How smart contracts and AI could work together

It’s a common refrain among IT teams: Data management challenges can hamper business agility and slow down AI-driven innovation. Why? Because as data grows and becomes more complex, good data management becomes more and more time consuming and requires a lot of effort. This type of data conundrum is what keeps Data Scientists awake at night (and not just figuratively). For data management to be well done, teams must aggregate data from several internal and external silos, reconcile inconsistencies and rely on their own information repository, to create a “single source of truth”. Additionally, businesses face constant challenges when it comes to this disparate data for regulatory compliance and reporting.

How smart contracts and AI could work together

Smart contracts have become 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) -based applications and to streamline data management functions. IT managers can now eliminate 60% of typical data management problems, overcoming a number of data challenges once and for all.

AI and Distributed Ledger Technologies Converge

Modern AI technology can perform complex tasks that were previously thought possible only for humans and in much less time. Distributed Registry Technology (DLT) provides distributed consensus on a shared registry in untrusted networks, which may contain, for example, unreachable or malicious nodes. While the best-known applications of DLT today are in the cryptocurrency realm, other applications have emerged in many industries beyond finance, such as insurance and healthcare. There is a growing landscape of use cases where AI and distributed ledger technology applications overlap.

One of the main reasons AI and DLT work well together is that the two dramatically improve data management. We have had great success in converging the DLT foundations for exchanging data and computing resources to enable AI applications in a much more effective and efficient manner than using traditional data management technologies.

DLT convergence with AI gives data providers the ability to share their data while keeping it confidential when needed and retaining the right to manage data access, enabling businesses to train algorithms securely and efficiently. on the data to derive information from it.

With DLT, the infrastructure under the hood gives developers a way to build an application across multiple organizations. While you typically build an application running inside your organization, the paradigm created for DLTs and blockchains was to span multiple organizations. Smart contacts are the programming layer of this underlying infrastructure.

Using smart contracts, the applications we have been developing since the dawn of the digital age within traditional centralized architectures can now be redesigned using a process-driven approach and deployed to an environment. shared, distributed or decentralized, which connects all participants and automates digital. process.

How Current Approaches to Data Management Work

Typically, multiple applications must work together to run business processes in order to move a workflow forward. Each application has its own database and transmits data back and forth to keep data silos in sync with each other.

This approach is fraught with complexity. Traditionally, organizations have had to rely on middleware-enabled star architectures, BPM systems that connect applications, API exchanges, and simple batch file transfers to make it all work together.

Additional complexity is added when companies try to stick to their reports
and analysis needs. For example, a marketing department might need information to perform targeting and segmentation analyzes to optimize future customer campaigns. After aggregating data from various source systems and cleaning up and reconciling inconsistencies, IT will load it into a central enterprise data warehouse for analysis or create specific data stores from a central warehouse.

Ultimately, however, the golden source of truth remains the apps from which the data comes. This means that when data inconsistencies exist, it is even more difficult to trace the inconsistency back to the system that owns that data.

Use smart contracts to improve data management

Smart contracts are a great way to link multiple application data stores together to produce
a clean version of the data without having to repeatedly aggregate and reconcile the source systems. Smart contracts bring some fundamental changes to the way we view business information and processes.

First, smart contracts turn entities into “digital assets” upon which action is taken. For example, a credit card issued to a customer is a digital asset on which a fraud flag is issued, a payment is made, a campaign is executed, etc. As a result, the entire business process of a company takes on a tangible form instead of being buried in multiple flowcharts and documents. Any commercial action results in a change in the underlying data in a deterministic manner. As any practitioner of enterprise technology knows, documentation is out of date as soon as it is produced. With smart contracts, your business process is easily codified in software, which is maintained every time you make a change and governed according to the demands of the organizational hierarchy.

Additionally, a smart contract-based app landscape enjoys a unique version of the truth in the form of the smart contract store. This data source does not need to be reconciled or aggregated retroactively. It is automatically created as business processes run over time. Data no longer functions solely as an application-driven static record. It can itself trigger events and move processes forward when changed. So, going back to our previous example, a credit card fraud alert can trigger action on the receivables payment system to change when a payment is due. New views or new data stores can be created from the central smart contract store.

This way, the old complexities associated with aggregating and reconciling data are no longer a problem. Essentially, this allows companies to embed algorithms into data, rather than embed data into algorithms. The focus is then on distributing the data to those who need it.

The smart contract approach in practice

To increase AI and automation capabilities, organizations can simply combine smart contract-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 master data, thereby aligning the underlying information to create a golden source of truth. Bots can more seamlessly connect to master 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 for creating automated and intelligent digital capabilities are becoming increasingly clear and attractive to organizations. From an enterprise and process-driven approach, they enable organizations to take advantage of features such as non-repudiation and atomic transactions, process-driven development, seamless multi-stakeholder integration and more great disregard of technical constraints.

An integration layer based on smart contracts between applications can be a powerful addition to the enterprise data management toolbox. It accelerates digital and information-driven transformation and unleashes a variety of operational benefits.

It’s a common misconception that smart contracts need a complex blockchain to function. That’s not the case here. Smart contracts can use 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 use traditional data stores and they can be adopted by any institution looking to harmonize processes and data, whatever their technological landscape.

Using smart contracts, companies can easily create contract-based orchestration layers that are independent of the persistence layer (meaning they can run on databases or data chains. blocks). This approach alleviates 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 opinions of the editorial team of App Developer Magazine.