Big data vs the right data: Becoming more productive in the cloud
Molly Sandbo, director of product marketing at Matillion, busts a common myth on the value of data and discusses how businesses can adapt their analytics program as data grows.
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We’re all familiar with the age-old debate of quality versus quantity. But have you ever considered the importance of quantity versus agility?
In the world of data, it’s often thought that success depends on how much of it you have in your business. Indeed, data is the lifeblood of modern organizations, with the information it holds helping companies to move faster, stay in tune with its customers and make a bigger impact. While this remains true, we can’t ignore that cloud data is growing exponentially in volume, creating internal obstacles in businesses that can stall productivity and innovation.
The fact is, data behaves differently in the cloud, and as it sprawls, its accessibility and integrity become more fragile. When businesses are challenged to navigate unprecedented events, like pandemics and supply chain disruption, data teams quickly become overburdened and struggle to make data useful. Many are forced to dedicate hours to circumventing outdated migration and maintenance processes, costing them time, productivity and money.
All of this has a material impact across the business and erodes the ability to be data-driven, including slower time to value, outdated information, and a tendency for end users to seek their own data and perform siloed analysis. More often than not, this leads to inaccurate data or unstandardized processes that can create inefficiencies in the business. It’s impossible to be productive with data if business users are spending their time doing manual coding rather than the strategic analysis that drives a company forward.
Organizations must make the move from manual methods and technologies and adopt fresh approaches to data integration and transformation. Otherwise, they run the risk of using big data instead of the right data across the business. This article will explore exactly what we mean by data productivity and how businesses can adapt their analytics program to manage the influx of cloud data being generated.
Misunderstanding and misuse of cloud data often comes down to how it is being stored. Data engineers have been grappling with legacy data integration technology, which cannot scale with the demand for data.
