A new era for data: what’s possible with as-a-service

But the right amount of data, cleaned and channeled correctly, can quench a company’s thirst for information, fuel its growth and lead to success, says Matt Baker, senior vice president of corporate strategy at Dell Technologies. Like water, data is neither good nor bad. The question is whether it is useful for the purpose at hand. “What’s hard is getting the data to line up correctly, inclusively, in a common format,” says Baker. “It has to be purified and organized in some way to make it usable, safe and reliable in creating good results.”

Many organizations are overwhelmed by data, according to a recently commissioned study of more than 4,000 decision makers conducted on behalf of Dell Technologies by Forrester Consulting.1 Over the past three years, 66% have seen an increase in the amount of data they generate. —sometimes doubling or even tripling—and 75% say the demand for data within their organizations has also increased.

the research company IDC estimated that the world generated 64.2 zettabytes of data in 2020, and that number is growing at 23% per year. A zettabyte is a trillion gigabytes; to put it in perspective, that is enough storage for 60 billion video games or 7.5 billion MP3 songs.

Forrester’s study showed that 70% of business leaders are amassing data faster than they can analyze and use it effectively. Although executives have vast amounts of data, they don’t have the means to extract information or value from it, what Baker calls the “Ancient Mariner” paradox, after the famous line from Samuel Taylor Coleridge’s epic poem, “Water, Water everywhere and not a drop to drink.

Data flows become data floods

It’s easy to see why the amount and complexity of data is growing so fast. Every app, device, and digital transaction generates a stream of data, and those streams flow together to generate even more streams of data. Baker offers a possible future scenario in traditional retail. A loyalty app on a customer’s phone tracks their visit to an electronics store. The app uses the camera or a Bluetooth proximity sensor to understand where you are and leverages the information the retailer already has about customer demographics and past buying behavior to predict what they might buy. When he walks down a particular aisle, the app generates a special offer on ink cartridges for the customer’s printer or an upgraded driver for his game box. It takes note of which offers result in sales, remembers them for next time, and adds all interaction to the retailer’s ever-growing stack of sales and promotions data, which can then attract other shoppers with smart targeting.

Adding to the complexity is a mass of legacy data, often unwieldy. Most organizations cannot afford to build data systems from scratch. They may have years of accumulated data that needs to be cleaned to make them “drinkable,” says Baker. Even something as simple as a customer’s date of birth could be stored in half a dozen different and incompatible formats. Multiply that “pollution” by hundreds of data fields, and clean, useful data suddenly seems impossible.

But abandoning old data means abandoning potentially invaluable insights, says Baker. For example, historical data on warehouse stock levels and customer order patterns can be critical to a company trying to create a more efficient supply chain. Advanced extract, transform, and load capabilities designed to marshal disparate data sources and make them compatible are essential tools.

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This content was produced by Insights, the personalized content arm of MIT Technology Review. It was not written by the MIT Technology Review editorial team.


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