From Data to Context: The Evolution of Software
The “data” of the Data Economy
The Data Economy was built on a simple economic insight: data had value.
As software became embedded into more parts of everyday life, an increasing share of users’ interactions with the world became digital. Whenever an activity became mediated by software, that activity began producing data. But what exactly was this data?
It was much more than the personal information usually associated with the term — name, age, address, phone number, or email address. Software was progressively creating digital representations of the worlds in which users lived and acted.
Consider what accumulated across applications.
1. Data About Who a Person Is
The most obvious category was information describing the user as an entity.
Names, ages, genders, addresses, education, employment, qualifications, account information, identifiers, memberships, and other attributes became digitally represented across different systems.
This was identity data — data describing who a person is.
2. Data About What a Person Has
Software accumulated information about the resources, objects, and assets associated with an individual.
Bank accounts, money, investments, insurance policies, vehicles, properties, subscriptions, devices, documents, digital assets, and purchases became represented within software systems.
This was state and ownership data — information about what exists in a person’s world, what is associated with them, and the state of those things.
3. Data About What a Person Does
As more activities moved through software, applications began recording actions.
Users searched, purchased, paid, travelled, worked, watched, listened, read, exercised, booked, cancelled, created, and edited.
Each interaction generated another observation.
This was activity data — a record of what a person does.
4. Data About Whom a Person Interacts With
Communication and social software digitised another fundamental part of human life: relationships.
Contacts, friends, followers, colleagues, customers, employers, family members, communities, and organisations became represented as networks of connections.
Emails, messages, calls, meetings, comments, and transactions recorded interactions between those entities.
This was relationship and interaction data — information about who is connected to whom and how those entities interact.
5. Data About Where and When Things Happen
Digital systems increasingly attached activities and events to space and time.
Calendars captured when things were expected to happen. Maps and mobile devices captured where activities occurred. Transactions carried timestamps. Photographs contained dates and locations. Travel applications recorded journeys.
This was spatial and temporal data — information about where and when events in a person’s world unfolded.
6. Data About What People Communicate and Create
A significant part of the digital world consisted not merely of structured records, but of digital artefacts themselves.
Emails. Messages. Documents. Photographs. Videos. Presentations. Spreadsheets. Notes. Contracts. Invoices. Source code. Voice recordings.
These artefacts captured conversations, ideas, decisions, work, knowledge, and experiences.
This was content data — digital representations of what people communicate, consume, and create.
7. Data About What Captures Attention
Software also learned from interactions that users did not necessarily perceive as explicit acts of data creation.
Searches, clicks, viewing duration, skipped content, repeated visits, purchases, abandoned purchases, likes, and shares all generated signals.
These behavioural traces became indicators of interests, preferences, relevance, and intent.
This was attention and behavioural data — information generated simply through a person’s interaction with a digital environment.
The Digitisation of the User’s World
Taken together, these categories reveal something more fundamental about the Data Economy.
Software did not merely create databases about users. It progressively created digital representations of the worlds in which users operated.
Those worlds consisted of entities — people, organisations, places, objects, accounts, contracts, products, and devices — and the states, relationships, activities, interactions, and events connecting them across space and time.
Different applications represented different parts of this world.
A bank represented customers, accounts, balances, and transactions. A calendar represented people, events, schedules, and commitments. An email system represented participants, conversations, and messages. A commerce platform represented customers, products, orders, and payments. A map represented places, routes, and movement.
A purchase, message, appointment, journey, payment, or document was therefore not merely a piece of data. Each was a digital representation of something existing, changing, relating, or happening in the user’s world.
Much of this data emerged naturally as a by-product of interaction.
As users interacted with people, organisations, objects, places, and systems through software, those interactions left digital traces that could be recorded, stored, connected, and analysed.
The Data Economy was therefore built on something larger than the accumulation of data about users:
It progressively digitised the world in which users lived and acted — and the interactions occurring within it.
The Role of Software
If data represented the user’s world, what role did software play within it?
A person, organisation, place, bank account, product, vehicle, or contract could exist independently of the application representing it. Data described those entities — their attributes, states, relationships, activities, and events.
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But representation alone was not enough. Users also needed a way to interact with those representations.
A banking application represented accounts, balances, customers, and transactions — but also allowed the user to transfer money.
A calendar represented people, events, and schedules — but also allowed the user to create a meeting.
An email system represented participants, conversations, and messages — but also allowed the user to communicate.
A commerce platform represented customers, products, orders, and payments — but also allowed the user to make a purchase.
Software therefore performed two closely related functions:
It represented a part of the user’s world and provided an interface through which the user could interact with it.
Through software, users could access entities, establish relationships, initiate actions, change states, and observe the consequences.
