The infrastructure foundation
The 2000s saw a transformation in how organizations store and process data. Open-source projects like Hadoop, Spark, and Kafka—many originating from Silicon Valley companies solving their own scaling challenges—made it possible to work with datasets that would have overwhelmed traditional databases. Initially, only large enterprises could deploy and operate this infrastructure. The expertise required was scarce and expensive. But cloud platforms changed this dynamic. Services like Amazon Web Services, Google Cloud Platform, and Microsoft Azure abstracted away the operational complexity, allowing mid-sized companies to access the same capabilities without building data centers or hiring specialized operations teams. By the early 2010s, the infrastructure barrier had largely fallen. Organizations of many sizes could store petabytes of data and run complex analytical workloads. The question shifted from “can we handle this much data?” to “what can we do with it?”The talent evolution
Powerful infrastructure created demand for people who could use it. Traditional database administrators understood relational databases and SQL, but the new paradigm required different skills: distributed systems, statistical modeling, and programming in languages like Python and R. Organizations began hiring data scientists, data engineers, and analytics professionals. These roles brought capabilities that hadn’t existed in most companies: predictive modeling, machine learning, and the ability to extract insights from unstructured data. The job title “data scientist” barely existed before 2010; by the mid-2010s, it was one of the most sought-after positions in technology. This talent could do things with data that simply weren’t possible before—identifying patterns in customer behavior, predicting equipment failures, optimizing supply chains in real time. But their effectiveness depended entirely on having access to the right data.The data acquisition gap
Infrastructure and talent, it turned out, were necessary but not sufficient. Organizations discovered that their internal data—however large—rarely contained everything they needed for sophisticated analytics. A retailer might have detailed transaction data but lack information about customer behavior outside their stores. A financial services company might have comprehensive records of their own customers but no visibility into market-wide trends. A healthcare organization might have clinical data but miss the social determinants that influence patient outcomes. The need for external data wasn’t new. What changed was the scale and complexity of what organizations wanted to do with it. Machine learning models require training data from multiple sources. Predictive analytics work better with more signals. The same infrastructure and talent investments that enabled sophisticated analytics also created demand for data that no single organization could generate internally. Acquiring external data, however, was difficult. The process typically involved:- Discovery — Finding organizations that had relevant data, often through personal networks or industry connections
- Negotiation — Working through legal and commercial terms, frequently taking months
- Technical integration — Building custom pipelines to ingest data in whatever format the provider used
- Ongoing maintenance — Managing schema changes, delivery schedules, and data quality issues