For a long while, computing power, storage, and network speed were bottlenecks to building an empire of data within the Information Technology sector—we’ve certainly always had the information, but the technology to harness it still needed its internal-combustion engine revolution. All that was almost a decade ago: now we’re not just swimming in storage and processing power, but we have the capability to network it together like never before.

Scott Yara, co-founder of Greenplum and vice president of products at EMC, has some choice words to say on the matter. In fact, he tells us that now that collecting and keeping giant warehouses of data is no longer the issue it used to be, and neither is retrieving it, we’re looking at a sector-wide change occurring within technology that will make all that data smarter.

His answer, to get smarter we need to get a lot faster,

Today, a number of technology vendors (including EMC, which formed a “data computing” products division last summer after it acquired Greenplum, the company I co-founded) are bringing to market a new wave of transformational appliances designed to analyze bigger mountains of data faster, with more penetrating insights, and at lower cost. Using industry standard servers and technologies like “parallel computing,” these “answer machines” can expedite all manner of useful tasks hundreds of times faster and more cheaply than competing technologies — from spotting business trends and unlocking new sources of economic value, to preventing diseases, providing fresh insights into science, and combating crime.

Even if now we can store giant amounts of data, we’re still staggered by the sheer volume that we could store; and as we still produce more, we had to triage what we keep and what we throw away. This is the ultimate question that data archivists have to make while they set lifecycles for information and decide what they’re going to keep for the future. As the future is unknown, the answer to that question is almost always wrong.

Yara goes on to explain how what once seemed to be “dead data”—such as lists of transactions and regions from old sales—has eventually become the turning point for certain industries to refine their approach to the market.

In fact, right now at the Strata Conference, the keynote itself included a great deal of industry leaders speaking about how they intend to use smart agents to sift through and filter huge tracts of data to enhance different sectors. Cross-Sell and Dealer Specialties have a merged project that will collect data from sales and inventory, compare it in real time across regions, and produce rapid analytics that would give subscribers to the service an edge over competitors.

With “Big Data” in play, competition will become about intelligence and market prediction as much as it is about inventory control. Five years ago, I listened to a speech about a smart program that Walmart had tested that would use current purchase statistics alongside outside triggers to guess when particular merchandise needed to move in order to fill a near-future market need. For example we have a huge snowstorm in the United States on the east coast right now—a big data predictive algorithm could be used to check weather trends for this time of year, look at previous year buying trends vs. weather, and decide it was time to shift a giant supply of snow shovels to the affected regions.

That’s only one example of a smart-system filtering data by comparing past and future trends to attempt to predict near-future events to allow slow-moving bodies like giant inventory systems become more agile. Tim O’Reilly has pointed out: data is the next revolution in economy evaluation.

Such a small thing, with a huge impact.

As Scott Yara says in conclusion, “That is where I believe ‘data computing’ is headed and, as an entrepreneur, I find it inspiring to be a part of an organization that is driving this movement. The evidence is all around us. Insofar as data is at the heart of the information revolution, ‘data computing’ is indeed the future … of everything.”

I would say that his prediction will most likely be vindicated as more of the industry leverages the cloud to collect, study, process, and build ever more complex models and intelligent solution systems.