How to Create Citizen Data Scientists
You’ve heard of citizens. You’ve heard of data scientists. Have you heard of citizen data scientists? If not, this is your guide to turning employees and customers into citizen data scientists to help make the very most of your data.
Briefly put, a citizen data scientist is someone who works with data in some way but is not a data scientist, data analyst, storage engineer or another type of data professional.
Citizen data scientists might help to collect data. They might help to interpret it. They might help to improve data quality by correcting errors. There’s no specific definition of what one must do to be considered a citizen data scientist.
The citizen data scientist concept builds off the citizen science movement.
How Citizen Data Scientists Can Help You
If your company is like most organizations today, you have more data than you can handle on your own. Every device on your network spits out data. Your customer support and sales logs are full of data. Your marketing efforts exude data.
Processing, storing, interpreting and securing all of that data is a tall order. Even with sophisticated data storage and analytics tools on their side, your team of professional data scientists may not have the person power or time to do everything they should do with data.
That’s where citizen data scientists come in. They fill in the gaps between the data operations you wish you had, and the ones you can achieve with your limited resources.
Examples of the types of work citizen data scientists might perform include:
- Performing manual data entry in situations where you can’t automate data collection.
- Reviewing data sets for errors. Through the crowdsourced efforts of many citizen data scientists, you can find mistakes and improve data quality.
- Providing support to one another when working with data tools. You may lack the resources to provide professional-grade support to every one of your employees or customers when they work with data, but citizen data scientists can offer community-based support.
- Validating or testing data analytics results. Citizen data scientists can help perform checks to confirm that the inferences made by your professional data scientists are true.
How to Create Citizen Data Scientists
Few people are likely to become citizen data scientists on their own. If someone is born with a deep interest in data, that person probably ends up becoming a professional data scientist. The rest of the population tends not to pay much attention to the intricacies of data analytics and tools.
After all, working with data is not as interesting to the average person as, say, scanning the skies for signs of alien life. Citizen scientists committed to the latter work are easy to come by, but citizen scientists usually require some encouragement – and education – before they will begin working with data.
That’s why you must cultivate citizen data scientists among your employees and customers. You can do so using the following strategies:
1. Make data open.
Data sets that can be reviewed by anyone within your organization, or even the public at large, invite ordinary people to collaborate with data analytics or data quality work. You must keep data privacy and compliance needs in mind when releasing data publicly, of course, but open data can be a useful resource if it is appropriate.
You don’t have to train each of your employees in using Hadoop or writing R scripts. But you should consider offering them basic educational material, such as short videos that familiarize them with the data tools that your organization uses.
3. Offer incentives.
Incentives for citizen data scientists who take the time to do things like review data sets for errors or validate data analytics results will help attract citizen data scientists to your organization. Incentives can include rewards like credits that may be redeemed for free access to a commercial service you offer.
4. Implement strong data governance policies.
When you allow non-professionals to participate in data operations, you want to make especially sure that data is handled in the proper way. This is why data governance is so important for creating good citizen data scientists.
5. Make data operations easy.
Working with data will always require some amount of skill. But you can help employees and customers to become citizen data scientists by streamlining data operations as much as possible. For example, use tools that automate the tedious processes involved in data operations, such as data transformations.