Why Your Company Needs a Data Engineer
The growth of data experts in the past 5 years has been exponential. More and more companies are realising the importance of data and its ability to enhance all areas of their business, both customer-facing and internally.
In 2020, Data Engineer was listed as the 8th fastest-growing job in LinkedIn’s emerging job report. Another two data roles made the list too: Data Scientist (3rd) and Artificial Intelligence Specialist (1st). This is no coincidence and each of these roles has a deep connection with each other.
You may already understand the need for a Data Scientist but in this post, I’ll talk through why Data Engineers should be regarded as just as important.
Modern Companies Need to be Data-Driven
A Gartner post in 2019 reported that data is the key driver in company growth yet wasn’t receiving the attention it deserved.
“Data and analytics are the key accelerant of an organization’s digitization and transformation efforts. Yet today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value.”
I think it’s safe to say that we all understand that companies need to be data-driven yet many are only just brushing the surface. With less than 50% of companies documenting it within their strategies, why are data roles ranking so high on growth lists?
The Gartner post also holds the answer to this question.
“By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.”
In just a couple of years, Gartner expects a 40% increase in companies building strategies around analytics. This growth has already started leading many companies to invest in data specialists. Data Science is the hot, new technology role.
Data Science is the poster child for modern analytics. AI and Machine Learning excite leaders and rightly so. The capabilities on offer for businesses of any size were a pipe-dream just a decade ago. However, I feel many companies are falling for the hype and believe that a couple of Data Scientists are all they need to transform their business into a futuristic, data-driven entity.
Now, don’t get me wrong, I’m not trying to downplay the importance of Data Science. In fact, it’s quite the opposite. Any company who wants to truly become data-driven needs to invest in Data Science but alone, it may not have the expected effect.
The real power of data comes when the thirst for science is coupled with engineering brilliance.
The science of going to the moon could never be realised without engineering.
Data Science Enablers
When talking about the 5 common pitfalls of building a data team, Laurence Goasduff in a Gartner post talks about overcoming shortages in data and analytics skills. Interestingly, the point he makes is that the demand for Data Scientist is a lot higher than the supply.
Although I partly believe in this sentiment, I think he provides the reasoning for this later in the post.
“Through 2023, data scientists and analysts will lose 60% to 70% of their productive time to activities like finding, preparing, integrating and sharing datasets, making data engineers a must-have persona on their teams.”
In my opinion, the demand for data scientists is high because it’s taking companies longer than expected to realise the benefits with their current team, so they are looking to add more Data Scientists to help spread the workload.
It’s like having a front end web development team and employing more front end developers because they’re spending too much time working on the backend API and database.
Instead, they should be looking to improve the efficiency of that team and remove that 60 to 70% loss of productive time. This is where a Data Engineer thrives. We’re Data Science enablers.
Rather than spend money on more Data Scientists, spend it on freeing up the ones you already have. The Data Engineering role is built around obtaining, cleaning and integrating data throughout a company. All of the upfront work required to allow the Data Science team to dig into a problem should be done by Data Engineers.
Not only will this improve the efficiency of your Data Science team but their output too. Your data is an asset and should be treated as such. Having an engineer build reliable, scalable and repeatable practices into your data platform is essential for any company looking to use analytics for growth.
Here’s another closing space analogy just because I can.
To get to the moon faster you don’t need more astronauts. You need people to build the rocket that will let the astronaut do their job.
Your Data Footprint Will Only Get Bigger
The whole point of becoming a data-driven company is that you can use these insights for growth. Using data to understand how your customers and business work allows you to employ techniques to grow faster. With this growth comes more data and the cycle continues.
As your data grows, so does the task of managing it. You need to ensure you can scale for the following demands:
- Throughput of data
- Analysing large amounts of data
- Real-time predictions, insights and customer feedback
- Data security
- Data regulations and compliance
All of this can be packaged up into the Data Engineering role. A Data Engineer can allow your business to scale through:
- Increasing data pipeline throughput
- Data warehousing for scalable analytics
- Building a real-time data platform
- Ensuring data is secure in motion and at rest
- Automating data compliance and auditing
If you are serious about becoming a data-centric organisation then managing the data platform is the most important first step. Not only so you’re able to scale and free up your data engineers, but data security, compliance and privacy are (rightly so) hot topics at the minute.
A company can go from hero to zero (see Facebook) due to its data management policies. People are becoming more and more focussed on their data footprint and your number one goal should be protecting their data first.
After all, their data is the reason you’ll be able to grow so quickly, so invest in this asset first before reaping the benefits through analytics.
If this post has sparked your interest in data engineers and you’d like to discuss it more, feel free to chat with me on LinkedIn.
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