So you’ve added ML into your workflow, now what? How do you analyze and get value out of the data you collected? You have to be data literate.
I’m not talking about just your manager. In order to reach the maximum value from your data, every team member should be able to extract insights and do it collaboratively.
What Your Team Should Look Like.
Using machine learning can take your workflow to the next level, but only if governed correctly and it involves a team structured like this:
The Overseer - The leader of ensuring collaboration, innovation, and creativity is taking place within the team handling the data and machine learning process.
The Generalists - A diverse group of people that bring a lot to the table and don’t focus on one aspect of data science, product, or machine learning. You want to avoid being too specialized because you don’t want one person handling the data sourcing or analytics process. This creates the possibility for bias machine learning and doesn’t require everyone to be data literate.
The Sourcers - Those who collect data and see the end result in sight. If you’re collecting data, you should know the business problem and why the data you’re collecting is going to bring a solution.
The Visionary - A kind of devil’s advocate that asks the team if there are hidden insights in the data and to check if everyone is reading the data correctly. On top of that, understand how the insights can translate to action.
While these team members should know how to extract insights from data, the way they do it has changed.
With No-Code Machine Learning, Data Literacy Looks a Little Different.
The typical process of establishing a data literacy program within your organization has changed with the introduction of machine learning. New priorities have emerged when implementing how your team should look at data.
Data Independence - Product managers want to be agile, avoid bureaucracy, and be on the same page with their whole team—regardless of technical skills. This means they want to be data independent and not have to go through a data science team to build and train models that don’t foster collaboration and transparency in how the data is used because there’s a loss in translation of technical language.
Collaborative Machine Learning - If only a few people in your organization speak technical coding language, the ability to collaborate decreases. If your machine learning isn’t collaborative and transparent, this leaves room for bias algorithms and a lack of cohesiveness. Once your team reaches the action phase of your machine learning process, your decisions may suffer.
Creativity - With no-code machine learning, teams can now use machine learning without knowing a technical language, going through the data science team, or relying on SQL queries. This leaves so much time for being creative with your data and opening up the culture of creative solutions because analyzing or predicting will be easier.
The traditional process of prioritizing data literacy in your business is still very important, but these new priorities should be at the center of communicating about your data in context.
Why You Should Invest in Natural Language Data Science to Increase Data Literacy.
We mentioned the term “Natural Language” in a previous post on how to talk to your data, but to reiterate, it’s important those involved in your machine learning workflow speak the same language. Natural Language means you can ask your data questions in plain English (or any human language) and the output will be comprehensive to your team. Natural Language Data Science avoids annoying SQL queries and further increases data independence from the engineering team.
Natural Language Powered Analytics ensures everyone is on the same page and includes everyone—not just those who have technical expertise.
Data Literacy Related to Action
There are many ways to approach this topic. Turning data into action has a lot to do with what problems your team is trying to solve with machine learning.
Going into the healthcare space, let’s say your business problem is to identify which of patients aren’t adhering to their prescription regiment and you want to know what attributes are related to that so you can create an intervention plan.
The quicker your team can read the data and understand it to identify which attributes are directly proportional, the quicker they can create an action plan.
Natural Language Powered Analytics is the quickest way to get actionable insights. Asking questions like “Total [Doses Taken] by [Drug Name]” or “Average [Rating] by [Drug Name]” can provide useful analytics your whole team can understand. If you can read the outputs of these questions in plain text or a graph, you can form decisions around it that you can quickly communicate to many other decision-makers in your organization.