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Combining AI and Blockchain Makes for More Impactful Big Data Analytics

Both artificial intelligence & blockchain are revolutionary technologies significant impacts on the evolution of big data analytics. Together, even more.

SEPTEMBER 13, 2018

On their own, both artificial intelligence and blockchain are two revolutionary technologies with a significant impact on the evolution of big data analytics. Most enterprises tend to put all their eggs in one basket or the other, playing it safe due to concerns around ROI and security. Still, these tools can offer organizations a sizeable competitive advantage over competitors. After all, leveraging, analyzing and managing data effectively can help businesses make more informed decisions, reduce costs and ultimately transform business processes.

However, in order to achieve truly effective big data analytics, combining AI and blockchain together has emerged as the one true big data analytics strategy to rule them all. Let’s briefly take a look at how each of these tools factors into big data analytics separately and then showcase why they actually work stronger together.

Combining AI and Blockchain Makes for More Impactful Big Data Analytics

Processing Big Data Using AI

Data sets have become so massive today—hence the phrase “big data”—that having humans take the lead on processing them is a fool’s errand. That’s where AI and machine learning algorithms come into play. These tools are much faster and more efficient at analyzing large amounts of data successfully with less risk of human error because, let’s face it, humans bore and tire quickly with these sorts of mundane tasks.

Without these human shortcomings, but armed with the ability to learn like humans (to a certain extent), AI is one of the most reliable technologies today for drawing business critical insights from big data. As more and more data is collected, AI will also never tire of sifting through and mining it—rather, AI and machine learning algorithms only grow stronger and smarter the more data they end up processing.

Securing Big Data with Blockchain

When people say data these days, cries of protection, privacy and security are always close behind. That’s because there have been one too many massive data breaches in recent memory for people to feel truly secure. However, blockchain is a tool that businesses can employ to help safeguard against the vulnerabilities that unfortunately cling to data and provide customers with more peace of mind.

As a shared digital ledger that creates an infinite, everlasting record of transactions, blockchain is so fundamentally secure due to the minute likelihood of a malicious actor hacking every single record within. This makes blockchain extremely reliable for big data management and the secure exchange of data.

Combining AI and Blockchain Makes for More Impactful Big Data Analytics

AI and Blockchain Like Peanut Butter and Jelly

The combination of AI and blockchain can support better, more impactful big data analytics and management. However, the hidden secret in regard to these two tools is that not only are the technologies compatible and work well together, but blockchain actually plays a significant role in augmenting AI. For example, enterprises using blockchain to produce metadata from their datasets can help AI overcome one of its most glaring deficiencies: unconscious bias that emerges from the developer of a given algorithm.

With an immutable digital record like blockchain, it’s easy to monitor any changes or weird abnormalities in data that could result from unconscious bias by consulting the blockchain and making adjustments. This, in turn, allows these machine learning algorithms to evolve in efficacy and reduces the chance for this bias to creep back in when training future AI models. Employing blockchain in tandem with AI facilitates the growth and innovation of this tool, which naturally dovetails into AI’s increased effectiveness in processing and analyzing big data. All things considered, is there any wonder why AI and blockchain are the dream team when it comes to better big data analytics?

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