The changing role of the data team in 2024
How AI and LLMs will redefine the role of data specialists
Summary:
In 2024, AI and LLMs will change existing roles within data teams. Data specialists will step up their game by embracing new tech and increasing their focus on organizational security. Thanks to the impact of AI, they'll acquire new areas of expertise, like AI Governance and Data Security. Understanding tools, such as Automated Machine Learning, and adopting software development practices will make their work smarter, not harder. With a stronger focus on data protection and regulatory compliance, data teams will need to pick up new skills. Their goal? To use data more intelligently, sparking innovation and keeping pace with technological advances.
Introduction
In 2024, there’s enormous potential for change in the realm of data engineering and analytics. The rapid evolution of AI and the emergence of new data trends and concerns will redefine the role of data teams.
Despite cost pressures and the demand for profitable ROI, investment in data analysis and management will grow in 2024. Businesses will be laser-focused on data teams, as catalysts for growth, efficiency, and risk mitigation. They’ll continue to support business teams in their decision-making tasks, but, with a difference. The emergence and rapid evolution of AI and LLMs will lead to interesting new roles for data team members. The increasing importance of security, semantics, data products, and AI governance amplifies the need for highly skilled data teams. Anticipate the emergence of exciting new roles such as AI Governance Specialists, Data Product Architects, and Data Security Officers.
The role of the data team has evolved from support to a key driver of business success. In response, businesses urgently need to invest in robust data capabilities to stay ahead of the game.
AI is the carrot
LLMs (Large Language Models) and AI (Artificial Intelligence) will have a huge impact on every aspect of the modern data stack. The exciting partnership between LLMs and data analytics combines vast amounts of data with the power of semantics and knowledge graphs to provide invaluable context. Data and analytics professionals will have front-row seats as this symbiotic relationship revolutionizes data transformation, preparation, analysis, and interpretation.
Amidst the excitement, remember not everything that glitters is gold. Organizations are still grappling with about 80% of their data being unstructured. LLMs, as efficient initial filters and powerful classifiers, can play an important role here. They enable us to extract insights and build machine learning features from unstructured data, such as customer support dialogues and sales discussions.
Semantic models and knowledge graphs are becoming indispensable components in the data team toolkit and in the future of AI. Along with the reusability of metric definitions, semantic models add context to data, making it easier for end users to consume. These models also enable AI systems to extract meaningful insights, even from vast and complex datasets. Knowledge graphs provide an effective way to organize and interlink data, acting as the connective tissue that allows AI to understand relationships and connections.
In 2024, advancements, such as Automated Machine Learning (AutoML), AI powered identification of patterns, trends, and anomalies, and explainability will impact data scientist and analyst roles. Some traditional tasks will be replaced, but new ones will emerge, opening up different ways for data teams to provide more value to the business. As roles change, so will working relationships with other teams, for example, analysts may increasingly interact with data ethics and AI engineering groups.
Security is the stick
The evolution of the modern data stack will almost surely lead to a rise in security incidents in 2024. With the growing adoption of LLMs and the widespread use of data products, the need for enhanced data security during large-scale data transfers is inevitable. Now, more than ever, it’s crucial for businesses to invest in advanced security systems that ensure data protection and regulatory compliance. This increased focus on security may lead to the emergence of new security-oriented roles within data teams and the implementation of cutting-edge technologies such as encryption and secure multi-party computation. As data engineers and data analysts, understanding and addressing these security challenges will be essential in maintaining the integrity and safety of data infrastructure.
Collaboration between roles and perhaps the definition of new roles is essential in an AI driven, privacy sensitive world. For example,
Data Scientists and AI Engineers will need to protect models from attacks, data poisoning, and unauthorized access.
Data Privacy Officers will need to ensure the data used to train and deploy models complies with privacy regulations.
Analytic Engineers and Data Analysts will need to ensure transparency and explainability in AI models to build trust and address potential risks.
Data Governance roles will need to properly manage regulatory compliance, including how data is shared and secured among third-party vendors.
But it's still about doing more with less
In practices and methodologies, data teams are increasingly resembling their software counterparts, for example:
Collaborating with other teams, business-wide.
Coordinating and managing tasks using agile practices, such as Scrum and Kanban.
Applying DevOps principles, such as CI/CD and a split between engineering and operations functions.
Having a product mindset, incorporating versioning, automated testing, and a user-centered, product-owner philosophy.
These positive changes are setting the stage for new data products. The convenience of Data-as-a-Service (DaaS) and LLMs, combined with affordable storage and compute capacities, has simplified the data product creation process. As the dependence on reliable data grows, data teams are evolving, supported by products specifically designed for them and their new role. This trend will directly impact resources, costs, and ROI calculations, highlighting the need for adjustments in response to the tightening observed in 2023.
Two interconnected trends, expected in 2024, will focus on enhancing efficiency, agility, and cost savings in the realm of data engineering and data analytics. The first trend revolves around small data, with data teams leveraging in-memory/in-process databases to quickly analyze and move data. This enables faster insights and streamlined data operations. The second trend, workload offloading, strategically shifts resource-intensive queries to cost-effective query engines, based on demand. While these two approaches change the performance characteristics of the queries, their cost-effectiveness and versatility will help businesses do more with limited resources. Stay ahead of the curve in data engineering and analytics by embracing these trends!
Onwards and upwards
In 2024, the landscape of data and analytics will continue to evolve, revolutionizing the way businesses operate. Semantic models and knowledge graphs will play a crucial role in unraveling complex datasets. The adoption of LLMs and DaaS will streamline the development of data products, highlighting the growing influence of data teams in driving innovation.
However, with the growing emphasis on regulatory and compliance requirements, data security and privacy will pose significant challenges. Data engineering and analytics teams will assume a more critical role requiring the emergence of new roles and skill sets. To succeed in these challenging times, it’s more important than ever for companies to embrace data literacy and invest in robust data solutions to make informed decisions.