Reliable data is critical for AI success
AI is only as good as the data it relies on. Right now, that data is often messy, unstructured, and lacking governance. This is a real problem for companies trying to scale AI initiatives. If the data going in is unreliable, the results coming out will be unreliable too. That’s why a modern data strategy is vital.
Many enterprise architectures are still operating with outdated frameworks that weren’t designed for AI workloads. The volume, variety, and velocity of data have increased significantly, yet data governance hasn’t kept up. Companies that don’t address this will find their AI efforts underperforming—or failing entirely. AI models depend on structured, high-quality data to generate reliable insights. If you’re running a business that relies on AI-driven automation, analytics, or forecasting, prioritizing data hygiene will determine whether your AI efforts succeed or remain stuck in proof-of-concept mode.
Data professionals who understand this gap will be in high demand. Organizations will need experts who can implement governance frameworks, clean up inconsistent datasets, and optimize data pipelines. Businesses that take data quality seriously will have a real advantage. When data is well-structured and properly managed, AI systems are faster, smarter, and more cost-effective.
Drew Firment, AWS Community Hero and Enterprise Strategist at Pluralsight, highlights that most enterprise data today remains unstructured and ungoverned. This directly affects AI’s ability to function at scale. Companies that invest in modernizing their data infrastructure now will be the ones fully unlocking AI’s potential in the coming years.
For decision-makers, the takeaway is clear: AI is a tool. And like any tool, it depends on the materials it’s working with. Ensure your organization is supplying it with clean, structured, and well-governed data, or expect diminishing returns on your AI investments.
SQL remains an essential skill for data professionals
Despite all the advancements in AI and automation, SQL is still one of the most valuable skills for data professionals. Companies generate and store vast amounts of structured data, and SQL remains the most efficient way to access, manage, and analyze it. Whether you’re working with large-scale enterprise databases or cloud-based platforms, SQL is fundamental. It’s not going anywhere.
The demand for SQL expertise is reflected in the numbers. The 2025 Pluralsight Tech Skills Forecast ranks SQL as one of the top skills in tech, with the fourth most-taken Skill IQ test in 2024. This reinforces its continued relevance across industries. Organizations need professionals who can write efficient queries, optimize database performance, and integrate SQL with cloud solutions like Amazon Redshift, Google BigQuery, and Azure SQL Database.
What executives should focus on is how SQL fits into a modern data strategy. SQL enables businesses to process and analyze structured data efficiently, which is critical for AI, machine learning, and real-time analytics. Teams working with data need to go beyond basic queries. Advanced skills like indexing strategies, query optimization, and performance tuning make a real difference in productivity and operational efficiency.
For data professionals, the priority is clear. If you’re new to SQL, start with core functions like SELECT statements, joins, and subqueries. If you already have experience, go deeper into optimization techniques and cloud-based SQL solutions. Companies are pushing more of their workloads onto cloud platforms, and SQL remains a central tool in managing these systems effectively.
Decision-makers should recognize that SQL expertise is a business necessity. A workforce proficient in SQL is better equipped to handle data-driven decision-making, ensuring that insights are accessible, accurate, and actionable.
Investing in SQL training now makes sure your team is prepared for the increasing complexity of modern data environments.
Stricter data governance and privacy regulations are reshaping responsibilities
Regulations around data privacy and security are becoming more stringent. Governments and regulatory bodies are increasing scrutiny on how companies collect, store, and use data. Businesses that fail to comply will face legal and financial consequences, not to mention the reputational damage that comes with a data breach or compliance failure.
The demand for professionals who understand data governance is growing. It’s no longer enough to store and retrieve data efficiently—companies need experts who can implement security measures, maintain data integrity, and ensure compliance with regulations like GDPR and CCPA. Organizations are prioritizing Data Compliance as a Service solutions to keep up with evolving legal frameworks. This shift means that data security can no longer be an afterthought. It must be integrated into business strategies from the start.
According to the Pluralsight Tech Skills Report, businesses are focusing on data security and governance more than ever. Executives need to recognize that addressing compliance early reduces long-term risks. The costs of regulatory penalties, data breaches, and loss of customer trust far outweigh the investment needed to build proper governance frameworks.
For data professionals, staying competitive means developing expertise in compliance, privacy laws, and security best practices. Encryption, anonymization, and access control policies are now essential skills. Companies working with AI must also account for AI governance—ensuring that models remain transparent, free from bias, and aligned with emerging ethical guidelines.
Decision-makers should act now. Compliance is not optional, and as regulations continue to evolve, businesses that lack strong governance policies will struggle. Investing in secure, transparent, and well-managed data operations ensures long-term success while avoiding legal and ethical pitfalls.
