A modern business user’s relationship with data is fairly complicated. It starts with curiosity. “Which of my top users will do X,Y, or Z?” You need a data output to move forward with a decision—except you’re having communication issues.
The question you’re trying to answer often comes with loopholes and turns into a string of jumping between engineers and trying to learn code. There’s communication barriers, you can’t commit enough time to the data science process, and maybe you consider breaking up with your data altogether.
We believe that this relationship with data needs to change. There needs to be a spark.
Over the years, working with business users we have started to learn that:
- Traditional data science is too complicated for the average non-tech team member.
- Non-technical users request a team of engineers to solve problems that could take weeks to answer.
- The process is in a technical coding language, such as Python or SQL.
- You have to guess which algorithm to assign to the data, which leaves room for wasted time or error.
- The user can’t visualize a data output and has to create their own charts and graphs to communicate their findings.
All of these things can be fixed by introducing no-code data science into your relationship.
What is No-Code?
Traditionally no-code tools have been anything and everything that allows anyone to run complex, programming heavy tasks without writing code.
Turns out, this isn’t a new invention. In 2003, Squarespace and Wordpress began shaping this landscape. Initially these no-code tools were meant for beginners to get started with tasks they couldn’t do without a technical background.
Today, these tools can allow anyone to get as complex as they want without writing code. And there are tons of no-code tools business users leverage to perform tasks more efficiently.
Image via Makerpad.
How No-Code Effects Business
The true value no-code tools create is they translate technical, esoteric tasks into automated, accessible tasks for those without a technical background. They democratize these skills and make creating a website, marketplace, automated workflow easier for small businesses and teams lacking technical expertise.
Enter No-Code Data Science
While no-code tools is a relatively old idea in the tech world, no-code data science is still young and new—the kind of spark you need in your relationship with data.
Traditionally, business users have to depend on engineers to get answers from their data. Many look towards a complex SQL query system or fancy neural networks, but what companies really need is a decision layer which helps them quickly get insights about their business. (We're not just talking about data visualization or making graphs, but real actionable insights e.g. predicting churn, fraud, understanding why in-app purchases happen, etc.) No-code data science allows you to do just that and puts machine learning and insights from data out of the hands of an engineer and puts it in the hands of everyone to use effortlessly in their business.
Transitioning From Traditional Data Science to No-Code Data Science
Let’s again look at the problems with traditional data science:
- The process is too technical.
- Getting insights takes too long to implement flexible and adaptive decision making.
- Data science is limited to large companies like Google and Apple that can afford a team of machine learning engineers.
Okay, now let’s apply no-code data science to these problems.
No-Code Data Science Simplifies the Process Down to Three Steps
The traditional ML process looks like this:
Turns out, what a business user truly cares about is just getting an insight. They have a question and they want answers fast. Imagine if you could just ask your data a question and get answers that help you make decisions quicker than the traditional ML workflow.
If you subtract the coding and model training and add a search bar, everything gets much easier. Data science transforms into three steps:
- Upload data
- Ask questions
- Get insights
Data Science Can Be Made Effortless With a Simple Search Bar
You’re probably wondering about “Step 2: Ask Questions” and what that means. Imagine if you could talk to your data in English and get answers as quick as your curiosity strikes.
We mentioned adding a search bar to your data science process. A search bar not only represents the ability to find answers—but also represents the ability to find answers quickly and effortlessly.
A search bar puts everything into our natural human language and takes away the technical language only technical users know. The search bar is how data science is effortless for everyone.
Read more on how to talk to your data.
No-Code Data Science Promotes Creativity, Flexibility, and Speed
The traditional process doesn’t allow for any of these things—especially if there’s only one data scientist in your whole organization. Sometimes it may take weeks to make a prediction. To analyze data, you have to construct a time consuming SQL query that leaves room for error or misinterpretation by team members.
With no-code data science and natural language, users can get reports as fast as they can ask questions and be flexible with how they use algorithms. No-code data science also allows business users to be creative and use data in a competitive way.
For example: If a non-technical user wanted to explore app store competition in the ideation process, they historically have to hire a data expert to explore an app store dataset. With no-code data science, that same non-technical user can get the same outputs and aren’t limited to a few questions to fill the data experts time.
They can ask creative questions such as “What is the probability an app in the music genre with 50 reviews will receive 10k downloads?” Or “What is the average number of reviews for the top 100 apps in the app store?”
Whatever the user is curious about, they can simply ask in a natural language and come up with creative predictions for their problems.
The Future of Data Science
We predict a revolution in the way people think about data science and machine learning in their business. Adding no-code to your machine learning process any SMB can get value from their data as if they were a thousand-person company.
How we think about our relationship with data science in business needs to change in the following ways:
- Traditional data science is a lengthy process with a tough technical barrier to Data science can be simplified into three steps.
- Traditional data science requires a team of engineers to train and build algorithms to Even smaller enterprises can access data science and prediction models without depending on engineers.
- Traditional data science takes weeks to solve simple problems and is in technical language to Data science can take a few minutes and be translated to the rest of your organization in English.
The way we begin rethinking our relationship with data science starts with no-code. If we rethink our relationship with data science, we can begin to use it more creatively to solve problems.
Instead of the complex process laid out for technical users, those who adopt no-code tools can get the same insights in more effortless ways.