You Don’t Need a Cape to be a Data Science Hero
Business analysts are huge assets to any company, acting as guides for improving processes, services, products, and software. They essentially bridge a fundamental gap, ensuring that IT is constantly informed of business perspectives to properly reach technical decisions. However, the role of the business analyst is constantly changing: as data becomes a growing asset to organizations, so do they.
With the rise of artificial intelligence and machine learning, new technology and tools have left a broader base of business users and business analysts empowered to use real-time data and analytics to improve efficiency by identifying business trends, problems, and predicting outcomes—independent of data scientist assistance.
In the latest episode of the Game-Changing Predictive Machine Learning Radio Series on August 8th, host Bonnie D. Graham led a panel of three experts with business analyst experience who spoke about their personal successes and failures in adopting and using advanced analytics tools.,
- “Business Analyst by Day, Data Science Hero by Night” episode 3 (listen to the replay)
- Christopher Carter, CEO at Approyo
- Jason Olson, Analytics Specialist at Kimberly-Clark Corporation
- Samantha Wong, Product Manager for Predictive Analytics at SAP
You can listen to the replay of the show at any time. For now, here are the highlights.
The Rise of Augmented Analytics
There has been a long running stigma surrounding data science, but this dates back to a time when we didn’t have the game changing tools we do today that can take the hard part out of it for you. Today’s tools help expose business analysts to data science concepts in a non-intimidating way.
As Jason Olson explained on-air, he doesn’t need to know how to code in R or Python because he leverages a tool that automates those practices. He says that there are only “a few broad types of analysis that need to be understood” by business analysts.
Trust the Process
The panel’s conversation then led to the concerns some people have about the lack of transparency into the automated process. They worry: How were these insights generated? What algorithm was used?
Sam Wong, who comes from an advertising background, points out that it shouldn’t matter what algorithms are used. These questions are asked because of the lack of trust and uncertainty people have in computer generated results—they don’t understand how deep learning works. However, as Sam suggests, the process and math behind it isn’t important—rather, business users need to use their time to focus on:
- Understanding what the problem is that you’re trying to solve
- Knowing how you will input the relevant data into the machine
- Letting it make the decisions
As Chris says, it doesn’t come down to the components any longer. “They don’t need to see the wizard, they want to see what the wizard did for them.”
What the Business Analyst Sees in the Data
Not all companies have a data science team as big as Amazon, but they don’t all need them. If you are able to get business analysts involved in the process, they will be able to notice things that data scientists would not necessarily see as significant. Thus, further informing business outcomes by acting upon what they see immediately.
Chris Carter supports this perspective of letting the machine pave the way for business analysts’ decision making. As he said, “Data will take you where the data’s going to take you.” He goes on to explain that this may be a different direction than you were anticipating, but adds that this is what makes it so cool.
Chris challenges the traditional view of business decisions being linear, because it’s multidimensional. With automated tools, we now have the ability to bring all of the factors and parameters surrounding decision making into nice neat packages, and then “voila”—the magic happens.
Tips for Success as a Citizen Data Scientist
The show wouldn’t be complete without the invaluable advice that was given to listeners. Sam encourages listeners by telling them there won’t always be a right answer, and that is one of the beautiful things about data science. Jason advises those who anticipate adopting machine learning predictive technologies to focus on having the data prepared for business analysts so the format is already nice and easily consumed when they go to use it. Once this is done, business analysts are free to “turn loose” with the capabilities that all panelists predict will soon be commoditized as day-to-day activities.
Okay… so what does this mean for the business analysts of the future? Well, a successful business analyst has:
- An extensive understanding of their business
- Picks up new technologies well
- Has the unwavering confidence that they will ultimately be successful in the end (a particular quality that Jason Olson says all his successful business analysts display)
Want to hear more from these three game changers, or should I say, cape wearing data science heroes? Listen to the full replay online, and join us next time on August 29th 2-3pm EDT live on the VoiceAmerica Business Channel.