths and address gaps. Just as doing a data inventory is critical to understanding what data you might publish, pursuing a skills inventory provides the foundation for growth.
Just as citizen surveys can be very useful in determining public needs for data, assessing skills internally can also provide a variety of benefits. First, an assessment can provide a more systemic overview of skills across the organization. You will likely confirm some things you already knew, but also uncover existing skills even where there might have been a perceived deficit. Building from existing strengths and knowledge can help ensure you get momentum as you launch any program to build new skills.
In addition, doing a skills assessment doesn’t require a lot of technical know-how to get started. You can begin by discussing with relevant department heads or managers, looking for existing groups that have data-related focus (often GIS, IT, and budget departments have strong data skills), or doing a simple survey of staff to get a feel for what knowledge the organization possesses. You may also discover people in your organization who don’t have strong data skills but are interested in learning as well. These people are important to engage in the long-run and you can’t find them unless you ask.
It is critical to cast a wide net as you get started. Often employees who are interested in building new data skills do not currently work with data or may not have it as a part of their job title. Focusing at the outset only on those who have “data” skills can limit the pool of potential participants and can be intimidating for people who don’t see themselves as “data” people (although they often are). Keep an open mind on who could be a participant and what topics you may start with for training.
This may also lead to discovering skills related to data that are necessary for building strong practices within an organization. A great irony of data work is that it’s often about people and culture, not data. Topics like ethics, problem definition, community engagement, and project management often come up alongside data topics. Focusing only on the “data” can make training seem disconnected from daily priorities and may not actually move the organization forward if other topics are neglected.
Finally, starting by framing training around an issue that is important in your community or organization like housing, transportation, or economic development can help to focus your skill-building efforts. Not everyone is interested in data but many are interested in those core issues. Use that to your advantage in recruiting participants and also to help your training focus on a tangible outcome aligned to existing strategic goals.
As you get started, keeping it practical is the name of the game. Data science, analytics, and AI are sexy topics and may seem appealing, but the real work of skill building is often fundamental. In my experience, organizations usually start off with the basics of identifying, cleaning, managing, and governing data and these skills can help set you up for success in the long-run.
Starting with a goal, hopefully aligned with organizational strategic goals, and then backing into skills you need can help identify a relevant and practical project. Making that goal explicit and sharing it with participants is key as well so they know what the focus for training will be. For example if you’re looking to start publishing data in on a particular topic, you’ll need your data to be clean, managed well, and prioritized for use. Training people on those basics can help accelerate progress as you go forward.
Finally, building new skills takes time. Focusing on practical skills is important but you probably will not see overnight results and that is ok. For example the City of Louisville made it a priority to train a specific group of employees on a particular tool and it took them several years to see the clear and compelling results they have now. Being practical and patient were keys to their success.
Organizing, publishing and using data is critical for organizational success in the 21st century and require strong skills in your organization. With the tips above, you have a roadmap for building data skills that hopefully shows it’s not as daunting as it seems…so get started on your data skills journey today!
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