You're tasked with improving algorithmic performance. How do you navigate conflicting business priorities?

When I’m tasked with improving algorithmic performance amidst conflicting business priorities, I start by assessing the needs of each priority. I communicate with stakeholders to understand their goals and seek common ground. I then prioritize tasks that provide the most significant performance impact while aligning with business objectives. This approach helps me balance technical improvements with business needs effectively.

Before jumping directly into development, analyzing the requirements is very important. Here are the steps I generally follow in my work: 1. ?? Gather requirements. 2. ? Get clarifications from stakeholders, if any. 3. ?? Analyze different ways/tools available to perform the task. 4. ??? Choose the most appropriate way/tool for the task. 5. ?? Start developing! In summary, not all tools will be appropriate for every task. Some tasks might require good performance but low accuracy, and vice versa. You need to analyze and determine which approach is best for solving your business problem.

Balancing Algorithm and Business Prioritize based on impact. Align algorithm improvements with key business metrics. Communicate trade-offs clearly, explaining how performance gains can impact other areas. Involve stakeholders in decision-making to ensure alignment. Use A/B testing to validate changes and measure real-world impact. Iterative approach allows for adjustments based on results. Focus on measurable improvements, demonstrating value to the business.

Before diving into code, evaluate the business's needs from its algorithms by engaging with stakeholders to understand their priorities. For example, marketing might want more accurate customer predictions, while operations may prioritize speed. Mapping out these needs helps identify which algorithmic improvements will deliver the most value. This initial assessment ensures your technical work is both impressive and meaningful to the business.

To improve algorithmic performance amid conflicting priorities, align efforts with business goals, assess impacts, and prioritize accordingly. Communicate openly with stakeholders, involve them in decisions, and maintain flexibility. Regularly reassess and adjust as business needs evolve.

No matter what kind of work you are doing, it is always important to prioritize your tasks and start from the top of the list. Every day, not all tasks are equally important, and sometimes we waste time on tasks with lower priority. To tackle this situation, I create a task list on a sticky note. You can also do this in any text editor or on your mobile device. Note down all the tasks and assign priorities with numbers, such as 1 for the highest priority, then 2, and so on. This way, you will not only save time but also organize your tasks efficiently. ???

With a clear understanding of business needs, prioritize tasks by evaluating the potential impact and feasibility of algorithmic improvements. While quick fixes offer immediate results, addressing complex issues can yield significant long-term benefits. Effective prioritization balances quick wins with strategic investments in future performance, ensuring sustained value and growth.

I use the Corporate Statement of Intent or other business statements and then make those goals the priority for the algorithm. I would then prioritise the greatest return to the company in terms of financial return, if that is direct or with ongoing savings and or merging other tasks.

Optimizing code is often the first technical step in improving algorithmic performance. This involves refining the algorithm's logic for efficiency while maintaining effectiveness, such as streamlining data processing or using advanced data structures. The goal is to enhance performance in alignment with business priorities, ensuring that optimizations contribute to overarching objectives.

In this fifth phase of SDLC, the testing is done to ensure that the entire application works according to the customer requirements. After testing, the QA and testing team might find some bugs or defects and communicate the same with the developers.