AI technologies like ChatGPT and Claude have shown impressive capabilities in generating human-like text and analyzing data. This has led some to speculate whether AI could automate or even replace the jobs of human data scientists. However, while AI will transform the field, human data scientists still provide unique value that current AI cannot replicate.
Far from reducing demand, the automation of lower-value responsibilities is expected to drive growth in data science as organizations realize more of its potential value. Gartner predicts global spending on data science will grow 38% from 2022 to 2027 as more complex, business-critical decisions become data-driven.
For the data engineers, data analysts, and scientists specifically, this does not indicate a decline in opportunities or value. With AI displacing repetitive tasks, data scientists will enjoy higher-value responsibilities, such as:
So in summary, AI will transform but not eliminate data science. In fact, it will elevate data scientists towards more impactful responsibilities essential for maximizing returns on data. The future is not man versus machine where ai replace data scientists but rather, man enhanced by machine.
In recent years, AI tools like large language models (LLMs) have rapidly advanced, showcasing an impressive ability to generate human-like text and analyze patterns in data. The viral popularity of ChatGPT demonstrates these capabilities, with the AI able to respond to prompts with coherent articles on a wide range of topics.
When it comes to data science responsibilities, AI as a whole provides value in automating certain tasks, and generative AI startups such as Akkio have created features which dive even deeper into these automations to maximize value for analysts:
Natural Language Processing - Many AI tools such as ChatGPT can parse text data to extract insights. This helps discover trends and sentiments in surveys, social media, and other qualitative data sources. For example, Akkio’s pre-built sentiment analysis model allows for easy social sentiment analysis, and paired with the Zapier integration, makes the analysis immediately actionable with instant Slack notifications for negative tweets or automatic retweets for positive tweets the model finds.
Data Visualizations - AI can automatically generate basic graphs, charts, and other visuals to highlight patterns in data. These help communicate insights to non-technical stakeholders. Akkio harnesses the power of generative AI to craft custom Reports and Dashboards that can be easily shared with internal teams or externally with customers, whether as standalone links or embedded in their product. If you want inspiration on ways to analyze your data, Akkio’s Generative Reporting allows analysts to request AI-built reports with just a description of their project and goals.
So in a nutshell, AI can handle rudimentary data preparation, visualization, and analysis - the repetitive grunt work of data science. But is this enough to replace human data scientists? Not likely.
While AI is automating more data science tasks, human data scientists still provide unique value AI cannot currently replicate:
Domain Expertise - Data scientists often have deep domain knowledge because of their industry, letting them better contextualize data insights. An AI lacks this background context.
Critical Thinking - Humans possess reasoning, strategic oversight, and judgment skills vital for impactful analysis. AI cannot perform complex decision making.
Communication - Data scientists interpret data and present data insights to inform business decisions by stakeholders. AI lacks the interpersonal skills needed for impactful communication.
Ethics - Humans can identify potential bias in data and analyze it in an ethical manner. Without oversight, AI can perpetuate biases.
Innovation - Data scientists design innovative new techniques for gaining unique insights from data where AI hits limitations. Their creativity pushes the field forward, and can help guide AI functions.
So in short humans provide strategic abilities, soft skills, ethics and creative problem solving AI currently lacks. But how long will this remain the case?
There are still major limitations in even the most advanced AI when it comes to higher level data science responsibilities:
Lack of Reasoning - While AI can generate text and surface patterns in data, it lacks true comprehension and reasoning ability needed to make judgment calls.
Prone to Errors and Bias - Without human oversight, AI can make false conclusions due to hallucinations or general accuracy issues and AI perpetuates historical biases present in datasets.
No Strategic Oversight - AI cannot step back and make strategic decisions on high-value problems or data science direction. It lacks human judgment and reasoning for complex decision making.
So while AI can augment and enhance many data science responsibilities, it cannot make the judgment calls and strategic decisions vital for maximizing value from data without human guidance.
AI and data science skill sets complement each other. Integrating them lets teams uncover insights impossible for either to find alone. Groups combining the strengths of humans and AI will become more common. For example, humans could generate problem definitions and evaluation criteria for an analysis while AI explores the solution space. While data scientists and analysts may be able to create predictive models, armed with Akkio, they will be able to test, validate, create, and deploy them much faster. What took data scientists months can now be accomplished in moments. Data scientists within agencies can now also create white labeled analytics offerings through Akkio, generating even more value for both their clients and company.
Given the strengths and limitations of both humans and AI, the future role of AI in data science is to minimize repetitive work and arm analysis with tools that allow them to mine insights from complex data sources more swiftly. Specific examples in Akkio include:
Repetitive Analysis - Simple descriptive analytics like averages and data cleansing can be automated to save data scientists time. In Akkio, a set of data prep tasks can be completed once, and then replicated across new datasets with similar schema, thereby automating that data prep. All reports and dashboards hooked up to live integrations automatically update as new data is pulled into Akkio.
Data merging across multiple sources would take a human days if not weeks to accomplish, but in Akkio data from different sources can be merged with ease. Akkio’s AI analyzes data from a variety of common data sources such as Amazon Redshift, PostgreSQL, mySQL, MariaDB, Google Analytics4 (GA4), Hubspot, Airtable, Snowflake, Big Query, Salesforce, and Google Sheets in a matter of minutes. Additionally, data that is missing fields would usually cause issues during analysis, but Akkio’s Fuzzy Merge merges datasets when there is not an exact matching column in both datasets to ensure proper data analysis.
Data often needs to be reworked in order to ensure the AI can properly ingest it and create error-free analysis. Akkio’s AI-powered Clean feature allows analysts to clean data 10x faster. Clean feature functionality includes standardizing data columns into an ISO 8601 date format for easy machine learning readability, removing unexpected nulls, unreadable columns and mostly blank columns, columns that have the same input for every row, replacing excess data categories with “other”, and lastly, flagging outliers, inliers, and clamp outliers.
This type of repetitive data cleaning and prepping would take days if not weeks by hand, but AI handles these tasks in one click, freeing up precious time for analysts to focus on more strategic endeavors. Similarly, Akkio's Chat Data Prep feature simplifies data preparation using simple written requests.
With Chat Data Prep, analysts can simply type natural language commands to prep the data and tease out additional insights such as "remove all columns except the first two" or "make a new column called Price per Square Foot that divides the Price column by the Area column". The ways data can be prepped are only limited by the creativity of the analyst.
So in essence humans and AI will work symbiotically, simultaneously augmenting each others' abilities. As the famous scientist Jeff Hawkins said, “AI will make humans more valuable, not less”. Data science will transform but not disappear.
So, will AI replace data scientists? AI has automated simple data tasks but cannot replicate human skills like critical thinking that remain vital for maximizing value from data. Data scientists should oversee AI, using it to enhance their productivity and focus more on high-value strategic responsibilities. With human and AI collaboration, data science teams of the near future will derive even deeper insights from increasingly complex data. So while it will transform aspects of the job, AI augments rather than replaces the essential human role of the data scientist.