Data as Currency & Dealmaker

Published Originally on Apigee

The amount of data collected by companies in all sectors and markets has grown exponentially in the past decade. Businesses increasingly interact with customers through social and business networks, through apps (and APIs) and therefore businesses collecting data from new and diverse locations outside the walls of their enterprises and systems-of-record.

The following is a perspective on 5 ways in which data is changing how we do business in 2012 and beyond.

Data as currency and dealmaker

Similar to the discovery of oil in Texas at the turn of the last century, enterprises that have been collecting and storing data will be the ones primed to leverage their data for new opportunities and striking new business deals.

Add to this new data sources like the explosion of social data, which provides a window into your real-world and real-time customers’ behavior. The data accumulates quickly and changes frequently, and the ability to capture, analyze and derive insights from it will be key to offering true customer-centric value across companies, and even entire industries.

Data is fast becoming the de-facto currency for facilitating business deals. Enterprises will be able to command monetary and opportunistic conditions in return for providing access to their data. Google’s custom search for websites is an example. By providing indexed data and search functionality to websites, Google in return has the ability to show ads and generate revenue on the website.

We will also see the emergence of data network effects: enterprises will be able to enrich their existing data sets by requiring that other enterprises who purchase their data return (or feedback) the enriched data to augment the original data set. Enterprises sitting on the most desired data will be able to continuously add value to their existing data sets.

Collaborations through data

I believe a new model of collaboration based on data is emerging. Enterprises realize that they can partner with other enterprises and create innovative, new business value by sharing and operating with shared or semi-shared data stores.

Apart from shared data storage and processing costs, enterprises will be able to leverage faster time-to-market and build enhanced data-driven features using such collaboration models. They could use shared data stores as primary operating data backends thereby realizing near real-time data updates and availability for their products and services.

The academic world has several examples and parallels to this notion where data sets are frequently generated, updated and shared between various research projects leading to better quality and more expansive research and insights.

Data marketplaces

While the Internet is full of open data, there’s plenty of data that companies will be willing to pay for – particularly if it’s timely, curated, well aggregated, and insightful. As a result, data marketplaces are a burgeoning business. Witness Microsofts data platform,Thompson Reuters Content Marketplace strategyUrban Mapping and many more.

Data can be defined by attributes such as “Latency of delivery for processing”, “quality”, “sample ratio”, “dimensions”, “context”, “source trustworthiness”, and so on. As data becomes a key enabler of business, it becomes an asset that can be bid upon and acquired by the highest bidder. The price associated with acquiring data will be determined by the data attributes. For example, a real-time feed of a data source might cost more than historical data sets. Data sets at 100% sample ratio might cost more than data at lower fidelity.

Ability to access and synthesize data is a competitive edge. To gain and maintain this edge, enterprises will have to add the cost of acquiring data to their variable operating costs. At the same time, enterprises will have to protect their data as they they protect other corporate assets. Protection (and insurance) against loss, theft, corruption will be required to ensure continued success.

End users will stake claim to their data

With the rise of social networks and even with the consumerization of IT, data is also becoming more personal. We trade our personal data for services every day as we interact with Facebook and other sites.

End users who are the generators of data that enterprises collect and use to improve their businesses will stake claim to their data and demand a share of the value with the enterprise. In addition, end users will demand and gravitate towards enterprises that give them the ability to track, view and control the data they generate for enterprises. Enterprises may have to either “forget” users because users demand it or compensate them for their data.

The jury is still out but the tide may already have turned in this direction in Europe. Data protection regulations may allow for a “right to be forgotten” law through which users will have the right to demand that data held on them be deleted if there are “no legitimate grounds” for it to be kept. This includes if a user leaves a service or social network, like Google or Facebook – the company will have to permanently delete any data that it retains.

Data disintermediation

The concept of disintermediation – of removing the middlemen and vendors and giving consumers direct access to information that would otherwise require a “mediator”  has been an active topic in the information industry and gains momentum in 2012 as data becomes currency.

We will see more and more enterprises exposing their data schemas, formats and other related capabilities publicly though a common data description language and data explorer capabilities accessible by both humans and machines.

Enterprises (or their automatic agents) will be able to crawl the web (or some other data network) and discover new data sources that serve their needs. Enterprises will have the ability to walk the data models and understand the structure and schema of various data sets and understand the intricacies of using these data sources.


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