How AI is redefining the role of databases

Today’s AI capabilities rely on vast amounts of data, causing data professionals to rethink their roles within the enterprise.

The AI ​​revolution we are engulfed in today is advancing at breakneck speed, and 77% of business leaders are already worried they are missing out on its benefits, according to a November 2023 Salesforce study. doing.

But with the near-limitless potential applications of AI, where should organizations focus first? The most valuable commodity a company owns: its data, and what is most closely related to its maintenance, manipulation, and consumption. About the position. After all, today’s famous generative AI models are only as good as the vast amounts of data used to train them. A competent manager of that data asset is essential.

AI will replace few, if any, data-related roles. Instead, AI-powered software will enhance its capabilities and encourage ambitious data professionals to jump into acquiring new and in-demand AI-related skills. Here’s a quick summary of how AI is impacting the role of data across your organization.

Chief Data Officer (CDO)

The CDO role is one of the most demanding C-level jobs in the IT industry, and according to Harvard Business Review, CDOs only stay in the role for an average of two and a half years. AI has the potential to transform CDOs by providing new opportunities to deliver value to enterprises.

Until recently, the CDO’s office was considered a cost center for ensuring data governance, integrity, and security. AI elevates CDOs in important ways. First, it adds rich automation to improve data quality, database performance, data analysis, and overall better results. Second, AI applications, from chatbots to price optimization tools to predictive analytics, rely on vast repositories of high-quality data, and many of these applications are already generating new revenue.

But AI also adds important new duties for CDOs. CDOs must ensure that AI training data does not produce biased results. A typical example is inadvertently associating risk with minority borrowers, job applicants, business partners, etc. Avoiding bias in AI is also the responsibility of AI app developers, so collaborative testing should continue.

data architect

Data architects deliver CDO vision, policies, and initiatives through effective planning and design. It starts with data modeling. This means gathering and analyzing data requirements and developing corresponding logical and physical models. AI-powered data modeling is in its infancy, but as the technology matures, architects will be able to create more sophisticated and accurate models.

Data architects can use AI-enabled tools to identify data usage trends with the goal of establishing optimal data location, storage performance, and data security to serve applications across the organization. . Such analysis can be extended to predictive capacity planning, allowing architects to decide which data should be stored on which platform, on-premises or in the cloud, now and in the future.

Data engineers and integration specialists

Data engineers typically focus on infrastructure and manage data at the system rather than the organizational level. Data integration specialists, on the other hand, tackle the age-old problem of mixing and coordinating data from multiple repositories for any number of business applications. These two overlapping roles are already benefiting from AI.

The main issue in this area is metadata management. This means organizing all the important information that describes the data that is useful to your company, regardless of its origin or platform. AI tools already exist to help surface and normalize metadata schemas for data mapping and integration. Some automate the creation of data pipelines that form the structure of data integration. New AI products can continuously monitor the quality of data flowing through the pipeline and flag inconsistencies in real-time.

Database Administrator (DBA)

Managing an enterprise database is a job with many facets, from performance tuning to intensive SQL queries to ensuring availability and security. DBAs typically need to balance the requirements of different sets of users while minimizing disruption as data stores grow and new versions of database software arrive. Again, AI reduces time spent on simple tasks and allows DBAs to spend more time understanding and meeting stakeholder needs.

But the big win is in optimization. By using AI-powered tools to analyze performance characteristics, DBAs can flag bottlenecks, predict future infrastructure limitations, and actually add capacity without human intervention. AI tools that examine the database itself can suggest indexing adjustments or recommend changes to queries that provide better results faster.

data scientist

AI probably offers the greatest benefits to data scientists, jobs that require advanced skills in programming, machine learning (ML), mathematics, and data analysis tools. For example, automated ML (AutoML) greatly reduces model development tasks, such as choosing the right machine learning algorithm for the job. Additionally, like any other programming, data scientists who write Python or R code can benefit from the productivity gains provided by AI coding assistants.

Data scientists have a broad perspective and leverage vast amounts of data to identify long-term trends, risks, and opportunities for a company. This process is powered by new analytics software that incorporates AI. But this job comes with a little secret. Data scientists spend most of their time sourcing, cleaning, and preprocessing data. While AI-powered data cataloging accelerates sourcing, AI tools are emerging to help meet the six dimensions of data quality: accuracy, completeness, consistency, uniqueness, timeliness, and validity. I am. This foundation adds value to data analytics across the enterprise.

data analyst

Data analysts, like data scientists, are leveraging new AI capabilities built into modern analytical tools, but data analysts typically focus on domain-specific decision support rather than big-picture insights. I’m leaving it there. Over the years, AI has powered predictive analytics, but new iterative ML capabilities are improving pattern (and anomaly) recognition and yielding more accurate predictions. AI can also automatically generate dashboards, providing the best visualization for the task at hand.

All this automation has the effect of expanding access to data analysis. Natural language interfaces allow those lacking query language skills to perform their own analysis, and the guidance provided by AI helps prevent unskilled people from making rookie mistakes. . AI is forever changing analytics at breakneck speed, vastly expanding capabilities and providing more powerful self-service tools to a wide range of business analysts.

software developer

Strictly speaking, software developers are not data experts, but clearly they work with large amounts of data in the form of millions of lines of code. At the same time, many developers are integrating his ML capabilities into applications that process all kinds of enterprise data. In both cases, AI-based coding assistants are having a double-digit impact on developer productivity.

Coding Assistant does more than just complete repetitive lines of code. By using vast open source code repositories and natural language queries of their own proprietary code bases, developers no longer have to heroically track down obscure syntax details. Coding assistants can provide well-formed services according to coding rules established by the developer’s organization. In some cases, the coding assistant may also recommend the appropriate machine learning algorithm for a particular application task.

Conquering the enterprise with AI

It is no exaggeration to say that no emerging technology has had a faster and more widespread impact than AI. Data managers and developers are the most affected, but professionals in marketing, product development, service operations, risk analysis, and more are riding the hockey stick of AI adoption. Improvements in data quality and analytics are already being felt across the enterprise. Perhaps the most surprising fact is that we are just getting started.

Jozef de Vries is Chief Product Engineering Officer at EnterpriseDB.