Foundational machine learning literacy is becoming essential
AI and automation are reshaping industries, and companies are integrating machine learning (ML) into core business operations. Data professionals who understand even the basics of ML will have a significant advantage. The ability to work with AI-driven analytics, automation tools, and predictive modeling is becoming a critical requirement.
Businesses that fail to adopt these technologies risk falling behind. The 2025 Tech Skills Forecast highlights that professionals with foundational ML knowledge hold a competitive edge. Understanding how models are trained, evaluated, and deployed allows teams to collaborate more effectively with data scientists and AI engineers. Even fundamental concepts like regression models, clustering techniques, and feature engineering contribute to better decision-making.
For executives, the goal is to ensure their workforce is prepared for this shift. AI is not replacing data professionals—it’s enhancing their capabilities. Employees who understand how training data impacts AI performance can help refine models, detect potential biases, and improve the accuracy of predictive analytics. This is especially important as industries move toward AI-driven decision-making.
Investing in ML education now will pay off in the long run. Businesses should encourage data teams to develop their skills in machine learning fundamentals. Even non-technical leaders benefit from understanding how AI generates insights, allowing them to make more informed strategic decisions. The companies that integrate AI literacy across their teams will be the ones maximizing efficiency, innovation, and long-term growth.
Data visualization and storytelling are critical for effective communication
AI-generated insights and complex datasets are only valuable if decision-makers can understand them. Data professionals must go beyond technical analysis and develop strong visualization and storytelling skills. Clear, well-presented data enables faster, more effective business decisions.
Visualization tools like Power BI, Tableau, and Databricks SQL help transform raw data into structured, interactive reports. These tools allow organizations to identify patterns, track key performance indicators, and communicate findings across teams. However, strong visualization alone is not enough—understanding how to present insights in a meaningful way is what drives action. Data storytelling ensures that technical reports translate into business impact.
Executives should recognize that without proper visualization and communication, even the most sophisticated AI models and analytics can be underutilized. Well-structured dashboards and reports provide a real-time snapshot of an organization’s performance, but their effectiveness depends on clarity, relevance, and accessibility. Decision-makers rely on data-driven insights to formulate strategy, and poorly communicated data leads to inefficiencies.
Businesses that develop strong data communication practices gain an advantage. Training teams in visualization best practices and storytelling techniques ensures that insights are clear, actionable, and aligned with business objectives. Companies that prioritize this skill set will see improved collaboration between technical and non-technical teams, leading to better execution of data-driven initiatives.
A balanced skill set is key to future-proofing data careers
The demand for data professionals is increasing, but technical knowledge alone is no longer enough. Future-proofing a career in data requires a combination of technical expertise, analytical thinking, and business awareness. Companies need professionals who understand data and know how to apply it effectively within an organization.
SQL remains fundamental, as structured data is still a critical asset for businesses. Mastering data governance and compliance is equally important, especially as regulations become stricter. AI and machine learning literacy are now essential skills, even at a basic level. Understanding how ML models function allows professionals to better integrate AI-driven insights into strategic decision-making. At the same time, clear data visualization and strong communication ensure that technical findings translate into real business value.
Executives should focus on developing these capabilities within their teams. Organizations that invest in continuous skill development will have a workforce that can adapt to evolving technologies and regulatory requirements. Data professionals who refine their expertise in security, governance, AI, and data storytelling will be best positioned for leadership roles in an increasingly AI-driven business landscape.
The companies that recognize the need for a well-rounded approach to data will be the ones leading their industries. Those who invest in a workforce that balances deep technical skills with strategic thinking and communication will maintain a competitive edge in the years ahead.
Key takeaways for leaders
- Reliable data is critical for AI success: AI initiatives are only as effective as the data they rely on. Leaders should invest in modern data strategies, ensuring structured, high-quality, and well-governed datasets to optimize AI performance and scalability.
- SQL remains an essential skill for data professionals: SQL continues to be a foundational skill across data roles. Organizations should ensure their teams are proficient in SQL for efficient data querying, performance optimization, and cloud-based database management.
- Stricter data governance and privacy regulations are reshaping responsibilities: Compliance requirements are evolving, making data governance a business priority. Leaders must integrate security, encryption, and regulatory adherence into their data strategies to avoid legal risks and maintain customer trust.
- Foundational machine learning literacy is becoming essential: AI-driven decision-making is expanding across industries. Companies should develop ML literacy within their workforce to improve collaboration between analysts and data scientists, ensuring AI tools are leveraged effectively.
- Data visualization and storytelling are critical for effective communication: Data insights are only valuable if they can be understood and acted upon. Executives should prioritize training in visualization tools and storytelling techniques to improve clarity in decision-making.
- A balanced skill set is key to future-proofing data careers: A combination of technical expertise, regulatory knowledge, and strategic thinking is necessary for long-term success. Organizations that invest in well-rounded data teams will maintain a competitive edge as AI and analytics evolve.
