Analyst’s corner digest #22
Top stories published in April-May 2024
Hi there!
It’s mid-year and a time for another issue of the Analyst’s corner digest.
In this issue we’ve got the articles about:
- Business Analysis Techniques: requirements are not created for the sake of requirements. They exist to unlock the progress and enable the next steps of the delivery. Explore how the requirements can be prioritised, used and managed.
- Business Agility: read about improving digital products and delivering real value with the use of MVPs, applying the concepts of Nimble organisations, and more.
- Data and AI: how AI will change marketing? how can it enhance customer care? what is the architecture of generative search? These questions are answered in our articles. And finally learn a thing or two about data frames.
- Special treat: an index of more than 120 articles by Karl Wiegers, all in one place.
Enjoy the reading!
— yours, Igor
https://www.analystscorner.org/
BA Techniques
So You Have Some Requirements. Now What?
by Karl Wiegers
Experienced software developers understand the importance of using requirements to create realistic plans, robust designs, reliable code, and revealing tests. These steps are necessary whether your next release represents 1 percent or 100 percent of the final product. There’s also a link between the software requirements and other project and transition requirements.
First Things First: Prioritizing Requirements (or anything else)
by Karl Wiegers
A problem with software product requirements is that there are always more than the team can fit into the box bounded by time, budget, resource, and quality limits. Even if you could implement all the requested functionality eventually, you can’t do it all at once. To deliver the maximum business value in the shortest time, each team must decide which product capabilities to build first.
Searching for Stakeholders: A Vital Key to Success
by Karl Wiegers
Consultant and author Tim Lister defines project success as “meeting the set of all requirements and constraints held as expectations by key stakeholders.” Every team, whether building a software solution or some other product, must identify its stakeholders and engage with them to understand those requirements and constraints, as well as their priorities and other concerns.
Requirements & API: Analysis
by Ilya Zakharau
With this chapter, we first will understand why we need requirements for API and their place in the requirements classification frameworks. Then, we review the requirements engineering process and specific aspects you need to consider
Tame your Backlog Beast — Backlog Management
by Lalita Lalwani
Backlog management is the process of documenting, tracking, and prioritizing the remaining work items in a project or a product development. A backlog is a list of tasks that need to be done to deliver value to the stakeholders.
How Do You Know If Your Requirements Are Good Enough?
by Karl Wiegers
A software team is never going to get a perfect set of requirements. Some could be incomplete, incorrect, unnecessary, infeasible, ambiguous, or missing entirely. Requirements sometimes conflict with each other. Yet the team still needs to build a product based on the available requirements information.
Practically speaking, the goal of the business analyst or product owner is to produce requirements that are good enough to allow the next development stage to proceed.
The Overarching Goal of Requirements Development
by Karl Wiegers
Software development is partly about computing and partly about communication. Requirements development, though, is entirely about communication. In general, we’re better at the technical side of software development than the human side. Those team members who lead requirements activities — I’ll call them business analysts (BAs), regardless of their job title — sit at the hub of a project communication network (Figure 1). They coordinate the exchange of requirements knowledge among all project participants.
Business Agility and Customer Centricity
How Nimble organizations navigate through complexity using The Flow System and the Agile Analysis Horizons
by Nuno Santos
In this article, I continue to dive into the concept of Nimble organizations. Inspired by the webinar series about Nimble from The Brazilian BA, (which you can find here), I started diving into the concept as well. I had my first take discussing the importance of experimentation and pivoting in organizations with a Nimble capability (which you can find here). Being Nimble requires tools for both sensing and responding, which is in fact how The Flow System™ addresses complexity. In this article, I introduce The Flow System within nimbleness. Also, we can combine it with the 3 Agile Analysis Horizons from the International Institute of Business Analysis (IIBA®).
Using these Methods can Help You Improve Your Digital Product as a Business Analyst
by Tharindu Senanayake
Living in the digital age, you have surely come across digital products that feel so natural to use and some harder to use even with manuals and tutorials available which made you pull your hair out trying to get something done using them. It all comes down to User experience (UX) and User Interface (UI). As business analysts, we play a big role in shaping the components of creating a better experience with digital products. You can use advanced methods to improve UI and UX to create a better experience for the customer. This will be the key to the success of the product.
The analysis to do in product discovery of MVPs
by Nuno Santos
To do a proper product discovery is finding the most valuable way a product can provide. Business analysis is powerful for discovering what an organization values most. There are different approaches that you can use in Discovery, depending on what you want to experiment. I already explored the power of proof of concepts (PoC) in another article. Now, I dive into another powerful (and commonly known) approach for Discovery: Minimum Viable Product (MVP) — but also what’s not MVPs.
