Data has evolved from a supporting input to a core driver of business value. But as organizations face growing pressure to modernize infrastructure, improve governance, and adapt to AI-driven change, turning data into a true advantage is more complex than ever.
The trends shaping data and analytics today aren’t standalone. They reflect a progression. From laying the groundwork to enabling real-time insight, monetizing assets, and shaping open-source ecosystems, each trend represents a new phase in the data maturity journey.
This blog explores four interconnected shifts and what they mean for organizations looking to build smarter, more future-ready data strategies.
Related Content: AI Governance: How an AI Council Could Make or Break Your Strategy
# Trend 1: Prioritizing Data Strategy, Management, and Governance
As organizations focus on AI and data-driven decision-making, their data environments become harder to manage. AI systems consume and generate massive volumes of information, creating new layers of complexity across data management, governance, and classification requirements. Rising privacy concerns and shifting regulations are pushing leaders to proactively strengthen how they govern, secure, and transparently manage data across the enterprise.
These rapidly shifting standards and compliance expectations are raising the stakes for data accountability. Organizations are now expected to demonstrate clear accountability for data, especially regarding societal and environmental impacts.
Reflecting this urgency, Fortune Business Insights projects that global investment in data governance will grow from $5.38 billion in 2025 to $18.07 billion by 2032.
# Establishing Data Foundations for AI Success
At the 2024 Gartner Enterprise Data Summit, analysts reinforced this shift, stating that “data governance is no longer a ‘nice-to-have’ but a critical business imperative.” Gartner predicts that roughly 80% of organizations’ digital scaling initiatives in 2025 will fail without a modern, enterprise-wide approach to data governance.
Trustworthy AI depends entirely on trustworthy data, making it essential to embed robust, clearly defined governance frameworks into everyday business operations. Given the “black box” nature of many AI models — where step-by-step logic leading to generated outputs isn’t transparent — clear processes and dedicated personnel to manage, govern, and accurately label data have become non-negotiable foundations of an effective data strategy.
With governance as the foundation, the next challenge becomes building the infrastructure needed to act on your data in real time and at scale.
Related Content: Is “Good” Good Enough? 3 Ways to Make Data Analysis More Efficient
# Trend 2: The New Era of Data Infrastructure and Analytics
As data maturity escalates, infrastructure becomes the next major lever. The rise of AI and real-time analytics is accelerating a shift from traditional, centralized systems toward cloud platforms, edge computing, and more agile architectures that store, process, and deliver data closer to the point of action. These modern systems support massive data volumes, enable faster decision-making, and scale more easily, powering the speed and precision organizations now need.
# From Data Management to Data Empowerment
Leading tech companies are advancing this evolution with modern architectures like data mesh and data fabric, which significantly improve how data is integrated, governed, and accessed across large, distributed environments.
In parallel, AI-powered analytics tools are putting insights directly into the hands of non-technical users, reducing reliance on centralized analytics teams and broadening access to data across the organization.
# Scaling Infrastructure for Speed and Precision
Edge computing brings speed and precision to the forefront, allowing data to be processed and analyzed where it’s collected. In industries like manufacturing, healthcare, and telecommunications, where latency negatively affects outcomes and competitive positioning, this capability is becoming a critical advantage.
Yet this rapid transformation comes with significant infrastructural implications and new demands. To keep up, many are investing in physical infrastructure like:
- Fiber optics and next-gen internet capacity
- Cell towers and edge infrastructure
- Satellite networks
- Sustainable energy solutions to power growing data center demand.
As data volume and complexity grow, organizations must ensure their infrastructure is agile, scalable, resilient, and capable of delivering insights at the pace of business.
With those foundations in place and access expanding, the question then becomes: how can data create not just internal value but revenue-generating opportunities?
Related Content: Change Analytics: Using Data to Guide Change Management Strategy
# Trend 3: Unlocking Revenue Streams Through Data Monetization
With modern infrastructure and accessibility in place, organizations are shifting their focus to the next opportunity: turning data into a source of revenue and strategic differentiation.
High-quality data is no longer just a strategic asset — it’s quickly becoming a powerful revenue driver. The global data monetization market is still in its early growth phase, valued at just $3.75 billion in 2024.
The data monetization market is projected to reach $28.16B by 2033
As momentum builds, more organizations are actively exploring new ways to capitalize on their data assets and services. Rather than relying solely on traditional data sales, organizations are increasingly monetizing related services, including:
- Data labeling
- Annotation services
- Synthetic datasets tailored for AI/ML model training
# Capitalizing on Data as a Strategic Business Asset
Fueled by the explosive growth of AI and ML technologies, we saw both the public and private sectors embrace new monetization opportunities in 2024 as they explored innovative partnerships to unlock value from the data sets they collect, generate, or enhance. Reddit’s high-profile partnership with Google, granting the tech giant extensive access to its data API for AI training, is just one high-profile example. These types of deals underscore the growing importance of high-quality, diverse data sets in training the next generation of AI systems.
