Recently, there has been considerable discourse surrounding the potential diminishment of data professionals’ roles due to the AI revolution. Contrary to this belief, I assert that expertise in data management will be more critical than ever. To harness the advantages of AI for their organizations and enhance their future career prospects, data professionals must adapt by acquiring new skills.
The current landscape presents an opportunity for businesses to extract greater value from their data with the assistance of AI. However, this transformation necessitates the active involvement of data specialists who comprehend the appropriate models and tools for diverse scenarios, as well as the strategic deployment of these technologies.
In the upcoming period, AI is poised to revolutionize data analysis in four key areas, prompting data engineers to upskill accordingly to meet these evolving demands.
Enhanced Data Pipeline Intelligence
The primary task of data engineers involves extracting insights from raw, unstructured data sources amalgamated through data pipelines to deliver valuable analytics. This role is on the brink of evolution propelled by AI integration.
By incorporating AI into data pipelines, data engineers can significantly expedite the process of deriving insights and value. For instance, consider a scenario where a business has access to customer support transcripts or documents in various languages. By integrating an AI model into the network, an engineer can swiftly extract profound insights from these textual data with minimal SQL commands. Tasks that would typically take hours to complete manually can now be efficiently executed by AI, uncovering invaluable insights.
Businesses stand to gain immensely from data engineers well-versed in leveraging AI models within data pipelines effectively. However, achieving this feat necessitates proficiency in selecting and applying models adeptly.
Streamlined Data Standardization
Data is often stored inconsistently across various sources, leading to disparities like one system referring to a state as “Massachusetts” while another uses the acronym “MA.”
AI excels in data standardization and deduplication tasks, allowing engineers to expedite processes such as consolidating multiple customer data sources into a cohesive database swiftly.
While proficiency in crafting efficient queries is essential, the primary benefit lies in freeing up engineers’ time to focus on strategic data initiatives rather than mundane data standardization tasks.
Evolution of Business Intelligence (BI) Analysis
Presently, BI analysts invest substantial time in generating static reports for executives, responding to follow-up queries by running new analyses. The advent of AI is set to revolutionize executives’ expectations from these reports.
As executives become more accustomed to AI-driven chatbots, they will anticipate interacting with reports in a conversational manner. BI analysts must elevate their skills to create dynamic, interactive reports by understanding the requisite tools, integrations, and prompts.
Cloud data platforms offer BI professionals avenues to enhance their skill sets to align with these evolving demands. However, mastering these capabilities will pose a challenge in 2024.
Management of Third-Party AI Services
As AI advances, data professionals will increasingly engage with external vendors providing AI models, datasets, and related services. Proficiency in evaluating options, selecting suitable solutions, and managing these third-party relationships will be imperative.
Transition to Strategic Tasks
AI’s integration into data architecture will enable professionals to delegate time-consuming tasks to AI, facilitating a shift towards more strategic endeavors. While acquiring new skills is essential, this transition will empower data engineers to focus on high-level, strategic work, enhancing their value within their teams and fostering a more engaging work environment.
