Attaining Desirable Results: The Importance of Viewing AI as an Integral Part of Data Solutions
In numerous organizations, AI remains concealed in restricted usage, serving as a hidden resource. Instead of being integrated fully into operations, AI is employed on a limited basis, then relegated back to its compartment. This limited application prevents AI from reaching its true potential in impacting a business profoundly. This is where DataOps comes into play.
modeled after DevOps, DataOps is an approach that can help organizations maximize their AI's potential across their business ecosystem. Although similar to DevOps, DataOps focuses on automating the management, testing, deployment, and replacement of data, rather than code. Companies that successfully apply DataOps techniques to anchor their AI data products against unified data stores facilitate the democratization of AI within their business, leading to a competitive edge.
Data products are ubiquitous, encompassing applications such as BI dashboards, machine learning models, and drug discovery platforms. These products equip large user groups with controlled access to clean, well-managed data.
The GenAI Revolution's Influence on Data Product Design
The emergence of generative AI (GenAI) has brought forth a new breed of data products. Large language models (LLMs), such as OpenAI, ChatGPT, and Google Gemini, provide businesses with powerful tools, including chatbots, coding co-pilots, and AI agents to optimize user experiences.
As the GenAI revolution is still in its infancy, companies are grappling with integrating these new capabilities into their existing infrastructure. A critical hurdle is providing these new AI data products with secure, governed access to the organization's historical and operational data stores.
Rushing to capitalize on GenAI, companies might be tempted to construct their data infrastructure conventionally. They may set up a new data warehouse or carve out a data mart in an existing one, where they can meticulously stage and carefully prepare the data that will serve the GenAI application. However, adopting this traditional approach leads to creating more data silos, intensifying data consistency issues, and adding excessive data engineering overhead.
Bridging GenAI and Data with DataOps
To increase agility and magnify the impact of AI data products on business outcomes, companies should consider adopting DataOps best practices. Imitating DevOps, DataOps encourages developers to break projects down into smaller, more manageable units that can be developed, tested, and deployed individually and more efficiently to data product owners. Instead of manually constructing, testing, and validating data pipelines, DataOps tools and platforms enable data engineers to automate those processes, generating high-quality data and promoting trust in the data itself.
Although DataOps predates the GenAI revolution, it has been instrumental in modernizing data environments for building BI and analytics tools powered by SQL engines or for building machine learning algorithms powered by Spark or Python code. In particular, the GenAI revolution has made DataOps even more indispensable and valuable. If data serves as the fuel propelling AI, then DataOps has the potential to significantly improve and streamline the back-end data engineering labor required to connect GenAI and AI agents with data.
The good news is that AI can enhance the DataOps process. Just as DataOps can provide clean, trusted, and reliable data for GenAI data products, GenAI can be harnessed to instill more automation into the DataOps process itself. Pairing an LLM with an existing DataOps platform promises new heights of automation and data-driven insights, expanding the pool of people qualified to manage the DataOps platform and amplifying the number of AI data products that can be pushed into production.
How DataOps + GenAI Combination Simplifies Data Engineering
Despite the high degree of automation offered by DataOps platforms, much of their value still requires the expertise of an experienced data engineer. Building, testing, validating, and securing data pipelines remain essential tasks. However, by integrating a generative AI model with an existing DataOps platform, unprecedented automation and data-driven insights can become achievable. This approach reduces the number of full-time equivalent (FTE) data engineers needed and broadens the pool of individuals qualified to manage the DataOps platform, thereby expanding the range of AI data products deployable in production.
Instead of dedicating half a dozen or more FTE data engineers solely to maintaining existing data pipelines, a DataOps/GenAI approach can drastically diminish the requirement for such specialized personnel. The fundamentals of YAML, Python, SQL, data models, data catalogs, and API data tools are no longer mandatory prerequisites for entry-level data engineering roles.
This is not to say that GenAI-powered DataOps platforms run autonomously. Humans remain in control and approve workflows. However, it alleviates humans from performancing the rote, manual work of constructing pipelines. Benchmarks like SWE-bench demonstrate that GenAI copilots have made considerable strides in understanding requirements and writing code, not to mention documenting their own work for validation. So why not use these GenAI capabilities in data engineering?
Thanks to GenAI-powered DataOps platforms, it is possible today to allocate a fraction of one FTE to managing data pipelines, allowing a company to allocate more data engineering resources to challenging tasks, such as determining valuable data and sharing knowledge of data with software developers and AI engineers collaboratively.
GenAI has a significant impact on data engineering and has the potential to streamline the DataOps work involved in data products. The scope of potential applications for GenAI is virtually endless, but GenAI's effectiveness is potentially limited by one crucial factor: enterprise data. By building automated data platforms that can be easily accessed by developers, companies can remove obstacles to data, democratize AI within their organizations, and achieve their business objectives.
- The integration of generative AI (GenAI) and DataOps can significantly improve the back-end data engineering labor required to connect GenAI and AI agents with data, as DataOps tools and platforms enable automation of data management, testing, deployment, and replacement, thus facilitating the democratization of AI within organizations.
- By pairing an LLM with an existing DataOps platform, unprecedented automation and data-driven insights can become achievable, reducing the number of full-time equivalent (FTE) data engineers needed and broadening the pool of individuals qualified to manage the DataOps platform, thereby expanding the range of AI data products deployable in production.
