Smart Data Fingerprinting: The Answer to Data Management Challenges

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Read more about author Sunil Senan.

The world generated 120 zettabytes of data in 2023, on track for a 1.5x growth over two years to exceed 180 zettabytes in 2025. Unfortunately, data management strategies have not kept pace with the evolution and expansion of data, largely continuing to work with old-world processes and structured information stored in historical databases. There is an urgent need to reimagine data management so enterprises can leverage the massive unstructured data contained in files, emails, websites, software codes, audiovisual content, sensors, Internet of Things, etc., existing in myriad formats and locations outside their traditional databases.

In parallel, data management solutions must also be upgraded to address various data challenges and opportunities, such as those created by advancements in artificial intelligence (AI). Thus far, data management solutions, like data management strategies, have been built for structured, governed, information and manual processes. On top, they lack the scalability and agility required to deal with the changes in the data landscape, AI models and technologies, and market regulations. And because they do not support interoperability with enterprise systems, they create data silos to impede governance and other kinds of progress. 

An autonomous data management solution can effectively handle massive datasets of structured and unstructured data and their attendant challenges. A key component of autonomous data management is smart data fingerprinting (SDF), an autonomous process infused with AI/generative AI capabilities that provides a granular, in-depth, view of all the data in an organization to create a solid foundation for AI systems and operations. With smart data fingerprinting, the organization can navigate the complexities of data – for instance, the noise and “real-time” speed of user-generated content – in a secure manner. Also, as one of the enablers of data traceability, non-repudiation, auditability, and accountability, SDF builds trust in data.

By doing all this, SDF enables enterprises to operate and innovate better to improve their position in the market. Here, we discuss some of its key advantages:

Unlock value from unstructured data: SDF empowers organizations to harness unstructured, ungoverned data existing in widespread locations and formats – product documentation, websites, images, machine data, etc. – for various purposes. For illustration, consider an AI-powered mortgage solution, which has to collect and verify a variety of customer data from their communications (email, phone calls, etc.)  as part of the approval process. To produce an accurate, reliable, and compliant outcome, the system has to ensure that the customer communications are genuine. A smart data fingerprinting solution does this by granularizing and fingerprinting the emails and call recordings. This is just one example of SDF’s many applications throughout the data/AI lifecycle. SDF transforms unstructured data to a significant business opportunity. 

Build trust in AI: According to a leading market intelligence firm, 80% of the data in the world is not governed. When enterprises consume this data through AI/generative AI and other means, they assume very significant risks, including the possibility of compromising data security, privacy, and confidentiality, violating regulatory compliance norms, or feeding poor-quality training data to algorithms to produce inaccurate, biased, or untrustworthy outcomes. Ungoverned data can also stifle growth and innovation. By ensuring adherence to trust, ethics, privacy, security, and regulatory requirements at scale, autonomous data management and fingerprinting build trust in AI, and also make the enterprise “data-ready” from an AI point of view. 

Improve AI precision at scale: High-quality data is the critical factor for achieving high precision in AI models. As algorithms continue to evolve, their ability to recognize and understand even more complex data will continue to grow. This will open doors for even more groundbreaking applications of AI. SDF acts as the foundation that supports the advancements/complexities of the data exploration and enables the evolution of algorithms to unlock new possibilities in AI. With data fingerprinting, developers can define the data requirements based on the algorithm and progressively evolve the algorithm for a specific purpose. 

Seamless ecosystem integration: By breaking down data silos, SDF promotes data exchange between systems and collaboration between teams not only within the organization but also across the ecosystem. Data transparency, traceability, and accessibility (facilitated by SDF) lay the foundation for “responsible AI” experimentation and innovation. SDF takes data management beyond compliance to drive insights and innovation, and consequently, higher business value for organizations in any industry. 

Summing Up

Smart data fingerprinting is a key success factor in AI and other data-intensive initiatives because it improves data security, regulatory compliance, transparency, and trustworthiness. However, while SDF may originate in data management, it drives value throughout the organization by supporting everything from operations to compliance to growth and innovation.