# Expanding Monetization Through Synthetic Data and Specialized Services
The rise of AI has intensified demand for quality training data. At the same time, the risks, costs, and restrictions around sensitive data have become more complex.
In response, synthetic data is gaining traction as a scalable, privacy-conscious alternative. It enables AI development without compromising compliance or introducing unnecessary risk. Many tech leaders now depend on synthetic data to continue training and refining their models, especially amid concerns about a looming shortage of usable “real” training data.
Beyond synthetic data, data-related services have also emerged as profit-driving opportunities. Uber’s launch of Scaled Solutions, a B2B data labeling service powered by its gig workforce, illustrates how companies can productize internal capabilities. Snowflake also remains a leader in the data-as-a-service landscape, facilitating a data marketplace that allows customers to source and purchase valuable datasets to enrich their own data.
# Balancing Innovation with Governance
Still, data monetization comes with caveats. Organizations must navigate tightening regulations, especially around personal and sensitive data, with proactive governance strategies and greater diligence. Whether they’re generating synthetic data or acquiring datasets from external sources, they must go beyond evaluating relevance alone. To ensure reliable outcomes, they need to assess how the data was sourced, how it’s been handled, and whether it can be trusted.
For organizations already optimizing infrastructure and governance, the next frontier is moving beyond commercial value toward open collaboration and ecosystem impact.
Related Content: Building Your Enterprise AI Chatbot Compass
# Trend 4: Contributing to the Community via Open-Source Data Sharing
As technology ecosystems expand, some of the most forward-looking organizations are taking a different approach to growth: sharing data. Open-source data sharing, once the domain of academia and public institutions, is now a strategic lever for accelerating innovation, building trust, and shaping industry standards. The rise of specialized data search engines and open-access platforms, like Google’s Dataset Search, is making it easier for teams across private and public sectors to discover, use, and contribute to open-source datasets.
# Expanding the Open-Source Ecosystem
The technology industry simultaneously contributes to and benefits from open-source data distribution. While monetized data sharing is growing rapidly, non-monetized, collaborative sharing has also surged in recent years. Tech companies and research institutions increasingly share a variety of data formats, from structured and unstructured datasets to analytical frameworks, insights reports, and visualization dashboards. This trend has been driven by rising demand within AI and ML communities for openly accessible data, transparency-focused regulations, and renewed momentum for public-private collaboration following increased data sharing during the COVID-19 pandemic.
The expansion of open-source data has also led to the greater popularity of specialized data search engines and platforms that make it easier to find and use this information.
Examples include:
- Google's Dataset Search — A powerful discovery engine for publicly available datasets across a wide range of providers.
- Google Colab — Offers access to Gemini AI nodes, enabling the training and experimentation of machine learning models in flexible, cloud-based environments.
- Data.gov — A robust U.S. government initiative providing extensive open datasets for both commercial and civic use.
- Salesforce’s Tableau Public — A leading platform for publishing and exploring interactive data visualizations built on open datasets.
- Kaggle — A collaborative online community where data scientists, researchers, and AI/ML practitioners share datasets, models, and experiments openly.
# Building Trust and Navigating Open-Source Opportunities
Openly sharing data can significantly enhance trust and credibility, especially as collaboration becomes increasingly vital to technological and societal progress. While open data access promotes innovation, collaboration, and transparency, organizations must carefully navigate the risks of sharing sensitive or proprietary data.
Unlike synthetic or marketplace-purchased datasets—which can raise questions around authenticity or quality— well-executed open-source contributions offer a more transparent and authentic alternative. Done right, they don’t just fuel innovation—they demonstrate a commitment to collective progress.
# Navigating the Future of Data & Analytics
Across industries, the data landscape is evolving fast, and so is the opportunity. The organizations that lead are those that invest not just in the tools but also in the strategy, governance, and teams to unlock long-term value from data.
At Propeller, we help clients move from siloed experimentation to enterprise-scale execution. Our cross-functional teams work alongside you to:
- Build and operationalize modern data strategies
- Strengthen data governance and integrity
- Activate AI/ML use cases with confidence
- Monetize and democratize data responsibly
- Empower teams through thoughtful change and enablement
Whether you're laying a data foundation, navigating infrastructure shifts, exploring monetization opportunities, or contributing to the open-source community, we meet you where you are and guide you on what’s next.
# Let’s Build What’s Next —Together
From strategy through execution, we bring clarity, structure, and momentum to your most complex data and analytics initiatives.
Special thanks to Propeller Alum Jon Kellerman for his support in developing the concepts for this piece.
