In recent times, data-led decision making has become a priority of utmost importance for business leaders across all industries. Put simply, deriving meaningful insight from masses of enterprise data is not just a nice-to-have competitive advantage, but a prerequisite for informing real-time decisions and maintaining relevance in today's shifting business environment. Take, for example, the way banks and financial institutions are using real-time analytics to uncover fraud. Even as recently as just five years ago, mining these insights in sufficient time was a nearly impossible task, but the arrival of cloud data analytics five years ago changed all that.
Enterprises continue to invest heavily in the cloud, putting immense pressure on data teams to deliver more useful insights across their organisations. Despite this, our recent research revealed that UK data teams spend nearly half (48%) of their time, on average, on data migration and maintenance, making it extremely challenging to fully capitalise on the potential of data talent and uncover business insights. We need data engineers to invest less of their precious resources on maintenance and reactive tasks, and concentrate their efforts on fully leveraging the scale and performance benefits of the cloud.
But how do we help them do that? Let’s first consider some of the overarching challenges they encounter on a regular basis:
· Volume, variety, velocity: Today’s data teams struggle with these “Three Vs” of modern data. Businesses need to have data insights ready so they can act on information as close to real time as possible, but often struggle to do so due to one of these common barriers.
· The perennial skills gap issue: By and large, businesses lack the resources to handle escalating demands for data from across the organisation. According to a DCMS survey, almost half of UK businesses (48%) are recruiting for roles that require hard data skills but 46% have struggled to recruit for these roles over the last two years. Realistically, it’s just not feasible from a human resource or cost standpoint right now to hire enough highly skilled data engineers to keep up with these changing data needs.
· The obstacle of legacy tools: Many organisations are also dealing with outdated legacy tools that are complex, inflexible, slow, and costly. Not only does this make data processes more time-consuming for data engineers, but it eliminates the possibility for the democratisation of data across the enterprise.
Unlocking data’s full potential
When it comes to adopting a cloud data approach, there are elements of a modern cloud stack that can help data teams be more strategic, solve these key problems, and move insights across the organisation more efficiently.
Far too much time is spent on manual integration, whereas greater automation can free up time for teams to focus on the unique business logic of the data processing itself. By transforming raw data into the refined, analytics-ready data required to support business intelligence, teams can better manage the “Three V’s” of data and automate non-critical tasks to make crucial decisions faster.
To tackle another of the common challenges faced, one way of bridging the skills gap problem outlined is to lean more on low-code and no-code tools. By enabling more business users to easily analyse data sets, this approach can broaden data teams and empower more users across the organisation to quickly unlock key business insights. This approach democratises data use and frees up valuable time for skilled data engineers to focus on more technically challenging and value-adding tasks and take full advantage of what the cloud has to offer.
However, to fully capitalise on modern cloud data tools, a business must have access to all of its data and have a modern data integration strategy to bring that data into a cloud data platform and transform it to make it useful for analytics. On-premises and legacy extraction, transformation, and loading (ETL) approaches to transforming data are inflexible, time-consuming, and no longer viable considering the unprecedented amounts of data organisations are dealing with today.
Unlike this traditional approach, adopting modern cloud ELT allows data teams to be more strategic with their cloud data platforms. This strategy is much more agile and helps to automate and operationalise data insights, allowing teams across the organisation to access and act on the same data that’s being used by the analysts in real time.
Reaping the benefits
Every modern business needs data to stay competitive, and implementing a truly modern cloud data stack can help them to handle its ever-growing volume, complexity, and speed. By fully embracing the cloud and its scalability and performance benefits, organisations can boost the productivity of their data teams, overcome the traditional challenges faced and reap the rewards of real-time insights. This ultimately allows them to spend the bulk of their time on more strategic work, driving greater business value and keeping them challenged and fulfilled at work in the process.