In an increasingly data-driven world, organizations can’t compete unless they’re equipped to effectively sift through huge amounts of data, understand it, and leverage it for growth. The opportunity, to put it mildly, is massive. According to Forrester, up to 73% of company data goes unused for analytics and decision-making.
This is a significant issue, especially as current global workforce trends driven by the pandemic mean many teams are spread across countries and time zones. With more remote workers and tons of new channels, there’s an even greater importance placed on effective data management.
Of course, the options for managing company data are vast, and not just any data expertise will suffice. Recent investments in AI-focused data management platforms clearly illustrate that companies want talent skilled in future-forward platforms. Databricks is at the top of the list.
As more organizations recognize the importance of leveraging Databricks for their data analytics and machine learning in their operations, it’s crucial that you set your business up for success by hiring data management talent with valuable skill sets.
What is Databricks?
Databricks succinctly refers to itself as the “data and AI company.” It’s built on open source and helps organizations unify their data, analytics, and AI quickly and effectively. Databricks is probably most well-known for its “Lakehouse Platform,” which works by combining the best elements of data lakes and data warehouses.
What makes the lakehouse approach so much more effective is that it’s easier to scale and accommodate widely varying types of data and use cases. While legacy programs often require combining multiple applications to achieve the same outcomes, lakehouse provides a common platform to serve companies’ data needs – from traditional reporting, to machine learning use cases, to generative AI.
Build a Databricks team
As more companies and leaders recognize that Databricks technology can make or break their data management and AI future, one of the biggest lifts is actually implementing it. It comes down to the right skilled talent or IT solutions partner.
Because Databricks and lakehouse are a design shift from standard cloud data warehouses or traditional on-premise data solutions, many companies tend to misstep when hiring. They’re likely to recruit computer science graduates with basic Python experience, or place traditional data practitioners skilled in SQL and ETL in Databricks positions. Unfortunately, that approach hinders scalability and often results in unnecessary technical debt.
While upskilling an existing team using Databricks training is an option, it’s not as simple as flipping a switch. One solution is to work with an organization like Smoothstack that specializes in training qualified tech talent specifically on the Databricks platform. That way, companies looking to benefit from Databricks’ capabilities gain access to the exact skill sets they need – when they need them.
For example, there are three main certifications in the Databricks ecosystem, including: Data Analyst, Data Engineer and ML Engineer. The benefits to bringing on skilled Databricks talent in these areas right out of the gate abound:
- Time is money: An organization is able to maximize data management through Databricks quickly and efficiently when skilled talent is already in place. There’s no need to wait for the team to get up to speed. Databricks-skilled talent hits the ground running.
- Find the right fit. When you start out with talent proficient in Databricks, you ensure that all elements (and benefits!) of the technology fit appropriately into your tech stack. This correlates directly to the quality of the data solutions and use cases built, and reduces the risk of technical debt that will eventually require clean up.
- Prove ROI. Companies that bring on already skilled Databricks experts are ahead of the game in proving the ROI of the platform. IT leaders can make the case for the technology spend and its benefits by tapping into knowledgeable team members already adept in Databricks.
Effective data and AI management: Meet the moment
Organizations across industries and sectors are increasingly coming to terms with the fact that they must meet the data management moment, and they need the right talent in place to get there. They simply can’t afford to put data management on the back burner. IBM found that poor data quality costs the U.S. economy up to $3.1 trillion annually. Steering clear of such losses is critical, and hiring Databricks-certified pros is an important step forward.