Trusted data might be the "most important asset you own," a rare commodity in the fast-moving, go-for-broke world of modern business. Every decision needs to be precise and every opportunity a nanosecond in the making. Using unverified or untrusted data can be damaging to any business and harmful to customers.
At the beating heart of the tech boom lies a concept almost too simple to explain, let alone to execute properly: trusted data. It is the foundation powering AI and the avenue through which enterprise software mends itself to the changing world. So, what exactly is trusted data and how is it achieved?
Not all data is equal. The success of AI, analytics, machine learning and the outcomes businesses are driving toward hinge on the quality and trustworthiness of their data. Imagine AI as a grand cathedral soaring into the skies. Build its foundation, solid and unyielding, upon trusted data. Just as a cathedral's stability rests on the strength of its cornerstone, the efficacy of AI and data-driven organizations rests on the quality of the data it ingests and uses.
The answer to why trusted data is so crucial lies in the essence of AI itself. AI algorithms, and businesses in general, learn and make decisions based on the data they're fed. If it relies on flawed, biased or incomplete data, then organizations using the data will likely make a flawed decision. If decision-makers in companies or consumers don't trust data, they will ultimately not rely on it and make alternative decisions. TechTarget's Enterprise Strategy Group reported in its January 2024 study "The State of Data Ops: Unleashing the Power of Data" that 62% of line-of-business stakeholders said they only somewhat trust their organization's data.
Building data trust starts with data quality, which is desirable and imperative. Organizations should ensure their data is accurate, relevant and timely. Like a chef meticulously selecting the finest ingredients for a gourmet dish, data stewards must curate and refine their data sources to ensure optimal results.
Next is the equally vital data governance, which is the framework that guides the management and use of data. Data governance establishes the rules, policies and procedures that ensure data integrity, security and compliance. It sets guardrails that follow regulatory requirements and personal data protection rights to keep data from falling into the abyss of misinformation and mistrust.
Another building block of data trust is knowing the data's history or lineage. Understanding the lineage of data -- its origin, transformations and movements -- is essential for establishing trust. Just as historians piece together the past from fragments of evidence, data lineage provides a narrative of how data evolves and travels through an organization.
Data lineage isn't only about historical record-keeping. It's about accountability and transparency. Knowing where data comes from and how it's manipulated instills confidence in its reliability and authenticity. As AI and data-driven technologies become more pervasive, lineage should extend to data audits, which will inevitably arise. Regulatory bodies and consumers alike should demand transparency and accountability in data acquisition, use and protection. Data audits should scrutinize data practices similar to how financial audits scrutinize a company's fiscal health.
A successful data strategy embraces the twin imperatives of data quality and governance, while also understanding data lineage and preparing for accountability over data use.
Imagine a future where AI algorithms get audited for bias and fairness. Data-driven companies should demonstrate the legality of their data practices and their right to use the data on which they run their businesses. Data use agreements might become a large part of future audits. This future isn't far off.
Data intelligence is also a critical requirement in the quest for trusted data. It encompasses the tools, technologies and processes that enable organizations to derive actionable insights from their data. It's the bridge between raw data and informed decision-making, illuminating the path to success. Data intelligence also plays an important role in democratizing data. Empowering employees at all levels with access to timely and relevant data enables organizations to unleash the full potential of their workforce. Decision-making can be agile, informed and democratized, propelling organizations to new heights of growth and innovation.
Data quality, governance, lineage and intelligence are four of the building blocks organizations must consider as they build a trusted data foundation. Also, consider the importance of building a culture of data trust. Foster an environment that protects and nurtures data as a precious asset. A successful data strategy embraces the twin imperatives of data quality and governance, while also understanding data lineage and preparing for accountability over data use.
The bedrock of an AI's existence is not the novelty of the algorithms but the quality and governance of its data, which should be a core element when building AI and large language models (LLM) for generative AI. LLMs should be accountable for the quality they provide to consumers and businesses.
The big elephant-in-the-room question is if a technology vendor builds a generative AI model built on an LLM they license and a customer relies on the data used, who is responsible if damage happens? The LLM provided bad information, the technology vendor built the LLM into their process and the customer used the bad data.
At the end of the day, organizations built on a foundation of trusted data should encounter fewer issues and ultimately win in the race to achieve success.
Stephen Catanzano is a senior analyst at TechTarget's Enterprise Strategy Group, where he covers data management and analytics.
Enterprise Strategy Group is a division of TechTarget. Its analysts have business relationships with technology vendors.
