Hey, did you know that as of 2021, humans are producing roughly 2.5 QUINTILLION BYTES of data every single day?
We gather data from numerous sources, including, but not limited to, web traffic, card transactions, apps, and satellite imagery. When fed into machine learning/AI-powered systems, it opens limitless possibilities for big organizations to transform digitally and generate billions in revenue.
The question is - in a data-driven economy where the internet hosts a commodity trade of nearly $1 million every minute, what are the key takeaways for us changemakers?
Takeaway #1: New Forms Of Data Offer Unmatched Speed And Clarity
Fast-paced competition is everywhere - there’s no time for businesses to push the brakes for effective decision-making. Luckily, data-fed AI systems enable organizations to fire on all cylinders at all times. Instead of taking months, complex/strategic questions can now be answered within hours.
Data also offers unprecedented transparency, undoubtedly one of the critical aspects of a business. Suppose you’re shipping raw materials for production - data-driven AI systems can help trace the supply chain and use geolocation data to ensure ethical trade.
Takeaway #2: Data is Being Refined And Connected By Specialist Firms
Recently, service providers have stepped their game up by introducing specialized niches for efficient data refining. This makes it easier to categorize and refine different data types to feed into AI/Machine learning systems for analysis.
Startups, such as SafeGraph, focus on collecting, decluttering, and updating geospatial data for apps and analytics to use. “It’s like selling top-notch butter to pastry chefs,” says CEO Auren Hoffman. “The end-consumer may not realize that the pastry contains butter. But the chef understands how important the ingredient is.”
Takeaway #3: New Tools Underway to Boost Non-Tech Companies
In the era of data chaos, implementing a data-driven ecosystem can be tricky unless seasoned experts back you up. Although AI boosts revenue through the roof, businesses struggle to implement such sophisticated systems.
As per CrowdAI CEO Devaki Raj, “It begins with a significant lack of understanding of where all of your data is.” CrowdAI helps develop computer vision models for non-data scientists, allowing people who haven’t even heard of Python to reap the benefits of AI integration.
Takeaway #4: Only Domain Experts Can Extract The Real Value From Data
If you’re expecting a data science team to extract the most value from data, the result won’t be fulfilling. Although a data engineer or scientist may build a mind-boggling model, deep down, they still might not understand what exactly to look for.
However, a data scientist teaming up with a domain expert maximizes the final output. “The domain experts are the ones closest to the problem, which makes them the most eligible ones to inform the automation,” says Devaki Raj.
Takeaway #5: AI Ethics And Privacy Guidelines Are A Must
None of us are unfamiliar with the never-ending clash between personal privacy and data usage. Although tracking data helps organizations analyze and understand trends, consumers don’t fancy being tracked.
Businesses have always pledged to work with anonymous data; nevertheless, most of them still have questionable cybersecurity standards. According to James Crawford, “if the alternative data economy is to be sustainable, it has to value the people who contribute to it.”
Summing It Up
Max Levchin, the billionaire co-founder of PayPal, had said “The world is now awash in data, and we can see consumers in a lot clearer ways.” However, you can remain blind if you’re not making the most out of the information in the best ways.
Change comes from increased knowledge. If you have any questions or comments on similar tech/data-related articles, don’t hesitate to reach out. Our team is always here to help.