This reveals an important distinction: the interaction was rarely just between the user and the software. Software mediated interactions between the user and entities in the user’s world.
A person communicated with another person through email. A customer purchased a product from a business through a commerce platform. A user transferred money between accounts through a banking application. A traveller navigated between places through a mapping application.
And every such interaction produced more data.
A conversation became messages. A purchase became an order and transaction. A meeting became a calendar event. A payment became a financial record. A journey became a sequence of locations and timestamps. A feedback loop emerged:
World → Data → Software → Interaction → Change → More Data
The world was represented as data. Software made that representation interactive. Users acted through software. Those actions changed the world or its state. Those changes generated new data.
As more of the user’s world became digitally represented, more interactions with that world could take place through software.
Software therefore sat at the centre of three things: the user’s intent → a digital representation of the world → interaction with that world
This was the deeper role of software in the Data Economy. Its value was not simply that it stored data or displayed information. It became the interface through which users could perceive parts of their world, communicate intent, act upon entities within it, and observe the resulting changes.
Software became the interaction layer between the user and the digitally represented world. And by mediating those interactions, software continuously generated the resource on which the Data Economy was built: data.
Software made the digital representation of the world interactive.
The Missing Context
But this model had a fundamental limitation.
Software progressively digitised the user’s world, but that representation was fragmented across applications.
A calendar knew about meetings and schedules. Email knew about conversations. A bank knew about accounts and transactions. A commerce platform knew about purchases. Maps knew about places and journeys.
Each application represented a part of the user’s world. But the user did not experience these as independent worlds.
A meeting in the calendar might have originated from an email, relate to a document, require a journey, involve an expense, follow an earlier conversation, and exist in pursuit of a larger objective.
To the user, these were parts of one situation. To software, they remained largely separate pieces of data.
The problem was therefore no longer simply the absence of data. Much of the data already existed, missing piece was an understanding of what surrounded that data, how those surrounding elements were related, and which of them mattered to the situation at hand.
Consider a calendar entry:
Meeting with Rahul — Tuesday, 10:00 AM, Mumbai.
The application could represent the event perfectly. But the event alone revealed very little about the situation.
Who is Rahul? What is his relationship with the user? Why is the meeting happening? What conversation led to it? What happened previously? Which documents are relevant? What is the user trying to accomplish? What commitments have already been made? What constraints might affect the meeting?
The answers might exist across email, documents, contacts, calendars, messages, travel applications — or simply in the user’s memory. Each answer introduces another relevant part of the user’s world: a person, relationship, conversation, document, event, objective, commitment, constraint, place, or prior action.
Individually, these are pieces of data. Together, they form an understandable situation. The awareness that connects them is context. Data represents parts of the user’s world. Context identifies the parts of that world that are relevant to understanding a particular situation. This distinction marks the transition from the Data Economy to the Context Economy.
The Data Economy solved an increasingly large part of the problem of digitally representing the world. The next problem is determining, from that enormous representation, what matters now.
For any given situation:
Which entities matter? Which relationships matter? Which past interactions matter? Which documents matter? Which events matter? Which constraints matter? Which objectives matter?
The challenge is no longer merely to retrieve more data. It is to assemble the relevant world around a situation.
Context is the relevant awareness of the user’s digitally represented world required to understand a situation and act within it.
Conclusion
The Context Economy changes the role of software.
If software in the Data Economy made digital representations of the world interactive, software in the Context Economy must make those representations contextual.
Software applications in the Data Economy were primarily organised around domains and functions.
Banking applications managed money. Calendars managed schedules. Email managed communication. Maps managed locations. Document systems managed information. Each application represented and enabled interaction with a particular part of the user’s world.
But situations in the user’s world rarely conform to these application boundaries.
A single situation may involve people from contacts, conversations from email, meetings from calendars, information from documents, places from maps, and transactions from financial systems. The user experienced these as parts of one situation, while software represented them across separate applications.
Software in the Context Economy must therefore move from being primarily application-centric toward situation-centric.
Its role is no longer merely to expose data and functionality within a particular domain. It must increasingly assemble the relevant parts of the user’s digitally represented world around the situation at hand.
For any given situation, this means identifying the relevant entities, understanding their relationships, recovering relevant history, determining their current states, recognising the user’s objectives and constraints, and continuously determining what matters as the situation evolves.
The fundamental problem for software in the Context Economy can therefore be expressed as:
Given a situation and the user’s intent, determine which parts of the digitally represented world matter, establish how they relate, and maintain that understanding as the situation evolves.
The software does not need all available data. It needs the relevant world around the situation.
The transition is therefore not merely: Data → Context
It is also a transition in how software itself is organised: Domains and functions → Situations
That relevant, situation-specific awareness of the user’s world is context — and constructing and maintaining it becomes the defining role of software in the Context Economy.
