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A year ago I took on the subject of how deeply embedded Big Data is becoming in our everyday lives in a piece with the modest title, Without Big Data Life As We Know It Would Be Impossible. I outlined a simple scenario in which we book an international flight, check the weather conditions in the intended destination city, secure accommodations, and make sure we have working wireless service upon arrival. Interestingly, but perhaps not surprisingly, Big Data plays a major role in completing each of these fairly routine tasks. 


Looking ahead, we can expect this trend to continue. In the future, we will find Big Data even more deeply embedded in even more routine activities. Not only will we be accessing Big Data more frequently, we will be significantly growing our own contribution to it.

 

There is an interesting analogy between the impact that we, as individuals, have on the overall data environment and the impact that we have on the real environment. When assessing how an individual’s behavior might impact climate change, we talk in terms of the carbon footprint: the total volume of carbon emissions that result from our personal lifestyle choices. Likewise, we each have a data footprint: the total volume of data generated by and on behalf of us.


The two footprints are similar in that both have experienced enormous growth in the recent past. But there is an important difference between them. Whereas there is growing societal awareness of the carbon footprint and a dedicated effort to get its growth under control, there is little awareness of the data footprint, and there is certainly no widespread effort to curb its growth.


And that may be a very good thing. Oddly enough, the exponential growth of our data footprint may be able to contribute to the reduction of our carbon footprint.


How is that possible? Consider air travel, one of the primary targets of efforts to reduce the individual carbon footprint. If you want to mitigate the damage you do caused by air travel, the most obvious solution is to cut down on the number of trips you take. But there are other ideas, too. Recognizing the environmental impact of air travel, aviation manufacturers are taking steps to reduce the overall carbon footprint of air travel.


For example, General Electric (GE) has recently announced substantial changes to the design of the CFM Leap aircraft engine, which powers the Airbus A320neo, Boeing 737 Max and COMAC C919 aircraft.  The new generation Leap is “designed to provide significant reductions in fuel burn, noise, and NOx emissions compared to the current… engine.” It is designed to generate 32K pounds of thrust, achieve a 99.87% reliability rate, and introduce a $3 million operating saving annually.

 

Where will these savings come from? New sensors intricately track how the engine is operating.  The use of data fundamentally transforms how the engine operates and makes it more efficient. But that efficiency requires a lot of data. The new version of the Leap aircraft engine generates 1 TB per day from those sensors alone. Add in avionics, traffic data, weather data… a massive amount of information is generated just from taking a flight. In previous versions, the Leap engine has completed more than 18 million commercial hours of operation, with some 22,000 of the engines manufactured. So we’re talking about a lot of data.


In every way but one, this engine now operates with a smaller footprint: it requires less fuel, it makes less noise, it generates fewer noxious emissions, it costs less to operate. Only in one area, data, is its footprint expanding.


And this is why we can each expect our own data footprint to grow enormously in the years to come. We aren’t creating all this data just for the sake of creating data. Big Data is bringing us big benefits, and this is occurring throughout the business world and in our personal lives.

 

Consider the improvements that big data is bringing about in a wide variety of business settings.  Retailers are benefiting from rapid sales analysis and response as well as a customer-driven approach to supply chain. Discrete manufacturers are enabling real-time operations and global product traceability both upstream and downstream.  Makers of consumer products are tracking customer buying behavior in real time, and radically changing their procurement and manufacturing processes. And, like the aviation manufacturers mentioned earlier, oil and gas companies are improving asset efficiency and integrity.

 

Closer to home, we are all accessing, modifying, and generating data all the time. Taking a drive across town, we access vast geospatial databases and generate new data in our vehicles’ (more modest) versions of the sensors in the Leap aircraft engine. Filling a prescription, we tap into an interconnected set of pharmaceutical and insurance databases. Our casual tweets and status updates feed into the global social media colossus. A trip to the supermarket might involve a quick smartphone search for product information, accessing enormous search engine and consumer databases and giving us each the opportunity to generate even more data. After all, it is our shopping behavior that provides the data points feeding into the retailer and consumer goods analyses mentioned above.


At every turn, we are accessing data, modifying and updating data, and generating new data. Between ultrasounds, social media mentions, YouTube videos, and online gift transactions, a newborn in 2014 has a substantially bigger data footprint than an adult did a couple of decades ago. As that child’s world becomes ever more dependent on and integrated with Big Data, that footprint is going to grow and grow.


How far will this go? That is difficult to predict. But as long as Big Data continues to be tied with providing new options, improving existing processes, and opening up new capabilities -- and as long as our infrastructures can continue grow to support this data -- there is no end in sight.


Blog article also posted on SAPHANA.com website.


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