Business success has been totally redefined in recent years, with big data now a necessary driving force of decision-making and innovation.
For decades, data was simply collected and stored with little emphasis placed on assessing what information could be pulled from it. Fast forward to today, and digital transformation across industries is being driven by actionable insights derived from large pools of data, meaning companies are getting closer to their customers than ever before. Mining insights in real-time allows financial institutions, for example, to detect and predict fraudulent activity and prevent devastating losses for account holders – a task that was nearly impossible five years ago. The evolution of the modern data fabric shows no sign of slowing down either, with cloud analytics now integral to the visibility and movement of data.
Indeed, the continued boom in cloud investment is encouraging data teams to make it worthwhile and use it to effect improvements to businesses. But that process is not without its challenges; our recent research shows cloud data integration processes to be vastly outdated in many cases, with three quarters of UK data professionals believing it’s costing their organisations time, money and productivity. In the context of an incessant war for data talent, which is hitting businesses hard, the situation soon becomes untenable. A modern cloud data approach is needed to help businesses alleviate the burden that manual migration places on data engineers.
This approach needs to be founded on driving full scale and performance benefits from the cloud, and it all starts with considering the barriers that often hinder data engineers in their efforts to deliver transformation:
- Volume, variety and velocity: otherwise known as the “Three Vs” of modern data, assessing the volume, variety, and velocity of data is key to understanding how big data can be measured, but can often stunt migration efforts due to the complexity involved.
- Battle for big data skills: today, organisations broadly recognise the value of big data to boost productivity, innovation, and the bottom line. Yet 46% have struggled to recruit for roles with the necessary skills over the last two years, according to recent DCMS research. A demand-supply imbalance means there are simply not enough data engineers to keep pace with evolving data needs.
- Outdated data management: legacy tools and processes are a root cause of cost leakages, potentially totalling £32.5 million a year. The impact of inefficient data maintenance can be felt across the business too, not just within data teams, making it more difficult to democratise data for all those leveraging it.
Tapping into greater data opportunities
Realising the full power of cloud data analytics as a strategic asset, and overcoming these common problems, depends on how effectively teams can implement a modern cloud stack.
Data teams grappling with manual integration are often deprived of time that could be reallocated to strategic and analytical work, making the case for enhanced automation. The “Three Vs” of data can be made much more manageable if raw data is transformed into analytics-ready data, refined for more immediate business application, as quickly as possible once it’s generated. Streamlining the data integration process in this way creates a positive ripple effect for data teams potentially burning out from large workloads, thereby making it easier and quicker for critical insights and results to be delivered.
Providing greater access to enterprise-grade, low-code and no-code interfaces is another means of addressing the data skills gap. Minimising the effort required to analyse data empowers more business users – who typically have little to no knowledge of programming – to quickly tap into key business insights and accelerate their analytics projects. Ultimately, such an approach democratises data use and alleviates the workload of overstretched data engineers, who can instead invest time taking full advantage of what the cloud has to offer.
The most valuable change for IT teams seeking to keep pace with the paradigms of the cloud and sophisticated needs of enterprise, however, is to move all data into a single cloud data platform where they can analyse it. Locally sourced and legacy Extraction, Transformation, and Loading (ETL) processes for data have long been inflexible, time-consuming, and are simply incapable of dealing with the unprecedented volume of data organisations now collect on a daily basis. More modern processes take place in the cloud, where they benefit from automated data insights and are operationalised in real-time for teams across the organisation, making for faster data ingestion and greater agility.
Deriving greater benefit for the business
We’ve spoken about the value of data for a long time, but it now plays a fundamental role in driving competitiveness. The ever-increasing volume, complexity, and speed at which every business’s data footprint is growing means a truly modern cloud data stack that helps organisations manage, automate, and analyse the influx of unstructured data is an absolute must. Equipping tech talent with the pathways and processes to use data more easily and effectively keeps employees fulfilled, supports innovation, and fuels the wider organisation with the collective insights to become truly data-driven.
