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The Most Underrated Skill in Predictive Analytics

Jack Riewe

Programming, statistics, machine learning, multivariable calculus and algebra, data wrangling, communicating about data, software engineering, and data intuition are all skills that make up a career in data. These are the skills hiring managers say you need to be successful. If you have all these skills, you’d probably be able to snag a position pretty quickly. However, there is a skill that overshadows these in solving problems, integrating data into your company’s workflow, and using data to its fullest potential—creativity

With Data You Are the Artist.

The dataset is your paint, the ML algorithm your brush, and the output is your canvas. Okay, that was my one cheesy metaphor for this post (check-in later for more). But, this is a good way to look at it.

One of the main purposes of this blog and Obviously AI is to help our audience unlock the creative possibilities of Natural Language Powered Data Science. Through the Natural Language lens, so many doors open for the user implementing machine learning into their business.

If you focus on harboring your creativity with machine learning and understanding the possibilities of Natural Language Powered Analytics, you overpower those with just technical data science skills. 

Processes That Are Improved With Machine Learning Creativity:

  1. Generating Hypotheses
  2. Workflow Simplicity
  3. Visualization

Creativity in Hypotheses

Solving problems with data begins with a good hypothesis. A problem essentially can’t be solved unless you can be a visionary and see a solution that can disprove a popular belief or uncover an unknown insight. 

Generating a great hypothesis unlocks the questions you’ll be asking your data and gets you to a creative solution. 

Creativity in Workflow

No-Code Machine Learning is changing how teams are structured. They foster a collaborative environment, level the technical playing field, and put machine learning into a language everyone can understand. 

With all these new abilities you have with Natural Language Analytics, you can foster data independence from a data team. As a PM, your workflow can be adjusted to reach maximum efficiency and agility. You can be creative with your workflow and solve problems without going through technically-minded people.

Just make sure your team is data literate and you know how to introduce ML into your workflow.

Creativity in Visualization

Machine learning is input and output. The hypothesis and the questions you ask affect the output of your query. If you enter a creative question, you will be presented with a creative graphic representation of your data that you can quickly analyze and communicate with your team members. Getting these simple answers in a Natural Language promotes creativity and progressiveness on how you ask questions compared to the traditionally formatted SQL query. Additionally, if you wanted to share data with your audience, machine learning allows you to visualize insights in creative ways.

How to Understand How ML Can Be Used.

A big thing we want to convey is all the possibilities with ML. It has limitless potential in providing solutions. 

We wrote a great post 😉on how to talk to your data to give a better understanding of what questions you can ask your data using Natural Language Data Science. We also have been providing use cases. 

So far, we walked through how to:

  1. Predict churn
  2. Forecast demand
  3. Apply dynamic pricing
  4. Improve Customer Experience

All of these are pretty common business problems and barely scratch the surface of the power of ML. From medical diagnosis to how many people will be riding their bikes based on the weather on any given day, machine learning can predict what will happen if the right data is present.

Here is an oldie, but a goodie WIRED article on modeling inspiration you should check out.

I believe it’s fair to say you can solve any problem if you have the right data. If you disagree, reach out to us on Twitter.

From a Google Developer Forum.

This is becoming a link fest, but here is a post on 5 startups using machine learning creatively to inspire you. 

How to Promote Creativity With Machine Learning

Quoting Damjan Vlastelica

“Although some teams rely on ‘the business’ to guide data scientists & analysts by pointing them to questions that they would like to have answered, you will be much more valuable if you are able to produce these leads yourself. So, learn how to create a good hypothesis for testing, working backward from what you know would be both valuable and implementable to the business, and adding some creative flair to ensure that you examine the problem from every angle, and you will enhance your value and impact.”

The steps necessary to promote creativity with machine learning include:

  1. Become Data Literate: This is something we touched upon in our last post and it intertwines with becoming data independent. As a PM or Business Analyst, you want to understand the data and the tools you use.
  2. Integrate ML Into Your Current Creative Processes: With No-Code ML, integration becomes available to everyone. Say your team is trying to discover ways to reach new audiences with content or create better digital experiences for your customers. While this is typically a creative process, you can integrate ML to back up your creative decisions and see patterns in how an audience interacts with your brand.
  3. Use Natural Language Powered Analytics: Putting ML outputs into Natural Language increases transparency and understanding. It’s also the best way to become data literate and voice ideas about the data instead of running the risk of a team member not understanding the output because of a lack in technical skills.
  4. Hypothesize as a Team: To create a creative environment, collaborate a hypothesis and analyze the data as a team to come up with questions to ask your data. Brainstorming sessions and encouraging ideas is team creativity 101. This time, you can center it around data.
  5. Practice: It’s cliché to say, but think outside the box with machine learning. Practice using ML without limits to unlock insights you didn’t know existed. You can only get good at using ML creatively if you practice with it.

By now, I hope you can see the importance of creativity in predictive analytics. While others are focusing on developing technical skills, you should be uncovering the possibilities of ML and practicing being creative with it.

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