The role of a business analyst comes in different forms and can even span across different job positions (think business intelligence analyst, marketing analyst, financial analyst — the list goes on). Regardless of the title, the core mission remains pretty much the same: generate insights for your organization, generally with a big dose of data as a starting point.
However, things are moving so fast in the data analytics space (particularly when it comes to hot new topics within data science, machine learning, and AI), you might be wondering: Will my job as an analyst still be in demand in the years to come?
The answer is yes! Here’s how you can be a successful business analyst now and in the future as AI plays an increasingly important role in organizations across the globe.
Start Thinking Bigger Than Spreadsheets
With the drastic increase in the volume of data available, the diversity of sources, and the continuous evolution of data processing technologies, the spreadsheet has shifted from being an enabler to more of a limiting factor. Issues such as data accuracy, siloed work, security, human error, and limitations with large datasets only scratch the surface of the frustrations associated with spreadsheets.
As a business analyst, start thinking about the next level of data preparation — ideally, it should move out of isolated spreadsheets and into the same place that advanced analyses and modeling is happening in your organizations so that projects can be more easily expanded and developed.
In other words, even if your immediate use case is not to build a machine learning model, having one central location for all of your work will help ensure transparency and enable collaboration, potentially with data scientists who can help you take your analysis to the next level.
Work on Upskilling Yourself
In any role, continuous learning is key. This is arguably even more true for different flavors of business analysts, as they find themself in a fast-moving field with new, sexy job titles everywhere (like data scientists, data translators, analytics product owners, and more).
Upskilling doesn’t necessarily have to mean becoming what’s being referred to as a citizen data scientist, though that’s certainly an option. It can also mean developing new, in-demand analyst skills that will help you become an even more vital member of the organization. This might include things like more robust data visualization skills, coding (SQL, Python, etc.), knowing how to leverage AutoML, or even additional soft skills like being able to present compelling data stories to the business.
Learn How to Talk the Talk When It Comes to AI
Not every analyst is interested in becoming a citizen data scientist — and that’s ok. But at the very least, it’s a good idea to understand key terms and concepts around data science, machine learning, and AI so that you can collaborate effectively with people in the organization working on those initiatives.
After all, being a successful business analyst is about providing great insights, which means not keeping to yourself and working in a vacuum or silo on one-off projects. Even if you’re not ultimately the one working on data science, machine learning, or AI initiatives, it’s still important for everyone working with data to be rowing in the same direction (especially if these new technology areas are of interest to higher-ups and executives in the company).
Give Away Your Legos
Whether you like it or not, Everyday AI is quickly becoming a reality at many organizations around the world. That means weaving data and AI into organizations’ DNA rather than siloing data work into specific teams or roles. Inevitably, Everyday AI will lead to more and more people on your team handling data, even if they don’t have “traditional” data roles or titles, like business analyst.
It might be tempting to react to these changes by clamming up, holding your tools and processes even closer to restrict the number of people working with data. But if you’re familiar with Molly Graham’s philosophy toward scaling teams (which can also apply to scaling something like data access), you’ll know how important it is to give away your legos.
In other words, instead of feeling emotional about other people working with data and worried that they are trying to take your job — it’s tempting, for sure — sharing your knowledge, tools, and more can help everyone be more successful. And bonus: You can move on to bigger, better, more exciting things and projects once team members can do some of the relatively mundane data tasks on their own.