The analysis behind small experiments in product discovery
by Nuno Santos
More often there will be experimentation, change and the need for flexibility. Frameworks like Dual Track Agile from Marty Cagan’s book “Inspired” influenced organizations to split their efforts into product discovery and delivery. The Discovery track is all about quickly generating validated product backlog items, and the Delivery track is all about generating releasable software. One can also say that, in the Discovery track the goal is to “build the right solution”, to discover the product to build. Whereas in the Delivery track the goal is to “build the solution right”, to deliver that product to the market in the best way of working that team can.
The Red Herring: Delivering Value or Distraction?
by Pragati Sinha
Picture this: You’re in the final stages of a project. The finish line is in sight. Suddenly, an intriguing new feature request appears. It seems like a quick thing that needs minimal effort for maximum results, and before you realize it, you’ve been drawn off course.
Such deceptive distractions, aka Red Herrings, lurk within every project. They may appear alluring, but they have the potential to misdirect teams. The sly reference “red herring” originates from a hunting practice from the 18th to 19th century. It’s believed that the early animal rights activists used the strong odor of cured red herrings to distract hunting dogs, giving the prey time to escape. In the same way, metaphorically speaking, red herrings can steer your project team off the track of the ultimate goal.
Data and AI
How Artificial Intelligence Will Change the Future of Marketing
by Digital Medium
Have you ever felt overwhelmed by the sheer amount of data bombarding you as a marketer? Website analytics, social media insights, customer relationship management (CRM) systems — the list goes on. Extracting meaningful insights from this data deluge can feel like trying to find a needle in a haystack. But fear not, weary marketer, because artificial intelligence (AI) is poised to revolutionize the way you approach your craft. By harnessing the power of AI, you can unlock hyper-personalization, make smarter decisions, and ultimately achieve marketing nirvana.
The Winning Architecture of Search Generative Experience
by Ole Olesen-Bagneux
On May 10th Elizabeth Reid VP of Search, announced Google’s Search Generative Experience (SGE). This is Google’s vision of deep incorporation of Generative AI (GenAI) into their search engine for the web. There is reason to believe that Google has defined the winning architecture for the incorporation of GenAI in search, and the reason is surprisingly simple — I explain it below.
Data Architectures Are Choices, not Ideals
by Ole Olesen-Bagneux
In Deciphering Data Architectures, James Serra delivers a needed and rare perspective on data and technology that is intellectually far beyond the flashy ideas of the moment. Historians have a dedicated term for the study of such a stretched timespan, exceeding more than the important events in a given era. It’s the french notion of la longue durée, the long term in English — the study of long term structural change, as transformations occur and alter the reality, slowly, as actions and reactions push each other again and again.
Mastering Data Frames and Indexes in Python and R: A Comprehensive Guide for Data Analysts
by Nilimesh Halder, PhD
This comprehensive guide aims to provide data analysts, scientists, and enthusiasts with a deep understanding of data frames and indexes in Python and R, furnishing them with the skills necessary to manipulate, analyze, and leverage data efficiently in their projects.
Harnessing Generative AI for Enhanced Customer Care Agent Coaching
by Arindam Sen
In the rapidly evolving domain of customer service, the pressure on customer care agents to deliver exceptional service is higher than ever. However, the traditional methods of coaching agents are often fraught with challenges such as inconsistency, lack of personalization, and scalability issues. Enter Generative AI (GenAI), a transformative technology that promises to revolutionize how managers coach and develop their teams. Let’s explore how GenAI can be leveraged to enhance the effectiveness of customer care agent coaching, ultimately leading to improved customer satisfaction and agent performance.
Unlocking Economic Insights: Mastering Analysis of Variance (ANOVA)
by Nilimesh Halder, PhD
This article aims to provide a comprehensive overview of the application of Analysis of Variance (ANOVA) in the field of economics. By integrating theoretical concepts with practical examples and code in Python and R, the article seeks to equip readers with the knowledge and skills necessary to leverage ANOVA in analyzing economic data. The inclusion of use cases and advanced topics ensures that the content is relevant and valuable to both students and professionals engaged in economic research and analysis.
Special treat — A Compilation of Articles by Karl Wiegers
Here’s an index with more than 120 of articles on business analysis, product design, project management, quality, writing, and more.
