Can AI Replace Data Analyst? A Delve into the Roles and Advancements of AI in the era of “Layoffs”

Artificial Intelligence (AI) has become a significant economic driver. By integrating this technology into organizations, aiming to reduce routine work, decrease labor costs, and boost human competitiveness.

The popularity of AI in the private sector is increasing, along with the concern about it replacing human labor. This has led to job losses for people who lack the skills, but the concern is not just for routine workers. Positions such as Data Analyst, which deal with the analysis and management of data within organizations, are also being questioned as to whether Data Analysts will still be needed by organizations when AI takes over data tasks.

This debate is still a popular topic when it comes to the use of AI within organizations. In this article, we will take a closer look at the work of both Data Analysts and AI to see if it is possible for technology to replace humans. Or, if the two sides have to work together, what form will it take?

Understanding the roles of Data Analyst and AI

The role of Data Analyst is very important for organizations. Their responsibilities include analyzing, synthesizing and transforming raw data into insights and statistical information for business decision-making. They are also responsible for cleaning and managing data to make it easy to access and use.

Data Analyst has 5 main responsibilities:

• Data management: Refine and polish raw data while making it accurate and ready for analysis.

• Documentation: The most important duty of a Data Analyst is to create data documentation so that the team can understand, access and use the data.

• Statistical analysis: Manage and analyze the data provided to produce statistical results.

• Data visualization: Manage and transform complex data into a format that is easy to understand and visualize.

• Business strategy co-design: Another important role of data analysts is to help design business strategies and decisions through data insights.

Having dedicated data analysts on the team facilitates access to filtered data, reducing the workload of other teams and increasing overall organizational efficiency. Beyond data management, data analysts transform data into assets to create strategies, drive the work of other departments and find new trends to meet the needs of creating products and services for customers.

While data analysts filter and refine data, many believe that integrating AI technology can further enhance the organization's progress, unlock greater human potential and simultaneously incorporate modern technology into the working process.

In terms of data management, AI is seen as a powerful tool with the potential to  reliably replace manpower. AI's working process is effective in several ways:

• AI possesses the ability to understand: AI that has been trained has the ability to understand the content and information that is fed into it.

• AI with access to statistical results: AI can analyze data fed into its learning process and generate statistical results.

• AI as a design and KPI measurement assistant: With its ability to analyze and manage data, AI can predict and calculate KPIs for business evaluation.

• AI for managing documentation for non-specialist users: Because AI can input and learn from a variety of areas, making data analysis more accessible to non-specialists.

In addition, we are now seeing more AI Assistants or AI assistants in various business services, such as ChatGPT that can search for information and create content according to our needs. These capabilities have raised concerns, not only about the misuse of data, but also about the replacement of manpower involved in data management.

However, experts, developers and people involved in AI development recognize that AI will not be able to replace humans in data work. Instead, humans will use AI to "help" with the work instead, because AI still has many limitations and the work on the forefronts cannot be done without humans.

Impact of AI Development on Data Analyst’s Working process

In addition to the above, AI also has the potential to work in various data management tasks. Examples of features that we are currently seeing and that are worth following include:

• AI assistants for communication: Which is AI Assistants that can deliver information or content that users want, simply by searching through text or short questions. AI can immediately generate answers or results for users. Sometimes we see that even employees in the organization choose to search for answers through AI before asking data analysts.

• Document Management and Analysis: When trained on the correct data and content, AI can analyze and organize various document formats, presenting them in formats that are easy to understand, like tables and numerical summaries. This streamlined information enables more efficient further analysis.
• Prediction and recommendation: Possessing the ability to filter and analyze the data fed by developers, AI can provide recommendations and predictions of possibilities through the data or statistics, along with proposing solutions and future operations for the organization.

Although concerns about AI replacing humans are increasing, experts believe this innovative solution will primarily enhance workers’ skills within the organization, driving business forward alongside digital transformation. Organizations can embrace this potential by focusing on upskilling their workforce and adjusting work plans to foster successful collaboration between humans and technology.

However, AI still has limitations and cannot work as well as humans due to various factors. This makes many parties believe that AI will definitely not replace humans in data work.

Limitations of AI in Data Governance Management

Speaking of AI’s capabilities and effectiveness, there are numerous issues that are still controversial. The same goes for the matter of its "limitations" because as it is known that AI is also perceived as a dangerous technology.

