How smart contracts and AI could work together
Monday, September 20, 2021
We caught up with Guido Santos from genesis.studio to hear about how smart contracts and AI could work together, allowing organizations to accelerate transformations, add operational benefits, and can easily be adopted regardless of a technology landscape.
It’s a common refrain within IT teams: challenges with data management can inhibit business agility and slow AI-driven innovation to a crawl. Why? Because as data grows and complexifies, proper data management becomes increasingly time-consuming and effort-intensive. This type of data conundrum is what keeps Data Scientists awake at night (and not just figuratively). In order for data management to be done right, it requires teams to aggregate data from multiple internal and external silos, reconcile inconsistencies and build on a clean repository of information, to create a “single source of truth” holy grail. Moreover, companies face ongoing challenges wrangling this disparate data for compliance and regulatory reporting.
How smart contracts and AI could work together
Smart contracts have emerged as one of the most efficient and effective ways to streamline data management. By converging AI and smart contracts, there is great potential to improve AI and machine learning (ML) driven applications and to streamline data management functions. IT leaders can now remove 60% of typical data management headaches, overcoming a number of data challenges once and for all.
AI and Distributed Ledger Technologies Converge
Modern AI technology can perform complex tasks previously thought possible only for humans and in much less time. Distributed ledger technology (DLT) provides distributed consensus over a shared ledger in untrustworthy networks, which may contain, for example, unreachable or maliciously behaving nodes. Although the most well-known applications of DLT today are in the field of cryptocurrencies, other applications have emerged across multiple industries beyond finance, such as insurance and healthcare. There is a growing landscape of use cases where both AI and distributed ledger technology applications overlap.
One of the main reasons AI and DLT work well together is because the two significantly improve data management. We’ve had great success converging DLT-underpinnings to exchange data and computing resources in order to enable AI applications in a manner that is much more efficient and effective than using traditional data management technologies.
Converging DLT with AI gives data providers the opportunity to share their data while keeping it confidential as needed and maintaining the right to manage data access, enabling businesses to safely and efficiently train algorithms on the data to derive insights.
With DLT, infrastructure under the hood gives developers a way to build an app across multiple organizations. While you usually build an app running within your organization, the paradigm created for DLTs and blockchains was to span multiple organizations. Smart contacts are the programming layer of that underlying infrastructure.
Using smart contracts, applications we've been developing since the dawn of the digital age inside traditional centralized architectures can now be redesigned using a process-driven approach and deployed on a shared, distributed, or decentralized environment, which connects all participants and automates digital processes.
How Current Data Management Approaches Work
Typically, multiple applications need to work together to execute business processes to move a workflow forward. Each application has its own database and passes data back and forth to keep the data silos in sync with each other.
This approach is fraught with complexity. Traditionally, companies need to rely on middleware-enabled hub-and-spoke architectures, BPM systems that connect applications, API exchanges, and plain batch file transfers to make everything work together.
Additional complexity is layered on when businesses try to meet their reporting
and analytics needs. For example, a marketing department may need insights to perform targeting and segmentation analytics to optimize upcoming customer campaigns. After aggregating the data from various source systems, and cleaning and reconciling inconsistencies, IT will load it into a central enterprise data warehouse for analysis or create specific data marts off a central warehouse.
In the end, however, the golden source of truth is still the applications from which data originated. This means that when data inconsistencies exist, it’s even harder to track the inconsistency back to the system that owns that data.
Using Smart Contracts to Improve Data Management
Smart contracts are a great way to bridge multiple application data stores to produce
a clean version of data without having to repeatedly aggregate and reconcile source systems. Smart contracts bring a few fundamental changes to the way we look at business information and processes.
First, smart contracts turn entities into “digital assets” on which actions are taken. For instance, a credit card issued to a customer is a digital asset, on which a fraud flag is raised, a payment is made, a campaign is run, and so on. As a result, the entire business process of an enterprise takes tangible form instead of being buried in multiple flow charts and documents. Any business action causes a change to underlying data in a deterministic manner. As any enterprise technology practitioner will know, documentation is out of date as soon as it is produced. With smart contracts, your business process is codified easily into software, which is maintained every time you make a change, and governed as the organizational hierarchy demands.
Moreover, a smart contract-based application landscape enjoys a single version of the truth in the form of the smart contracts store. This source of data does not need to be reconciled or aggregated retroactively. It is automatically created as business processes execute over time. Data no longer just works as a static record driven by applications. It can itself trigger events and drive processes forward when it is changed. So, going back to our previous example, a fraud alert on a credit card can trigger an action on the payments receivables system to alter when a payment is due. New views or data marts can be created off the central smart contracts store.
In this way, old complexities associated with aggregation and reconciliation of data are no longer a concern. It’s essentially enabling companies to bring algorithms into data, as opposed to bringing data into algorithms. The focus then shifts to distributing data to those who need it.
The Smart Contracts Approach in Practice
To augment AI and automation capabilities, organizations can simply pair smart contracts-enabled technologies with AI and ML applications. The general idea is that smart contracts can be used to orchestrate processes and transactions closer to both business rules and core data, thus harmonizing the underlying information to create a golden source of truth. Robots can more seamlessly connect to core data and orchestration workflows. AI and ML applications can leverage smart contracts and harmonized data to further extend analytics capabilities, we call this full-stack automation.
The potential benefits of smart contract applications to create automated and intelligent digital capabilities are becoming clearer and more appealing to organizations. From a business and process-driven approach, they allow organizations to leverage features such as non-repudiation and atomic transactions, process-driven development, seamless multi-party integration, and higher abstraction from technical constraints.
A smart contracts-based integration layer across applications can be a powerful addition to the enterprise data management toolkit. It accelerates digital and insights-driven business transformation and unlocks a variety of operational benefits.
It’s a common misconception that smart contracts need a complex blockchain to run. That’s not the case here. Smart contracts can utilize traditional data stores and they can be adopted by any institution looking to harmonize processes and data, regardless of their technology landscape. Several smart contract technologies such as Daml, Java/Go/Node.js (Fabric Chaincode), Kotlin (Corda), and Solidity/Vyper (Besu) can utilize traditional data stores and they can be adopted by any institution looking to harmonize processes and data, regardless of their technology landscape.
Using smart contracts, companies can easily build contract-driven orchestration layers that are persistence layer agnostic (meaning they can run on either databases or blockchains). This approach mitigates the data management problem, reduces the complexity of adoption, and accelerates the time-to-market for AI and analytics programs by bridging data silos without requiring constant reconciliation.
This content is made possible by a guest author, or sponsor; it is not written by and does not necessarily reflect the views of App Developer Magazine's editorial staff.
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