Big Data London is fast approaching and topics like AI and ML are taking center stage.
But here’s the catch: Adopting innovative technologies requires mature data processes.
After all, did you know that 87 percent of data science projects never make it to production?
Every European enterprise is interested in looking for new ways to leverage data to improve operational efficiency and better serve customers.
But everything from predictive analytics to training Large Language Models (LLMs) requires reliably moving large volumes of data from a multitude of systems and transforming data to align with requirements. Moving from on-prem, legacy systems to the cloud only compounds that difficulty.
In my conversations with customers, I learn what inspired their adoption of Fivetran, but I also witness the traits of mature data-driven organizations — and the key elements are always automation, reliability and scale.
Without these traits, you’ll struggle to stay competitive and miss out on the opportunity ahead.
Automation unlocks the value of your data — and your team
In a recent conversation, an analyst asked “Why invest in automated data movement when I have data engineers?” and my reply was simply “Why invest in a car if we already have horses?”
If you're looking to drive decisions with your data to keep up with your global competition, you’ll need efficiency — and automation provides efficiency.
Data engineers spend almost half their time maintaining data pipelines. The total average cost? $520,000 per year according to research.
When you consider the volume of data sources most enterprises maintain, it creates a vicious build and repair cycle — a cycle that makes your team inefficient and ineffective.
Consider a multinational, fast-casual food chain like Nando’s. Their data team previously spent 80 percent of its time manually building data pipelines for their marketing team. Automating data movement cut that to 20 percent.
Now, their marketing team has real-time access to all of their data, centralized and ready for analysis. They can build tailored and hyper-specific campaigns without waiting for builds and maintenance — enabling them to jump on trends as they happen.
Their data team is freed to work on higher value work, their marketing team has the agility to meet their market head on — all due to the efficiency automation provides.
Takeaway: Mature data-driven organizations don’t focus on how their data arrived, but rather how they’re going to use it.
Reliability ensures you’re building with accurate, fresh data
Asking your team to train Large Language Models (LLMs) is unfeasible if you can’t even rely on all of your data arriving.
This is especially true for business-critical data, wherein the difference between accurate and inaccurate has dramatic outcomes for your enterprise.
Take JetBlue, the fifth largest airline in the United States. With an average of 900+ flights per day, JetBlue generates millions of data points per day. Their data engineering team needed to rapidly access that information for analytic use cases including informing revenue forecasting, prescribing aircraft maintenance and informing insights on operational readiness.
Using automated data movement, JetBlue successfully moved more than 115TB of data from 130 different systems into its Snowflake Data Cloud. This accurate, always-fresh data enables their entire enterprise to better meet customers’ expectations — including avoiding maintenance-related flight delays by proactively solving problems.
If your business relies on data (which is always the case) — reliable, accurate data is required for any innovation that improves your business.
Takeaway: Mature data-driven organizations don’t focus on if their data arrived, but rather how they’re going to use it.
Data access, at scale, enables innovation at every corner
Democratizing access to data unlocks opportunities to innovate.
That’s exemplified by German real estate enterprise Engel & Völkers. Automated data movement changed the way they worked — even with a headcount of over 16,000 employees.
Previously, they struggled to scale data use across their organization — dealing with data silos and delayed access due to many disparate sources.
Automated data movement streamlined the process. With data from their sources moving automatically into Google BigQuery, they centralized their vast data landscape and enabled self-service analytics.
Within only six months they saw a 100 percent increase in employees using Tableau reports.
This provided data access at scale, providing every team the opportunity to use data to drive decisions and improve outcomes. They even improved their internal operations, as IT leveraged this data availability to optimize their IT support process.
Takeaway: Mature data-driven organizations don’t use data for some decisions, but rather unlock data-backed decisions at scale.
Now before you start attending conferences like Big Data London and grow inspired by the many new technologies through the industry, you just have to ask yourself — is your organization ready?
I’m excited for the conversations I’ll have with fellow data leaders around ways they can prepare for the cutting edge. After all, one of the best parts of my job is inevitably seeing the innovation we enable.
Maybe you can catch a Fivetran-branded taxi on the way!