In an era marked by the rapid evolution of artificial intelligence (AI) technologies, there is a prevailing fear that machines will render humans obsolete in many professional domains. Data analysts, in particular, find themselves at the forefront of this debate, as AI’s capabilities in processing and analyzing vast amounts of data continue to expand. Gartner’s recently published paper Maverick Research: Data and Analytics Roles Will No Longer Be a Priority (May 2023) states that by 2030, the number of traditional descriptive analytics dashboards will decrease by more than 50% in most modern digital businesses, and 75% of operational decisions will be made within an AI-enabled application or process. As such the hypothesis is that as more decisions are augmented and automated via AI, many data and analytics roles will be less important. This is because the underlying data and analytics for augmented decisions or automated processes will become increasingly invisible to most users. If trust in AI grows, so will AI accuracy, and with these developments, AI will become the primary influence in most business decisions. Whereas today data literacy and user adoption of analytics tools are prioritized, in the future a shift will take place to focus on monitoring how decisions are made, and to leverage prescriptive analytics to augment the decision-making experience in context. Prioritizing decision engineering and AI governance roles, as well as the data foundation that will be required to support decisions will emerge.
However, contrary to popular belief, the rise of AI holds immense promise for the future of data analysts and analytics roles. I believe that data analysts and other related roles will not only remain relevant but will thrive in a world driven by emerging Gen AI technologies.
The Emergence of Generative AI
Generative AI (Gen AI) represents the next frontier in the development of AI technologies. Unlike its predecessors, Gen AI possesses the ability to not only perform specific tasks but to understand and learn from complex data, adapt to novel scenarios, and generate intelligent insights and recommendations. With its vast processing power and data-driven decision-making capabilities, Gen AI has the potential to revolutionize the way organizations operate.
The Evolved Roles of Data Analysts
As AI takes on more responsibilities in data analysis, the role of the data analyst will undoubtedly undergo a transformation. Rather than being viewed as a threat, this evolution should be embraced as an opportunity for growth and advancement. As per the debate for AI in other areas, the same concept applies here whereby instead of laboring over tedious manual tasks, data analysts will be liberated to focus on higher-value activities that require human ingenuity and creativity.
Data analysts will assume the crucial responsibility of verifying the insights and recommendations delivered by AI systems. While AI can process data at an unprecedented speed and scale, it still requires human intervention to validate and contextualize its findings. Data analysts can become the bridge between AI-driven insights and real-world applications, ensuring that decisions are rooted in human expertise and domain knowledge.
A Collaborative Future
In the world of emerging AI technologies, collaboration between humans and machines will become paramount. Data analysts will work hand in hand with AI systems, leveraging their technical skills and domain expertise to guide and refine AI-driven insights. By combining the power of automation with human intuition, data analysts will unlock new possibilities for faster, more accurate decision-making, and problem-solving.
Moreover, as data analysts adapt to their evolving roles, new career paths will emerge. These paths will emphasize the development of skills that complement AI technologies, such as data engineering, model interpretation, and AI system management. Data analysts will find themselves at the forefront of AI implementation within organizations, playing a pivotal role in harnessing it’s full potential.
What about the Roles that Use Data Management & Analytics Tools?
AI will also impact other roles through the data lifecycle via tools that support and feed data analysts and engineers.
- Data Observability – With the increasing complexity and scale of data, data observability tools become indispensable. These tools monitor and ensure the quality, integrity, and accuracy of data, allowing data analysts to detect anomalies, identify data drift, and maintain data health. By leveraging data observability platforms, data analysts can establish trust in their AI systems and ensure that the insights generated are based on reliable and up-to-date information. (See my article on Key Players in Data Observability)
- Data Integration, Data Fabrics and Data Meshes – The seamless integration of data from various sources is crucial for AI-driven data analytics. Data integration platforms enable data analysts to consolidate, clean, and transform data from disparate sources into a unified format suitable for AI processing. Data fabrics, on the other hand, provide a layer of abstraction, allowing data analysts to access and query data across different systems and platforms, regardless of their location or format. These tools ensure that AI systems have access to comprehensive and reliable data, enabling accurate and comprehensive insights.
- Master Data Management (MDM) – In an AI-driven world, MDM platforms play a crucial role in maintaining a consistent and unified view of critical data across an organization. These tools provide data analysts with a centralized hub for managing and harmonizing core data entities, such as customers, products, or locations. By establishing a single source of truth, MDM platforms enable data analysts to make informed decisions, ensure data quality, and enhance the accuracy of AI-driven insights.
- Data Governance, Data Catalogs and Metadata Management – As the volume and complexity of data increase, data governance and data cataloging platforms become essential for organizations. Data governance tools facilitate the establishment of policies, processes, and standards for data management, ensuring compliance, privacy, and security. Data catalogs, on the other hand, act as a centralized repository of metadata, facilitating data discovery, lineage tracking, and collaboration. These tools will continue to empower data analysts to navigate the complexities of data governance and cataloging, enabling them to effectively leverage AI technologies while ensuring ethical and responsible data practices.
- Business Intelligence (BI) Tools – BI tools have long been instrumental in data analytics, and their importance remains paramount in the age of AI. These tools provide data analysts with intuitive interfaces, visualizations, and dashboards to explore, analyze, and communicate insights from AI-driven models. BI tools allow data analysts to distill complex AI-generated insights into actionable information for decision-makers across the organization. With AI integration, these tools can leverage machine learning algorithms to uncover patterns, anomalies, and predictive insights, enhancing the overall analytical capabilities of data analysts.
- Data Science and ML Platforms – Data scientists and machine learning engineers play a critical role in developing and training AI models. Data science and ML platforms provide a comprehensive set of tools and frameworks for data scientists to design, train, and deploy AI models at scale. These platforms streamline the data preparation, feature engineering, model development, and deployment processes, empowering data analysts to leverage the full potential of AI algorithms in their analytics workflows. By combining human expertise with AI-driven models, data analysts can extract deeper insights, discover hidden patterns, and unlock new opportunities for decision-making.
Benefits for Individuals and Companies Alike
The symbiotic relationship between data analysts and AI technologies brings numerous benefits to both individuals and companies. For individuals, this collaborative future presents an exciting opportunity for professional growth. By acquiring skills that align with the evolving demands of AI, data analysts can expand their career prospects and remain at the forefront of technological advancements.
For companies, the integration of AI and data analysts will revolutionize decision-making processes. Faster, more accurate insights will enable organizations to identify trends, optimize operations, and create innovative solutions with unprecedented efficiency. Companies will benefit from lower costs, increased productivity, and the ability to leverage data as a strategic asset.
The rise of Gen AI does not signify the end of data analysts and analytics roles; rather, it heralds a new era of collaboration and growth. Data analysts will evolve into strategic partners, working alongside AI systems to validate, interpret, and contextualize data-driven insights. This partnership will enable faster, more accurate decision-making, while still preserving the critical role of human judgment.
As individuals adapt to the changing landscape, they will find new career opportunities that complement AI technologies. Companies, on the other hand, will unlock the potential of data-driven decision-making, revolutionizing their operations and reaping the benefits of more accurate, automated, and faster insights at a lower cost.
In embracing the transformative potential of Gen AI, we can envision a future where data analysts are at the forefront of technological advancements, leading the charge toward a more intelligent and data-driven world. It’s an exciting time where technology is moving at a breakneck pace, individuals, companies and incumbent vendors should understand and incorporate AI now more than ever. Like the lines from the Terminator movie “The future is not yet written …” And “Come <along> with me if you want to live <you’re best life and career>”