A New Era for Data: What’s Possible with Service as a Service

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But the right amount of data, clean and properly channeled, can quench a company’s thirst for information, fuel its growth and drive it 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’s useful for the purpose at hand. “The hard part is aligning the data properly, inclusively, in a common format,” Baker says. “It needs to be purified and organized in some way to make it usable, secure, and reliable to create good results.”

Many organizations are overwhelmed with data, according to a recently commissioned study of more than 4,000 decision-makers and 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 that they generate. – sometimes doubling or even tripling – and 75% say the demand for data within their organization has also increased.

The Research Society IDC believes that the world has generated 64.2 zettabytes data in 2020, and this number is increasing by 23% per year. One zettabyte equals one trillion gigabytes. To put this into perspective, it is enough storage for 60 billion video games or 7.5 trillion MP3 songs.

Forrester research showed that 70% of business leaders accumulate data faster than they can analyze and use it effectively. Although leaders have massive amounts of data, they lack the means to extract insight or value from it – what Baker calls the “former sailor” paradox, after the famous phrase from Samuel Taylor Coleridge’s epic poem, “Water, water everywhere and not a drop to drink.

Data streams turn into data streams

It’s easy to see why the amount and complexity of data is growing so rapidly. Every app, gadget, and digital transaction generates a stream of data, and those streams come together to generate even more streams of data. Baker offers a potential future scenario in physical 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 they are and leverages information the retailer already has about customer demographics and past shopping behavior to predict what they might buy . When she walks past a particular aisle, the app generates a special offer on ink cartridges for the customer’s printer or an upgraded controller for her game box. It notes which offers generate sales, remembers them for next time and adds all the interaction to the retailer’s growing stack of sales and promotion data, which can then attract other shoppers with smart targeting.

Added to the complexity is a mass of legacy data that is often unwieldy. Most organizations don’t have the luxury of building data systems from scratch. They may have years of accumulated data that needs to be cleaned to be “potable,” says Baker. Even something as simple as a customer’s date of birth could be stored in half a dozen different, incompatible formats. Multiply this “contamination” by hundreds of data fields and getting clean, useful data suddenly seems impossible.

But giving up old data means giving up potentially invaluable information, Baker says. For example, historical data on warehouse stocking levels and customer ordering patterns could be essential for a company trying to create a more efficient supply chain. Advanced extraction, transformation, and loading capabilities, designed to tidy up disparate data sources and make them compatible, are essential tools.

Download the full report.

For more, listen to Jenn Savadra and Jen Felch discuss how companies are balancing the new trend of working from anywhere in a post-pandemic world on the Business Lab Podcast. Also hear from John Roese on increasing innovation through operations on the Business Lab Podcast.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not authored by the editorial staff of MIT Technology Review.

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