Why humans matter to make data impactful
Posted On 02/12/2021
Data-smart technologies like AI and Machine Learning are on the rise. Especially with huge amounts of data, they are a tremendous help to automatically find insights and patterns in your data. Some would even wonder: do we still need a human or a data scientist to do the job, if a machine can do it for us?
The truth be told: machines help us crunch the data, but we need humans more than ever to make data impactful. In this article, we’ll explore why data and humans will always go hand-in-hand, and there is no reason to fear new data technologies.
How humans make data impactful
Humans think creatively
There are a million possibilities of data you could analyze. But not everything is equally worth analyzing. For example, take this story of a family who had to get their baby’s cholesterol levels tested in order to get insurance coverage.
High cholesterol can lead to a lot of medical complications. As a result, it’s common for insurance companies to test cholesterol levels and raise premiums for risk patients. But the chances of high cholesterol in a newborn are so low, so why require this type of painful and expensive testing for a baby? No human with common sense would come up with this idea, while a machine learning algorithm would simply suggest testing all patients.
People have the creative capacity to think about what’s worth analyzing. When a person takes a closer look at the data, they will come up with much more meaningful hypotheses than a machine could ever do. Humans have the power to connect the data to their real-life context and day-to-day challenges.
Humans search for the ‘why’ behind the data
So whatever a data dashboard might show us at first glance, people will always link new insights to what they already know. The first question a human will ask is: “Why did this happen?”
For example, imagine an online web store has seen a sudden spike in sales for iPhones. Their systems recommend restocking their inventory to keep boosting sales. However, an employee points out that Apple is launching a new model to replace the older model, and people are now buying the older model in a final sale. If they would have followed the recommender system, they would have had a hard time selling a bunch of outdated phones.
Humans will never let technology bluntly tell them what to do. But they won’t always have the answer at hand either. In that case, humans need tools that enable them to mine their own insights and look at the data critically before simply acknowledging a pattern.
Humans use visual techniques to make data impactful
Processing huge amounts of raw data is impossible for the human eye or brain, unlike a computer. However, humans can come to more meaningful conclusions by using visual techniques to make the data readable.
For example, Anmol Garg, a data scientist at Tesla Motors had a vast amount of sensor data available from their cars. To make sense of it, he started making visualizations to understand tire pressure in their cars. As a result, he found a ton of use cases for the data, such as:
- are tires properly inflated when the car leaves the factory?
- how often do customers reinflate the tires?
- how long does it take to respond to low-pressure alerts?
- when are tires likely to go flat?
These are all patterns of information that need human creativity to come up with. Representing the data visually inspires problem-solving: which information to look at, how to interpret certain trends, etc.
Intuitive analytics tools as a partner in crime
So yes, humans are the ones making data truly impactful, not technology. But does that mean technology is redundant then? Absolutely the opposite. In fact, technology is the facilitator that empowers & supports a business user or customer to use smarter insights every day.
Because in fact, technology is what helps any person translate huge amounts of complex data into good data stories. As a result, it will become indispensable for software products to offer visual data analysis tools to their users. They need it as a means to generate new insights in real-time.
There are multiple ways to enable product users to mine insights within an application:
- A low-barrier entry for data analysis: offer dashboard examples based on available data to your users as a starting base, which they can tweak to their own needs & preferences.
- A playground for data analysis: give your users free reins to explore their raw data starting from a blank canvas. Let them build their own insights in an intuitive environment for maximum exploration.
Whether your users prefer a more guided or explorative approach, there are a few things you can do to ensure a smooth experience on your platform:
- Offer an easy, intuitive interface to generate insights;
- Integrate visual insights right into the workflows of users;
- Allow interactive slicing & dicing of the data, drill-down, etc.;
- Make data actionable through e.g. automatic alerts, automatic action-taking, etc.;
Want to learn how your platform can empower its users with powerful tools to mine insights? Get in touch with one of our product experts.