In complex environments, cognitive load (relating to tax complexity and multiple inputs that affect decision-making) can significantly hamper the quality and efficiency of project choices. High-performance business analysts can play an integral and important role to avoid this issue by advocating for the adoption of a decision-making compass to deliver insight to stakeholders about how well their objectives may be satisfied by potential alternative courses of action. The next time you’re embarking on a high-stakes project, make sure you have a decision-making compass ready to use, even if only informally defined. While there is no guarantee that applying it to project choices will completely eliminate the risk of mistakes, it will no doubt help move things in the right direction and maximize performance.
Companies that treat the documentation of customer stated needs, wants, demands, desires, ideas, specifications as the focal point of the BA work tend to pay a steep price by solving the wrong problem or addressing a problem manifestation rather than the underlying business issue. And now, with LLMs readily available to produce similar outputs at a much faster pace, there is reason to believe that these document-centric roles will soon join the statistics of LLMs replacing human jobs.
An adept BA, on the other hand, has no reason to fear an existential threat. It’s hard to predict exactly how generative AI will end up integrated into our work lives going forward. Still, LLMs are far from being able to send stakeholders in the desired direction, identify the initiatives that actually make sense and are likely to produce the desired result, and reliably answer the question, How does this project/feature/requirement we’re working on contribute to the organization’s strategy?
It’s no longer rare to see machine learning (ML) models used to support a variety of business decisions, from whether a financial transaction should be sent to the fraud investigation team, to what discount a distributor should get.
Still, even in organizations that have embraced ML-based systems, it’s common for business problems that could benefit from machine learning to be solved using a less effective (and often more costly) approach.
As a business analyst, it’s useful to be aware of some signs your business problem might benefit from ML...
Being “data-driven” doesn’t help create project success; being evidence-based does. Evidence-based problem solving reduces the risk of blind spots and confirmation bias and increases the chances of achieving the desired outcomes. In high-stakes projects, risks can be dramatically reduced when a business analyst is willing to apply first principles thinking, hypothesis testing, and information value analysis to integrate the best evidence into the decision-making process.
Sometimes the best solutions are right in front of us, hidden in plain sight. Get in the habit of working from first principles and you’ll find it easier to cut through preconceptions to change the business question and quickly see alternatives that you may have missed.
Does it make sense to merge Agile philosophies with data science? The short answer is yes, as long as the organization recognizes and accommodates the ambiguous, non-linear nature of the data science process rather than expecting data scientists to fit into the same mold they’ve adopted for “Agile software development”. The problem, in my experience, is that this rarely happens. Probably because the data science field is still new, many organizations are still trying to shoehorn data science into Agile software engineering practices that compromise the natural data science lifecycle.
The quality of any data analysis created to inform business decisions will ultimately be constrained by the quality of the underlying data. If the data is faulty, then the analysis will be faulty too. This is why data wrangling–the transformation of raw data into a format that is appropriate for use–has become such a ubiquitous task in most organizations. Unfortunately, the significance of data wrangling is still often overlooked. And this is where data-savvy business analysts can help save the day.
The practical applications of data science are multiplying. From predicting if a delivery will arrive late to recommending how much herbicide to use to save money and protect the ecosystem, there are endless examples of organizations harnessing data science solutions to improve the efficiency and quality of business decisions.
As mentioned in my previous article Three Myths About Data Science Debunked, sooner or later business analysts will be involved a project with a machine learning or AI component. While BAs don’t necessarily need to know how statistical models work, understanding how to interpret their results can give them a competitive advantage.
This article discusses three concepts that can help analysts add value to data science projects (future articles will cover additional ones). Cultivating skills in these areas will increase your ability to build cross-functional alignment between business and data science teams and prevent bad decisions based on flawed analyses.
Despite the lack of consensus on the definition of data science, many organizations already have a data science team. And even in companies without data scientists, sooner or later business analysts will join a software or process improvement initiative with a machine learning or AI component. When that happens, good understanding of what data science is (and isn’t) can make a big difference in a BA’s ability to create value.
Study after study in behavioral science show that certain approaches are more effective than others when we’re trying to convince others to see things our way. Leaders in many industries, including the public sector, have learned the wisdom of using the latest evidence of what influences behavior and applying those insights to solve practical issues. As a result, behavioral insights have now been successfully used to convince people to reduce their energy consumption, contribute a larger amount to their retirement fund, eat healthier food, and more.
Most of us have seen praise and recognition at work go to the people who react quickly when a problem occurs:
The majority of IT business analysts spend their careers in “reactive mode”. They are assigned to tasks like define the requirements for a new partner loyalty program, create user stories for an enhancement to a billing system, and go about delivering their artifacts.
Data-inspired analysts are those analysts who make a conscious decision to “go upstream” and find data to help their organizations identify the areas of value creation with the highest return on investment before jumping into “solution mode”.
The COVID-19 crisis is reshaping businesses and livelihoods, and seasoned and new BAs alike have an unparalleled opportunity to put their analytical skills to great use. Whether you are still employed, or has been laid off or furloughed, now--while we navigate the pandemic crisis—is a good time to demonstrate the value of business analysis and the contributions you can bring to your current or future employer. Here are three examples of how you can accomplish that.
As the pandemic continues to upend life around the world, data, big and small, takes a central role in mobilizing the right efforts to prevent a much greater calamity. And as people and organizations face unprecedented hardships, business analysts, data scientists, and data analysts are going to be integral to the solution. We have the skills that the world is counting on to arm our leaders with the best possible information as they are tasked with making immediate choices, allocating resources, and anticipating the next obstacles to overcome.
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