A number of analogies have arisen in recent years to describe the importance of data and its role in shaping new business models and business strategies. Among these is the concept of the “data factory”, recently highlighted by Abhi Mehta of Bank of America to describe businesses that have realized that their greatest asset is data.
WalMart, Google and Facebook are good examples of data factories, according to Mehta, who is working to ensure that BofA joins the list as the first data factory for financial services.
Mehta lists three key concepts that are central to building a data factory:
- Believe that your core asset is data
- Be able to automate the data pipeline
- Know how to monetize your data assets
The idea of the data factory is useful in describing the organizations that we see driving the adoption of new data management, management and analytics concepts (Mehta has also referred to this as the “birth of the next industrial revolution”) but it has some less useful connotations.
In particular, the focus on data as something that is produced or manufactured encourages the obsession with data volume and capacity that has put the Big in Big Data.
Size isn’t everything, and the ability to store vast amounts of data is only really impressive if you also have the ability to process and analyze that data and gain valuable business insight from it.
While the focus in 2010 has been on Big Data, we expect the focus to shift in 2011 towards big data analytics. While the data factory concept describes what these organizations are, it does not describe what it is that they do to gain analytical insight from their data.
Another analogy that has been kicking around for a few years is the idea of data as the new oil. There are a number of parallels that can be drawn between oil and gas companies exploring the landscape in search of pockets of crude, and businesses exploring their data landscape in search of pockets of useable data.
A good example of this is eBay’s Singularity platform for deep contextual analysis, one use of which was to combined transactional data from the company’s data warehouse with behavioural data on its buyers and sellers, and enabled identification of top sellers, driving increased revenue from those sellers.
By exploring information from multiple sources in a single platform the company was able to gain a better perspective over its data than would be possible using data sampling techniques, revealing a pocket of data that could be used to improve business performance.
However, exploring data within the organization is only scratching the surface of what eBay has achieved. The real secret to eBay’s success has been in harnessing that retail data in the first place.
This is a concept I have begun to explore recently in the context of turning data into products. It occurs to me that the companies that represent the most success in this regard are those that are not producing data, but harnessing naturally occurring information streams to capture the raw data that can be turned into usable data via analytics.
There is perhaps no greater example of this than Facebook, now home to over 500 million people using it to communicate, share information and photos, and join groups. While Facebook is often cited as an example of new data production,, that description is inaccurate.
Consider what these 500 million people did before Facebook. The answer, of course, is that they communicated, shared information and photos, and joined groups. The real genius of Facebook is that it harnesses a naturally occurring information stream and accelerates it.
Natural sources of data are everywhere, from the retail data that has been harnessed by the likes of eBay and Amazon, to the Internet search data that has been harnessed by Google, but also the data being produced by the millions of sensors in manufacturing facilities, data centres and office buildings around the world.
Harnessing that data is the first problem to solve, applying the data analytics techniques to that, automating the data pipeline, and knowing how to monetize the data assets completes the picture.
Mike Loukides of O’Reilly recently noted: “the future belongs to companies and people that turn data into products.” The companies and people that stand to gain the most are not those who focus on data as something to be produced and stockpiled, but as a natural energy source to be processed and analyzed.