After talking to some customers who use our platform, we found that one of the things they’re most excited about is the idea of becoming data independent.
Naturally, we brainstormed a post on how to do this and create your own little Fourth of July celebration from a data science team.
What Does It Mean to Be Data Independent?
Being data-independent means using the power of machine learning to make your own data predictions and analytics without going through a data science or machine learning team. It also means to separate yourself from annoying SQL queries to get insights from your data. Being data-independent promotes collaboration, speed, and creativity with analyzing and making predictions.
Your Game Plan to Become Data Independent Should Look Like This
It’s the classic problem of getting lost in translation. Enter movie reference:
You want to solve a technical problem but can only do it in technical language. Not everyone knows a technical language. Start with these steps:
1. Begin thinking of ways to put your machine learning process all in one universal language.
There are two ways you can tackle this. You can put all your technical language into a natural human language so everyone can understand and you can communicate insights better or you can hire technical data experts and hope everyone can communicate in a technical language.
2. If you hate using SQL queries, find a way to avoid them.
No PM has to do SQL queries if they employ a no-code ML solution. If you can put your machine learning process into a natural language, you can employ a no-code way of asking your data questions.
3. Once you’ve done the last two steps, start looking at your data differently
Since you can talk to your data in a natural language and don’t have to do SQL queries or coding, data independence quickly becomes within reach. Think of your data queries as Google for Enterprise Data. You can easily search for insights in plain English and share them across your organization.
The Challenges of Being Data Independent.
We feel like it wouldn’t be right to address some of the challenges of data independence as well so you can begin thinking of how to deal with them.
1. “Data can be misinterpreted.”
This is an obvious statement and I put it in quotations because it’s a statement you should always say to yourself before rushing into business decisions. While just the technical team owning the data is going out of style, you should be wary of the data being misinterpreted by someone who isn’t data literate. As a PM, ensure your team is on the same page and encourage data literacy skills.
2. You’re now handling sensitive data.
Business leaders should decide how liberal or conservative you should be with your data from the start. Being conservative with your data can mean only a few people control the ML process, possibly leading to biased decisions. Being liberal with your data can mean greater security risks or skewing datasets.
Data Independence is the Future of Work.
As one of our categories in this blog, we love talking about the future of work—specifically from an AI and ML lens. We believe becoming data independent will promote data literacy, creation, and collaboration among team members.
Stop going to your technical team for answers you can find yourself in a natural language without using code. And please, get rid of the SQL query from your workflow.