Big data vs the right data – enhancing productivity in the cloud

Molly Sandbo, Director of Product Marketing, Matillion, discusses common myths around the value of data and the ways businesses can adapt their analytics programme as data grows

We’ve all heard about the age-old ‘quality vs quantity’ debate, but in a data-led world, perhaps it’s time to start thinking about the importance of ‘quantity vs agility’.

It’s increasingly the case that a business’ success depends on the quantity of data they have. It’s also true that data is the lifeblood of modern organizations; the information it holds helps us to move faster, stay connected to customers and make a bigger impact. However, we can’t ignore the fact that there is more and more cloud data at our disposal, which in turn creates internal obstacles for businesses to remain productive and innovative.

The fact is data behaves differently in the cloud, and as it sprawls, its accessibility and integrity become more fragile. And 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 Many are forced to dedicate hours to circumventing outdated migration and maintenance processes, costing them time, productivity and money.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 which has a material impact across the business and erodes the ability to be data-driven, including slower time to value, outdated information, and a tendency for end users to seek their own data and perform siloed analysis. Which, more often than not, leads to inaccurate data or unstandardised 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. Businesses must move away from manual methods and technology and introduce new approaches to data integration and transformation. If not, businesses run the risk of using big data instead of the right data across the company.

This article will explain what ‘data productivity’ is and how companies can apply it to their analytics programme, in order to better manage the influx of cloud data being generated.

Data expectations and data productivity: the gap

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 realising 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 per cent) of UK businesses are struggling to recruit for 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-strategise.

Improving the usefulness of your data

Organisations need to provide their various teams with data in a transformed, analytics-ready state if they are to capture greater value from it. Modernising 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 synchronise 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. Democratising 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 maximise the impact data can have.

A new approach to engaging with cloud data

For a long time, companies have been misled to some extent by the potential of big data. Often, the right data is big but to win the data race, organisations need more than scale.

The more dynamic data is generated in different formats and by different sources, the more difficult it becomes to integrate. Companies can’t continue to manually migrate their data, as it simply wont flow fast enough. Businesses need to adopt a data analytics strategy that empowers and supports the need of modern data teams. If the objective is for teams to become more productive with their data, they will need to develop the right modern cloud data stack.