The issue of AI governance is often debated in terms of safety, causing both the government, private sector and regulatory agencies to turn to solve problems through the establishment of appropriate regulations for the use of AI in both private and public sectors.

Important issues on AI governance also include data governance or data regulation as the efficiency of AI development is also directly limited by the efficiency of its governance.

Efficient data governance will promote AI work in many ways, such as:

• Increase confidence in original content: Good data governance will help AI produce contents or provide accurate information. However the data used to train AI must be accurate, appropriate, diverse and unbiased.

• Reduce inaccurate and biased data: Good data governance will help reduce biased and inaccurate data  for AI. By designing protocols that can be continuously improved, with tools to deal with illogical data and false data.

• Keep the content up to date: Good data governance with the ability to consistently incorporate new and accurate data will enable AI to deliver up-to-date and globally relevant information.

• Reinforce confidence as a tool to assist humans: Good data governance will reinforce that "humans" are equally important in the operations. By positioning AI as a powerful tool for efficiency, humans can focus on boosting the overall quality and credibility of the operations through critical thinking and expertise.

In summary, the issue of data governance is still a controversial matter in the subject of AI technology adoption. If it can be improved and developed appropriately, it will help AI work to be more compatible with human work.

The Future of AI and Data Analyst

Although there are still concerns about AI governance, we can develop effective methods for human-AI collaboration in the future, which can be done in a way that "humans work to guide AI" and "AI works to help humans".

Humans work to guide AI

• Training:The main function of humans in AI development is to train AI to be sufficiently effective for use. For example, the work of Siri and Alexa as AI assistants must be accurate in order to be able to assist users and become a face for the brand. Currently, we can see that tools such as AI Assistants are being developed to support complex commands and support local languages ​​better.

• Explaining: Effective AI training goes beyond simply feeding it data.  Human expertise is crucial in adding informative descriptions and context to the training data. This ensures the AI can learn and function optimally within specific industries.  For example, the pharmaceutical industry relies on medical data with precise descriptions, while the legal industry requires data enriched by the expertise of lawyers or legal practitioners.  In all cases, high-quality and accurate data is essential for reliable AI performance.

• Sustaining: Even after industry-specific training, AI systems often require human oversight. This ensures the AI's work remains appropriate, safe and continuous, particularly in areas like analyzing and guiding an organization's future direction. For instance, some banks utilize AI to screen loan applicants, but have encountered biased errors due to outdated data. Similar to Apple, which initially used AI for customer data analysis to develop products, concerns arose regarding data collection permissions. In both cases, human teams were formed to monitor, troubleshoot, and ensure responsible technology implementation.

AI Work to Help Humans

• Amplifying: AI is flawless in tasks such as analysis and recommendations to enhance decision-making for users. Organizations use AI specifically for this purpose. For example, in the design industry, tools such as Autodesk's Dreamcatcher, which has changed the industry by being a tool that can recommend interesting designs. Simply by entering the desired data, the tool will generate works in various formats based on the entered data for the designer to choose from. The designer can then edit the work or adjust the appearance to match their desire.

• Interacting: Another role where AI assists people is in customer communication. Stores of all sizes, as well as large organizations, now utilize AI-powered assistants to answer customer inquiries on online platforms. Moreover, chatbots have evolved to become capable work assistants. A prime example is Microsoft's Copilot, designed to streamline tasks and enhance efficiency within the Microsoft 365 suite.

AI can collaborate with humans, not replace them, especially in data analysis tasks. By taking over routine tasks, AI frees up data analysts to focus on higher-level work that capitalizes on their unique skills and strategic thinking.

Human judgment is irreplaceable; no tool or AI can match it. Likewise, technology cannot take on the same level of responsibility as a human.

Data analysts can streamline their work by effectively integrating AI into their processes. Since AI technology remains under development, data input will contribute directly to its learning. This includes identifying new data sources and experimenting with data generation techniques.

Even though AI will automate some aspects of a data analyst's work, tasks requiring human analysis remain essential. Therefore, the workforce may need to adapt and acquire new technological skills to enhance their collaboration with AI

References: CastorDoc, Harvard Business Review, TechTarget, Forbes