Australia’s industry landscape is rapidly evolving, thanks in part to AI becoming more integrated into operational and administrative processes across virtually all businesses.
Not only has AI and business automation reshaped day-to-day processes, it has also disrupted how we create value and make decisions at work every day. Likewise, how we conduct business analysis.
The old ways can no longer suffice. Business analysts must learn to embrace this unfamiliar technological future, because opting to sit this one out could lead to being left behind. To help you tackle this great unknown, read on to know the key technical skills for business analysts in the age of AI.
Data literacy
The first step of reducing technological uncertainty is to arm yourself with knowledge, particularly on data. This includes understanding data structures, building familiarity with different analytics tools and being able to interpret datasets, all the while being able to identify trends and derive insights.
Given all the cross-platform workflows arising in data analytics disciplines, the next generation of data scientists will naturally need to have a strong working knowledge of SQL and advanced Excel. It’s also recommended to be familiar with other programming languages like Python, R Studio and Java. Business analysts can learn these skills through various learning pathways and in a learning environment that suits them best. For instance, you can now undertake a Master of Data Science online, supporting you in learning from some of the country’s best data scientists and engineers at your own pace.
Prompt engineering
Generative AI engines are an exceptional tool when you need information quickly. This is because by cultivating LLMs (large language models), gen AI engines are capable of accessing vast volumes of data and utilising Machine Learning (ML) to generate insights based on this data at great speed. However, while it is efficient, the critical question remains: is it providing the right information and is it delivered in the way we need?
When AI output is unclear or poorly framed, there is a risk that business analysts might interpret or present inaccurate findings to stakeholders. This not just spreads misinformation, it also puts the business analysts’ reputation and credibility on the line. This is why it’s crucial that business analysts master the art of prompt engineering.
Here are a few tips and tricks to improve your prompts:
- Be clear on your objectives
- Utilise action verbs to specify the desired action
- Use precise language and avoid ambiguity
- Detail the target audience
- Include relevant facts and data
- Define the intended output format and length
AI development
Alongside mastering the art of prompt engineering, business analysts can also benefit from learning how to set up their own proprietary AI systems. In industries where centralised data management is becoming so crucial for maintaining compliance, managing supply chains, and monitoring metrics (including environmental performance metrics), proprietary AI solutions make it easier for analysts to isolate data insights that are most relevant to their reporting.
Proprietary AI systems can also support organisations in accessing historical data records easier than manual processes, which may require sifting through dates and dates worth of files to find the required insights. When trained on internally managed datasets (like company datasets), your proprietary AI software can also maintain accuracy, as it won’t attempt to provide an answer based on poorly maintained external datasets (i.e. open source datasets, for instance).
Granted, AI development is still a specialised skillset, even with cloud devs platforms and processes. Even so, business analysts looking to futureproof their CVs will definitely gain some attention from prospective employers if they can showcase their own proprietary AI systems in their portfolio.
Data visualisation
Although AI, made efficient with prompt engineering, can generate valuable insights for business analysts, not every output is suitable for all stakeholders. Take lengthy reports as an example. Busy stakeholders like senior managers and directors wouldn’t have the time to read all of that. They need reports they can quickly grasp the key information from. One of the most efficient mediums is through data visualisation.
This technical skill focuses on transforming numerical and written data into intuitive visual elements, such as charts and dashboards. To do so, business analysts need to familiarise themselves with tools like Tableau and Sisense.
Making charts may seem simple and straightforward, but effective data visualisation is far more complex than that. Business analysts often need to condense dozens of pages into only one to two pages of visuals. This requires careful consideration on which chart is the most appropriate given the type of data, whether the visualisation communicates the insight clearly and if information presented is relevant or repetitive.
Aside from visual contents, business analysts also be strategic on layout and priority. The top of the dashboard or page is often reserved for key elements like KPIs. It is then followed by charts. Charts at the top are often considered high priority. Layout is especially important because it helps stakeholders identify what matters most.
AI governance and ethics
AI has become so effective in improving processes that organisations are increasingly integrating it into all aspects of the business. However, despite its technological benefits, various ethical concerns relating to trust, accountability and fairness are being raised.
One of the most prominent ethical concerns surrounding AI is bias. It all stems from where AI gets its data and algorithms, which is from Big Data, a huge dataset that also contains economic, social and cultural bias. If this is the foundation of AI’s knowledge, then there is a high likelihood that output it releases is not unbiased. Repercussions if left unexamined is that individuals and groups can be negatively impacted by unfair or discriminatory results.
To address this challenge, business analysts must take an active role in determining the credibility of their AI findings. They can do so by assessing their data sources, looking out for potential biases and validating assumptions through research.
Equip your CV with these in-demand AI skills
In the age of AI, business analysis is no longer what it used to be. Technological prowess is now a fundamental requirement as organisations are quickly adopting AI to improve efficiency and decision-making. For those unaccustomed to AI or the developing technologies, it can be a bit daunting to get started, but the developmental areas we’ve outlined above are proving to be potent jumping off points.
Be sure to start your upskilling journey in the age of AI with any of these technical skills. In doing so, you can demonstrate to prospective employers that you’re committed to keeping your finger on the pulse for all things AI and business automation.
