In McKinsey’s vision of the “data-driven enterprise of 2025”, automation plays a vital role. By automating recurring decisions and “basic day-to-day activities”, employees will be “free to focus on […] innovation, collaboration, and communication.”
Yet despite this appealing image, even the most ambitious organizations continue to rely on manual data processes. Even in advanced analytics projects, 80% of company time is spent on repetitive tasks like data preparation.
The first step to securing the benefits of automation is understanding the true cost of your manual data processes. Read on to find out just how significant their impact can be — and how you can start your data automation journey.
Processing errors are the most obvious risk of manual data processing — but it’s easy to understate their impact.
If your data processing tasks are time-consuming and repetitive, mistakes are inevitable. Performing the same simple tasks week after week makes it virtually impossible to avoid momentary lapses of concentration or slips of the finger. And that’s all it takes for errors to find their way into your datasets — and potentially impact your decision-making.
With poor-quality data costing organizations an average of $12.9 million each year, it’s not something you can afford to ignore.
Automating data pipelines: Why you should move on from scripts and ExcelManual processes are often reactive solutions — a job needs to be done, and this is the quickest way to tackle it.
But this means your processes will lack consistency. Each of your teams will face different challenges. And if their day-to-day data demands aren’t the same, their manual data processes will likely diverge, too.
They might:
As a result, your organization’s datasets can quickly become inconsistent. This makes it easier for data silos to emerge, as sharing data becomes more time-consuming. And without alignment on best practices, it’s harder to guarantee your data is error-free.
Your business insights are where your data truly delivers value for your organization. But to be effective, these insights need to be timely. Having a clear view of your performance three months ago won’t help you make effective decisions today.
Unfortunately, manual processes mean your insights will be subject to significant delays. This is partly a result of the issues mentioned above. Poor-quality data means you spend more time validating your datasets and correcting errors, and less time drawing conclusions. Meanwhile, inconsistencies between teams make it harder for you to build an integrated view.
How CloverDX can help you modernize your ETLBut there are deeper issues that can arise from an over-reliance on repetitive manual tasks. By spending more time on manual work, staff are more likely to feel stressed and disengaged. This makes it harder for them to develop critical insights or identify innovative solutions.
Manual data processes are time-consuming, difficult to manage, and prone to errors. By automating your data processes, you can deliver higher-quality insights at a faster pace — all while reducing the burden on your teams.
Of course, we don’t want to undersell the challenges of automating your data processes. It requires coordinated effort and ongoing investment to deliver results. But the risks of sticking with the status quo are far greater.
A key first step to automating your data processes is ensuring you have the right tools in place to support your efforts.
When is it the right time to ditch your homegrown ETL?Or perhaps we should say tool. By adopting a single solution, you can avoid the extra cost and effort associated with an over-elaborate tech stack. You’ll be able to develop a coordinated approach to data automation that links up every part of your organization.
To kickstart your data automation journey, look for a platform that:
If you’d like to see how CloverDX’s data integration platform delivers all these features, and many more, book a demo today. It’s time to move on from manual data processes — and we’re here to help you.