Data Integrity: What is It, and Why Does It Matter?

TD;DR

• Poor data quality costs businesses billions annually and undermines trust in data-driven decisions, AI models, and Big Data analysis.

• Zero-Knowledge (ZK) technology offers a promising solution by verifying data without exposing it, but it faces scalability, cost, and integration challenges, particularly with existing blockchain standards.

• Horizen 2.0 emerges as a game-changer for ZK applications, addressing these challenges with streamlined proof verification, robust security, and developer-friendly tools.

• By advancing ZK technology, Horizen 2.0 aims to ensure secure and cost-effective data integrity solutions, empowering developers, businesses, and consumers in the AI and Big Data era.

Bad Data Is More Costly Than You May Think.

According to Gartner, poor data quality costs organizations an average of $12.9 million per year. According to IBM Big Data & Analytics Hub, this equates to about a 3.1 trillion dollar cost to the US economy.

While many think that poor data integrity is due to malware, that is a smaller part of the story. Most errors are due to internal and unintentional causes, like mistakes in data collection, inconsistencies in formatting, and human error.

Data Integrity Becomes Increasingly Critical as We Enter the Age of AI.

AI models require vast amounts of data input, and if that information is inaccurate going in, it will be inherently flawed coming out. As the saying goes, “garbage in means garbage out”—except with AI, the amount of data that requires verification is exponentially higher.

To maintain data integrity, it is important to ensure the validity, consistency, and completeness of data entry, integration, and distribution. Data integrity also includes safeguarding data—ensuring the safeguarding of data from unauthorized users and compliance with regulatory bodies.

So, how do we verify the 402.74 million terabytes of data that is produced every day? This article will review:

• the challenges to data integrity,
• ZK proofs as a potential solution,
• limitations to ZK proofs
• and how we can address them to develop a solution that is affordable, scalable, and secure - as this is the only way organizations can effectively adopt the innovative technology.

There Is No Trust Without Integrity - and the Same Goes for Data.

Nearly 67% of data-driven companies do not trust the quality of their data according to a report by the LeBow College of Business. This impedes them from making qualified, intelligent business decisions, effectively leveraging AI models, or benefiting from Big Data.

Threats to datasets include:

• Human error: deleting critical data or inputting inaccurate data.
• Inconsistencies across formats: tracking the same data differently across systems that require integration.
• Collection error: collecting incomplete data or inaccurate data.
• Cybersecurity and internal breaches: malicious actors stealing or corrupting data.

These threats expand when data is shared across multiple systems - internal or external. And cost significantly more the bigger the datasets become. This is all before any compliance or regulatory fines due to data breaches are taken into account.

So, How Do We Solve This Multi-Trillion-Dollar Problem?

Zero-Knowledge (ZK) technology is one solution that is becoming increasingly popular.

ZK proofs can verify knowledge about a piece of data without revealing the data itself. One key benefit of ZK proofs is that it preserves privacy even within transparent systems like a public blockchain (ie: Ethereum).

This technology has countless applications in the real world, and we are seeing new use cases emerge as adoption becomes more common. These range from identity management (being able to verify you are of age, or the right person, etc. without having to reveal your identity), to private transactions (keeping your wallet and financial transactions private), to supply chain traceability, and more.

The challenge with ZK proofs is that they are resource-intensive. They are not inherently scalable, and verifications can be quite costly - and these costs will only increase with AI models becoming more mainstream.

The vast majority of the Web3 space is built on EVM-compatible applications; however, the EVM standard was not initially designed with ZK capabilities in mind, which necessitated a new set of tools and protocols to build ZK applications (which are not EVM compatible).

Unfortunately, these advancements come with a caveat: the need for different programming languages, interfaces, and tools, which can create a barrier to communication and fragmentation within the broader Web3 community.

Horizen 2.0: the Blockchain Optimized for ZK Applications

Developers of ZK dApps often encounter difficulties in writing and auditing verifier contracts, which are essential for validating ZK proofs. These custom contracts are not only complex but also prone to vulnerabilities that can be exploited.

The need for a more streamlined and secure solution has led to the development of Horizen 2.0, which offers built-in precompiled contracts to handle proof verification, thus reducing development overhead and enhancing security.

Horizen's native Layer 1 architecture maintains complete decentralization through its own network and infrastructure, ensuring robust security and uninterrupted operation.

Data Integrity is Vital for Data Trust and Business Continuity: Horizen Is Going to Make It a Reality

Data integrity challenges are growing alongside our dependency on big data and AI. New technologies are constantly emerging to help solve the problem of data integrity, like ZK proofs, but they are not without their own limitations.

Horizen began its journey into the ZK space in 2017 and has since realized and set out to resolve these limitations so that any developer looking to leverage ZK technology can do so, securely, efficiently, and without the heavy costs generally associated with ZK proofs.

Horizen 2.0 will be a new frontier for ZK technology and a space where innovative developers can highlight their creativity, organizations can benefit from better data quality, and consumers can rest assured in their data privacy.

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