Data engineers are among the most important resources an organization can have, and yet according to a Gartner Data Team Management Survey, fewer than half of respondents invest in the role. Why? Until recently, the critical role of engineers has gotten lost amongst the ever-accelerating push for data scientists to derive insights that inform business decisions. In other words, organizations were focused on outcomes, not necessarily on the infrastructure to efficiently reach those outcomes.
But that’s changing. As data becomes more readily available and technology advances, organizations have realized that speed to data access is an important competitive advantage — one that is hindered without efficient data preparation. And, since much of that data contains sensitive personal information that must be protected by laws and agreements, an inefficient data preparation process is only delayed further by complicated legalese and data protection implementations.
This is where data engineers come in. In Gartner’s report, Data Engineering is Critical to Driving Data and Analytics Success, data engineering is defined “as the practice of making the appropriate data accessible and available to various data users (e.g., data scientists or data analysts) at the right time.” The report goes on to say that data engineers must be collaborative and cross-functional in “building, managing and operationalizing data pipelines in support of data and analytics uses,” — all while ensuring compliance with data privacy requirements and regulations.
It’s never too early to invest in data engineers, but it can be too late. Here are three signs your organization needs data engineering.
1. More Time is Spent Prepping Data Than Using It
If you’re like nearly a third of data and analytics leaders, your biggest barrier to executing data-driven initiatives is deploying data and analytics using existing business processes and applications. It’s a common problem: Many organizations don’t have the internal capabilities to manage data from ingestion to transformation to production.
According to Gartner’s analysis of data science teams, nearly half of the time spent on data projects happens before even starting model development. This means if a data science project takes a month, more than two weeks is spent analyzing the problem and collecting and preparing data. Data engineers — particularly when equipped with the right automated data governance solutions — are able to shorten that time substantially, allowing data consumers to securely access data and derive insights faster to enable sound data-driven business decisions.
When time is of the essence, this can make an invaluable difference. Take the COVID-19 pandemic: its highly transmissible nature and fast-moving community spread mean the response time to virus exposure can be the difference between suppression and eruption. For The Center for New Data’s Covid Alliance, a nonprofit coalition of science, technology and policy experts working towards a coordinated response to COVID-19, this scenario is a reality. The Covid Alliance uses geolocation data to predict potential superspreader events so that state and local governments can proactively implement mitigation measures, like social distancing guidelines, to slow or suppress virus transmission.
The Covid Alliance is able to mine these actionable insights because its data engineers proactively manage the data pipeline from ingestion to curation to production. Immuta helps streamline this process by automatically applying sensitive data discovery tags and global policies to incoming data, then implementing data access controls at query time so data engineers don’t lose time manually executing data privacy mechanisms. Without data engineers using dynamic data solutions to lead this process, the Covid Alliance and other organizations with time-sensitive data projects would be unable to leverage data in a meaningful way.
2. Your Data-Driven Initiatives Rarely Succeed
Despite organizations investing substantial amounts of money in data-driven initiatives, 87% fail to make it to production. If this sounds familiar to you, it might be a sign that you need a data engineer — or several of them.
Why do these initiatives that companies claim to be top priorities succeed at such a low rate? Too often, the data curation and management process becomes a game of hot potato, in which multiple teams and stakeholders are involved, but there’s no clear delineation of responsibilities. As a result, data gets passed around and discussed with little forward movement.
Gartner’s Data Engineering is Critical to Data and Analytics Success report sums up this dilemma simply: “It is…becoming clear that the creation and maintenance of these data pipelines won’t take care of itself; it must be someone’s job.”
That “someone” is a data engineer. Without a data engineer who is specifically responsible for building and maintaining the data pipeline, it’s difficult and time consuming to operationalize high quality data that is both compliant and secure. As a result, data-driven initiatives are slow to get off the ground — if they even make it that far — and are more likely to be abandoned.
While data engineers help ensure a functional data pipeline exists and is well-maintained, executing a data project is generally a cross-functional effort, with data engineers at the center. Creating a RACI model that clearly lays out which functions are responsible and accountable for various project-related tasks, versus which will be consulted or informed, can help keep an initiative moving in the right direction. This way, data engineers know where their responsibilities as data owners begin and end, so they can prepare data adequately and efficiently.
When data engineers have the right tools in place to manage data pipelines, it shows in the business results. Databricks users with Immuta’s core, native capabilities experienced a 40% improvement in productivity and a 300% increase in data utilization, enabling teams to complete more data projects and unlock more data-driven outcomes.
3. There’s No Point Person for Data
One of the reasons so few organizations invest in a data engineering function is due to a lack of understanding about what it takes to build and manage a data pipeline. We live in an environment where we expect data to always be at our fingertips, without necessarily giving thought to the process behind making it usable. That process, for the average person, is not easy.
Many organizations have a skills gap that makes data preparation for production overly complicated and labor-intensive. Without the right skills in place, several different data team members may try to fill in the gaps. This not only means that multiple contributors are needed to do the job of one person — and in doing so are distracted from their official job responsibilities — but there’s also no guarantee that the way they’re going about these tasks is correct, consistent, secure and compliant. Clearly, this approach is unscalable and risk-prone.
Data engineers help solve this. Their unique combination of skills differs from data architects, data stewards and data scientists: Whereas these roles focus on data strategy, quality and application, data engineers work with the physical data to deliver usable outputs to those functions that meet standards and expectations. Data architects, stewards and scientists, as well as business analysts and legal and compliance teams, rely on data engineers for data outputs, making the data engineer the point person for data. This mitigates confusion and workarounds that can put data quality and protection at risk, and may be why demand for data engineers has recently increased by 50%.
Gartner’s Data Engineering is Critical to Driving Data and Analytics report lays out the skills to look for in high performing data engineers and illustrates their position within a data team relative to data architects, data stewards, data scientists and business analysts. Data and analytics leaders who find themselves lacking a clear point person or team to manage data needs can leverage this report to begin building out a successful data engineering function.
The forward-moving momentum data engineers deliver to data-driven organizations is invaluable. While hiring for data scientists spiked as businesses realized the competitive edge data can provide, the need to efficiently and securely operationalize that data has caused the demand for data engineers to eclipse that of data scientists. As leaders build out data engineering teams, they should look for a specific combination of hard and soft skills, and set their data engineers up for success with automated data governance solutions like Immuta, which can streamline data preparation and protection.