Big data vs the right data: Becoming more productive in the cloud

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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 Many are forced to dedicate hours to circumventing outdated migration and maintenance processes, costing them time, productivity and money.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has a material impact across the business and erodes the ability to be data-driven, including slower time to value, outdated information, and a tendency for end users to seek their own data and perform siloed analysis.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. In other words, old habits are preventing teams from realizing the meaningful outcomes they are looking for.

What’s more, the task of making sense of big data in its raw state is simply too great for any one of us to complete manually, especially as businesses face a digital skills shortage. The DCMS reported just under half (46%) of British businesses are struggling to recruit data professionals in the last few years, meaning there just aren’t enough experts equipped to manage the demand for data we already have, let alone the volume.

Ultimately, wrestling with data is distracting teams from effectively seeking out the pieces of insight that will drive competitive potential. The opportunity to become more productive — and making data useful so businesses can accomplish more — comes down to how businesses re-strategize.

Organizations need to provide their various teams with data in a transformed, analytics-ready state if they are to capture greater value from it. Modernizing and orchestrating data pipelines is key to increasing data productivity and helping to deliver real-time data insights for improved customer experience, fraud detection, digital transformation, AI/ML and other business critical efforts.

The ability to load, transform and synchronize the right data on a single platform means cloud environments can run more efficiently. Choosing a solution that is both “stack-ready,” and can be integrated into native cloud environments, but also “everyone-ready” empowers users from across the business to glean insights no matter their skill level.

Democratizing data at a time when businesses are facing increasing resource pressure will help alleviate the workload of overstretched data engineers, who can re-invest time in tasks that add value to the data journey. As cloud data expands to unprecedented levels, being able to quickly scale data integration efforts helps companies accelerate time-to-value and ultimately maximize the impact data can have.

For a long while, businesses have been somewhat misled by the promise of big data. Indeed, sometimes the right data is big, but organizations need more than scale to succeed in the data race.

As more and more dynamic data is generated by multiple sources and formats, it becomes more difficult to integrate. If companies continue with the legacy approach of manually migrating their data under these circumstances, it simply won’t flow fast enough. These companies need to implement a strategy for their analytics program to empower and support the needs of modern data teams. For teams to become more productive with their data, they need to start with building the right modern cloud data stack.