The Four Eras of Data

Over the last 30 years, I have watched organisations' relationships with data transform continuously. From the days of ERP and operational reporting, through the rise of data warehousing and BI, to today's cloud-native data platforms and AI, the pace of change has been constant. I think about this as four distinct eras, each with its own business objectives, its own technology drivers, and its own definition of what data is actually for.

First, is what I call the Business Process Automation Era (1990s to the early 2000s) which saw organisations investing in ERP, CRM, and other systems to automate business processes. The goal was efficiency and control, not data and insight. Data was considered a by-product of digitised processes and was mainly used for backward-looking operational reporting. The technology drivers were SAP, Oracle, Siebel, and Excel-based reporting (maybe even Lotus 1-2-3. I'm definitely showing my age!)

Next came the Data Management and Decision Support Era (Mid 2000s to 2015). Organisations now fully recognised that data could support better decision-making. We experienced the rise of data governance, data warehouses, and BI to bring structure and access to data. Data became an asset to be stored, managed, and analysed, mostly for tactical and departmental decisions. The technology drivers were data warehouses, ETL tools, reporting applications, and dashboards.

The third, and current era is the Data Value Era (2015 to the recent present). There has been an explosion of data sources, cloud platforms, AI/ML, and stakeholder expectations. Boards now ask "How is our data driving growth, innovation, and resilience?". Data is now recognised as an investment for driving competitive advantage, monetisation, customer experience, and business agility. We have seen the emergence of cloud platforms like Microsoft Azure, Amazon AWS, Snowflake, and Databricks. We now regularly talk about concepts such as data products, data ops, and data mesh.

We are now on the cusp of moving to a fourth era, what I would call the Trusted Intelligence Era (from the present day onwards). We will be experiencing autonomous and adaptive data systems with organisations focused on investing in building trusted data ecosystems and establishing human-centred AI capabilities. The technology drivers are large language models, agentic AI systems, AI governance frameworks, and real-time data analytics. But the challenge of this era is less about technology and more about trust and business value. Not simply whether we should do this, but why are we doing it. What value does it deliver? Also, how do we ensure that stakeholders such as employees, customers, and regulators trust the systems making decisions?

Looking back across these four eras, my experience has been consistent. The technology has never been the hardest part (although it definitely was more difficult before the internet!). What has always lagged is the human side, the governance, the culture, and the trust. In the era we are now entering, getting that right is key. The organisations that will succeed are those that invest as seriously in their people as they do in their technology.

Which era do you think your organisation is in?