In 2012, Harvard Business Review called data science the hottest job of the 21st century.
Back then, we realized that big data was a massive opportunity for new discoveries. The rise of user-generated content on social media meant that big data was coming in various forms and large quantities. At that time, data science was seen as an emerging field.
So, where do we stand over a decade later? Big data and data scientists remain significant. The U.S. Bureau of Labor Statistics predicts a 36% growth in data scientist jobs from 2023 to 2033—much faster than most other professions.
But there’s a major factor we must consider: AI. The demand for accurate, clear, and reliable data has increased in the AI era, highlighting the crucial role of data engineers, who are tasked with creating quality data pipelines that ensure trustworthy AI outcomes.
AI Brings New Responsibilities for Data Management and Governance
Data is the fuel for AI, and data engineering will continue to advance to meet the challenges of a more complex tech environment. With AI’s growth, data governance and privacy remain critical for complying with regulations like HIPAA, ISO, GDPR, or the EU AI Act. Problems such as disparate data, inconsistencies, and incompatible data types can hinder model development and pose privacy and governance risks to organizations.
Understanding the Impact of Poor Data
Low-quality data without proper processing can lead to flawed business strategies and unexpected expenses. Gartner reports that poor data quality costs organizations an average of $12.9 million annually. Therefore, data needs to be transparent and understandable at every stage—from acquisition and integration to cleaning, governance, storage, and analysis—to support business decisions.
The surprising thing about AI is that failures are rarely due to a bad algorithm or learning model. It’s usually not the math or science, but the quality of the data used to find the answer. – Dan Soceanu, Senior Manager in Technology Product Marketing at SAS
Data Sensitivities and Privacy
One of the risks with data quality is the potential for accidentally sharing confidential information, especially sensitive data in areas like healthcare. Data engineers use techniques like data masking and anonymization to protect personal and sensitive information, ensuring it can be used for analysis without revealing private details.
However, when data is fed into an AI process, precautions must be taken to prevent sensitive data from unintentionally appearing in AI outputs. Data engineers also play a role in ensuring ethical standards are upheld without bias.
“Addressing ethical concerns in AI requires a comprehensive approach focused on fairness, transparency, and accountability,” said Vrushali Sawant, Data Scientist in Data Ethics Practice at SAS. “Without clearly understanding how AI algorithms reach conclusions, there’s a risk of perpetuating societal inequalities and losing trust in their decisions.”
The Rise of Synthetic Data
Data engineers will lead the charge with emerging technologies like synthetic data. Industries with strict regulations need to build, train, and test models but often face data privacy and availability challenges. Using synthetic data in a data and AI platform can address these concerns and speed up model development and deployment.
For instance, in healthcare, synthetic data can help bridge data gaps for rare diseases, while in finance, it can resolve data privacy issues.
Forbes supports the predictions for synthetic data, expecting artificially generated datasets to become the preferred training ground for machine learning models.
“Synthetic data can address long-standing data management challenges for organizations. Companies spend a lot of time acquiring, preparing, and cleaning data for their AI development efforts,” says Brett Wujek, Senior Manager of Product Strategy at SAS. “It’s not a one-time process. It happens repeatedly. With a reliable synthetic data generation process, organizations can avoid costs related to data acquisition and preparation and essentially ‘turn the crank’ on the data they need at any given time.”
AI engineers will need to regularly review synthetic datasets to ensure they are high-quality and accurately represent real patterns—a growing responsibility with AI.
Modern Data Management and Automation
Machine learning and AI capabilities can automate repetitive tasks, allowing data engineers to focus on more strategic work. DataOps is crucial for data engineering and maintaining efficient data pipelines with high-quality data.
“The path to successful AI is intrinsically linked to modern data management practices,” says Soceanu. “Data-powered AI is often hindered by unstructured, inaccessible data across the enterprise.”
High-quality data must be ready and available to inform decisions. Finding new ways to automate and streamline data tasks will help data engineers ensure trusted data is passed to the data science team.
Alignment in the Data and AI Life Cycle
The demand for vast amounts of preprocessed data to support AI initiatives has grown significantly, with no signs of slowing down. As a result, data engineering teams are collaborating more closely with data science teams than ever. But this collaboration doesn’t end with data science. AI success is achieved when data and AI platforms support all roles, including data engineers, data scientists, MLOps engineers, and business analysts. Working within a single platform allows teams to efficiently complete the end-to-end data and AI life cycle with transparency.
As data management and governance become more crucial for ensuring trustworthy AI outputs, the importance of every role within the data and AI life cycle increases. Enhanced collaboration among data engineers, data scientists, MLOps engineers, and business analysts will lead to quicker value realization and more reliable AI. Among these, data engineers are the unsung heroes, playing a vital role in the foundational success of data and AI initiatives.
