DAOs could hold the answer to better data governance guidelines

We’re excited to bring Transform 2022 back to life on July 19th and virtually July 20-28. Join AI and data leaders for sensible conversations and exciting networking opportunities. Register today!


Automation, hybrid work models, human cloud, metavers and more flexible and collaborative work environments are just a few of the trends shaping 21st century office. This is another trend that does not receive the attention it deserves for its innovation: decentralized autonomous organizations or DAOs.

DAOs are companies made up entirely of coded rules and decisions. As the term implies, being autonomous means that an entity operates almost entirely without human intervention, although it can serve as an extension of a traditionally limited liability company. In other words, people, often aligned in the common interest and without a single leader, collectively own the company and, within the project and its platform, often work across borders. All administrative responsibilities for DAO are in the competent hands of Blockchain Technology.

If, for example, DAO reaches a specific objective, codified smart contracts in blockchain technology applications will self-execute other directions. Essentially, these smart contracts define the rules for automating the company’s operational process – determined by DAO members and available for review by DAO’s blockchain.

The future of these organizations looks so bright that businesses are starting to accelerate their growth with supportive infrastructure. One example is Utopia Labs, a company I invest in. Designed in late 2021, the organization is building an operating system to make DAO more efficient. AI-focused tech leaders today have a lot to learn from this momentum. Transparency, accountability and efficiency in smart contracts can provide insights into shaping and establishing data governance guidelines for AI-powered systems.

The need for data governance of AI-powered systems

AI-powered systems have gone beyond the automation of physical, repetitive tasks, repeating without any supervision or instructions. Now, companies are using data-based AI models to accurately predict behaviors and shorten the cycle of innovation, helping to quickly bring new products and services to market without sacrificing quality. AI allows companies to more effectively monitor the system for patterns and inconsistencies with uninterrupted attention span. If the system detects irregularities, the company can take immediate action and minimize any potential risk to the operation.

More importantly, many companies are becoming more data-centric in their business models. This is where data governance policies play a crucial role as data serves as a strategic hub for generating new business opportunities. Some companies simply repackage and sell data, others use the information to guide and guide improvements, and others offer AI-powered data catalog solutions for inventory and interpreting data from a variety of sources. Wherever a company falls, data is a commodity.

As such, data governance is an absolute necessity. And DAO’s unique framework can be informative in providing guidance on how to operate AI-powered systems. Community decisions, not motivated by central decision-making authorities, promote DAO. Similarly, the entire customer-company relationship can be restructured to provide data governance.

DAOs: Creating Better Data Governance Policies

By interacting with and engaging customers while using personal data for craft services, contributors understand how information flows through each node and process. Collaboration makes the relationship closer and more repetitive. It also promotes the data collection process and enables the three most important guidelines:

1. Consumer-led product development

If AI-powered systems continue to provide product development information using the current tracking-based data model, consumers are limited to selecting products as a result of monitoring and interpreting their data and behaviors. In contrast, DAO product decisions are user-driven from the start, enabling users to understand their own needs in the product and then report design decisions specifically for those biases.

2. Repeat continuously

DAO is constantly repeated. Contributors zoom in and out of the project’s orbit, lending capacity slows down compared to the length of a traditional employment service. This accelerates the cycle of innovation and constantly fine-tunes existing products or services with new capabilities as they emerge.

3. Democratization

In DAO, contributors vote on the direction of the project, creating a feedback loop that does not currently exist in AI-powered systems. Rather than respecting complexity in simplicity, DAOs shift the arc of the data model towards community centralization by asking humans whether they accept internal decisions.

However, the DAO guide is not without difficulties. Despite being distributed, DAO may still be subject to biases, risks and manipulations. Take Maker Platform, for example. It uses the DAO framework of member voting to guide protocol development. Any decentralized exchange can invest in voting power with MKR tokens. However, those with the most MKR tokens have more influence, as their votes carry more weight. Hence the possibility of “authority” in decision making. While the community is still small, bad artists can still destroy the structure of the new regime.

We are on the first chapter of a story that weaves a path through difficulties and downsides in its path to a paradigm shift. Both DAOs and AI systems will need to be audited and regulated in a way that enables their successful journey.

Dan Connor is a common partner Ascend Venture Capital,

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is a place where experts, including tech people working on data, can share data-related insights and innovations.

If you would like to read about the latest ideas and latest information, best practices and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing to your own article!

Read more from DataDecisionMakers

Similar Posts

Leave a Reply

Your email address will not be published.