Business Analysts are expected to do all the things. They need to be lighting-fast coders while being domain experts, guide the machine learning process while explaining the outcomes, make sure the business doesn’t make bad decisions while drawing conclusions from data.
The task list never really ends.
But that’s okay! Business analysts rise up to these challenges on a daily basis, leading data initiatives and business decisions.
They are truly modern-day superheroes. While they may not be in the Marvel Universe yet, Business Analysts still need some support from their companies to make sure they succeed. So much of the business’ success rides on a Business Analyst’s ability to understand data and take action from it. Why not make it easier for them by addressing these challenges?
Here are some of the most common challenges Business Analysts face today and how to fix them.
Analysts are expected to analyze data at a lighting-fast speed
Personally, we think SQL is old school, but that doesn’t mean it isn’t still used widely today. Analysts need to plot graphs and data visualizations to cut time to insight. Quickness is a virtue and often linked to a successful analyst. While speed is the name of the game, this leaves their code wide open to human error and false insights from a dataset.
While getting insight from your data is incredibly valuable, asking your Business Analyst to quickly code and build visualizations isn’t sustainable and could lead to inaccurate information. Additionally, coding and plotting graphs takes away from another expected analyst task: Being a domain expert.
Analysts have to know everything
Analysts are expected to be a domain expert while working in tandem with machine learning engineers and statisticians while also communicating in marketing and other business terms.
Yes, this sounds extremely difficult—and it is. Especially having to code and plot graphs, very little time is left to analyze data predictions. We found most analysts don’t have a lot of time to keep up with industry trends and best practices because their day is mostly filled with data wrangling, cleaning, and coding SQL.
This allows analysts to fall behind and have blindspots.
They’re also expected to be great storytellers
Usually, analysts are the perfect mediator between the data team and the rest of the company. They must know technical terms and how to translate them to non-technical terms, making sure everyone understands the data and how to take action from it.
Analysts have to make stories from the data and explain a narrative. This is especially hard when there are multiple data sources they have to collect data from and put data together to make decisions from.
So how do they solve these problems?
You may have already guessed it, but the answer is AI and automation.
Build a Strong AI System
From the start analysts should guide the building of the business’ AI system. To make any meaningful machine learning models, proper data collection, data input, and engineering. Because the analyst is involved in all of these processes, they should build a strong AI system within the company to scale ML initiatives for the long run.
Read more about building AI systems here.
Automate, Automate, Automate
The tools we have today allow analysts to automate mundane tasks so they can focus on being domain experts and making business decisions. Today, analysts can automate data predictions, data collection, data visualizations, and even formatting databases. Cutting out repetitive tasks allow you to focus on what the data outputs mean, crafting data stories, and fulfilling tasks for non-technical teams.
Turn to No Code
Like we said, coding SQL takes time out of an analyst’s busy schedule and creates the possibility for human error to influence a business decision. By cutting out coding and analyzing your data with no-code tools cuts down time to insight. No-code tools also save time building data visualizations to use in presentations and quickly communicate with team members. Obviously AI even lets you to automate your data predictions and export them to a spreadsheet. This is incredibly valuable for avoiding repetitive predictions other teams in the business ask you to perform.
Give Business Analysts the tools to succeed
With analysts being a vital part of business decision making and AI systems, you need to give them the tools to succeed. Empower them to cut out repetitive tasks to become domain experts and have more time to explore what your company’s data means.
Remember, how your analysts perform affects the decisions your business makes.