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But Matt Baker, senior vice president of corporate strategy at Dell Technologies, said the right amount of data, cleanly and correctly delivered, can satisfy a business’s desire for insight to drive growth and success. Just like water, data is neither good nor bad. The question is whether it is useful for the purpose at hand. “The difficulty is getting the data aligned correctly in an inclusive way, in a common format,” Baker said. “It has to be purified and organized in a way that makes it usable, safe and reliable when creating good results.”
According to a recent Dell Technologies study of more than 4,000 decision makers commissioned by Forrester Consulting, many organizations are overwhelmed. 1 Over the past three years, 66% of organizations have seen an increase in the amount of data they generate—sometimes doubling or even tripling—and 75% say the need for data within their organization has also increased.
research company International Data Center estimated world production 64.2 zettabytes 2020 data, and this number is growing at a rate of 23% per year.A zettabyte is a trillion gigabytes – to put it another way, that’s enough storage space 60 billion video games or 7.5 trillion MP3 songs.
Forrester research shows that 70% of business leaders are amassing data faster than they can effectively analyze and use it. While executives have vast amounts of data, they have no way of extracting insights or value from it — what Baker calls the “ancient sailor” paradox, from a famous quote from Samuel Taylor Coleridge’s epic: “Water, There is water everywhere, not a drop.”
Data flow turns to data flood
It’s easy to see why the volume and complexity of data is growing so rapidly. Every app, gadget, and digital transaction generates a stream of data that flows together to generate more streams of data. Baker offers a potential future scenario for brick-and-mortar retail. A loyalty app on a customer’s phone tracks her visits to an electronics store. The app uses a camera or a Bluetooth proximity sensor to know its location and uses the information the retailer already has about a customer’s demographics and past buying behavior to predict what she’s likely to buy. As she walks through a particular aisle, the app generates special deals on cartridges for the customer’s printer or controller upgraded for her game box. It records which offers lead to sales, remembers next time, and adds the entire interaction to the retailer’s growing sales and promotion data, which may then be intelligently targeted to lure other shoppers.
Adding to the complexity is the large amount of legacy data that is often intractable. Most organizations don’t have the luxury of building data systems from scratch. They may have years of accumulated data that would have to be cleaned to be “drinkable,” Baker said. Even something as simple as a customer’s date of birth can be stored in six different and incompatible formats. Multiply this “pollution” by hundreds of data fields, and suddenly it seems impossible to get clean, useful data.
But letting go of old data means giving up potentially valuable insights, Baker said. For example, historical data on warehouse inventory levels and customer ordering patterns can be critical for companies trying to create more efficient supply chains. Advanced extract, transform, load capabilities – designed to organize disparate data sources and make them compatible – are essential tools.
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This content is produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.
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