Lie #4 — You need to hire to close the skills gap
Datanova 2023: The data lies (and truths)
There’s a growing chasm separating the digital-first businesses from the rest of the pack.
But many are stuck somewhere in the middle. And they’re lost. The IT playbook that companies have long-relied on is defunct, leaving them struggling to plot a path forward for their AI and analytics endeavors.
As businesses continue to diagnose why their efforts are coming up short, many think it’s a skills gap. They’re convinced that just a few more hires or a couple more training modules is all they need to finally become data-driven.
You can’t hire or upskill your way to success.
Companies spent the last decade racing against rivals to hire top engineering talent. Data-related jobs of all stripes quickly turned into the most sought after roles — and the salaries proved it. Businesses soon realized they couldn’t compete with the lucrative compensation offers that others dangled in front of prospective candidates.
So companies turned to the next viable option: trying to build up the expertise internally. Corporate upskilling became one of the hottest trends. The upside was that many workers got armed with the core abilities needed to remain competitive in their careers in the coming decade. But it’s very hard to just transform your existing workforce into data professionals. And doing so requires a level of investment that many companies simply can’t afford.
Now, despite all the hiring and upskilling, businesses are still at a loss as to why their AI efforts are struggling to gain momentum. They’re taking the wrong approach to overcoming a talent shortage.
Technology can solve the skills gap for businesses.
Leaders are realizing what they really need is a bridge between those who know the questions to ask and those who can wrangle the data needed to get the answer.
Tech can serve as that bridge. The emerging future is one where employees can access powerful AI and analytics capabilities through an easy-to-navigate interface that automates most of the necessary work, alleviating an already overworked IT team to focus on more high-value tasks.
Will that turn your business analyst into a data engineering expert? No, but it will help them get more accurate insights faster, all without the need to waste engineering time trying to move and clean-up the necessary data.
Becoming data-driven requires simpler self-service solutions, not more expensive talent.
Companies can hire all the engineering talent they want, but if the data is bad, the outcomes will be too. It’s such a critical step, yet it’s so often overlooked.
We at Starburst know this because we see it everyday. We quickly become the foundation that customers build their AI and analytics efforts around because we solve that pain point. They trust us to ensure the information being fed into models is accurate and up-to-date. And with an interface focused on self-service, companies can easily build the sought after connection between analysts and engineers for much cheaper than the cost of a team of new data experts.
What sounds more powerful to you? An analyst that just has access to a few Oracle warehouses, supported by expensive engineers that just spend their time building ETL pipelines? Or one that can query information across every application in the company through a single platform all without wasting the time of your engineering team?
Don’t spend valuable resources trying to make your analysts into engineers. And don’t waste money on an army of new data technicians. Let Starburst fill in that knowledge gap.
If you’re a data rebel, register now.
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