When I first became interested in coding, I was working as a senior accountant creating month-end financial packages. Half of my month was working as an accountant, while the other half was spent performing business analytics and building intricate dashboards in Excel. I quickly noticed a lot of my deliverables required a dynamic approach to accurately tell the data’s story.
Ultimately, my research focused in on analytical strategies and the power of using a variety of input fields and statistics to create formulas specific to the department’s unique variables. My coworker and boss at the time supported my potential and recommended I seek out training in a technical field, which is when I discovered Data Analytics. Confident and driven in my decision, I left my job as a Senior Accountant to commit myself to Columbia University’s certified Data Analytics program.
Even though it felt like a gamble and I was faced with an overwhelming amount of knowledge, languages, and software to learn, pursuing my education left me feeling empowered and positive that my hard work would lead to a brighter future.
Now that I’ve found my place at Effectual, I’ve been introduced to so many new opportunities and always volunteer to take on ambitious tasks. My programming skillset also strengthens my ability to make the cloud more accessible for Effectual customers.
Rethinking your cloud usage
The cloud can sometimes seem like an unapproachable, ethereal concept, but it often only requires a mindset shift in how we approach it.
As a cloud data analyst, I get to dive into how different companies are using their cloud spend, or how they’re preparing for a move to the cloud. I like to tackle the cloud like I tackle a coding project: by looking at the problem I’m trying to solve and seeing if there’s a more efficient way to do things.
Companies will often look to reduce their cloud spend. But that’s not always the right choice – you don’t want to spend less money if it means you’re not progressing. Instead, I look at where inefficiencies can be shored up.
I like to tackle the cloud like I tackle a coding project: by looking at the problem I’m trying to solve and seeing if there’s a more efficient way to do things.
For example, many AWS users are familiar with Amazon RDS or Redshift Database and use them frequently. As AWS continues its rapid pace of innovation, it regularly releases new services that might be beneficial for certain projects.
One of those new releases is Athena, a query service where you can gather data from Amazon Simple Storage Service (S3). Alternatively, you can use Kinesis Firehose or Elastic Map Reduce; all those services let you process data without using a database. If you can have your data in S3 and use that mixture of services for your end product, you can reduce the processing time needed – and thus, streamline your costs for the cloud.
By looking at where inefficiencies lie, you’re increasing the accessibility of the cloud to help where it would be most impactful. Taking a step back and really looking at the “why” behind your solution is critical.
Putting your data to work
Once you have that understanding of your cloud spend, you can also look for other ways to make your processes more efficient. For example, a lot of us working with infrastructure are building services that could be standardized, so we’re exploring AI and machine learning solutions. Those solutions use infrastructure as code to build without having to involve an entire cloud ops team.
We also use AI and machine learning to help us make agile projections that aren’t normalized merely based on what a customer has shared. Of course, our solutions are tailored to each individual customer, but that doesn’t mean they need to be misinformed.
By looking at customers with similar platforms or customer bases and using data from those situations, we are able to make better recommendations for that customer when they’re moving to the cloud. And we present those recommendations in ways that are easily understandable and demonstrate why our strategy is the correct decision.
Continuing worldwide growth
One of the reasons I love working at Effectual is the way we empower companies to come into the market. I’m always excited to hear about new customers getting into the cloud or expressing an interest in coding or data analytics. Even if it’s a smaller business or a government agency, we want people to feel like the cloud is accessible.
For new developers looking to get into the field, this work can be challenging at first. But when you get into it, you can own your skillset and create unique solutions.
Despite not having the most “traditional” background in coding, I’ve always felt welcomed and invited to be a part of new projects and groups in my career, and it’s taught me more than I ever would have thought.
I’d encourage everyone with any interest in technology or learning how to code to go after those passions — you never know what you’ll build!
Josefina Amaro is a Cloud Data Analyst at Effectual, Inc.