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Tag: Business Analysis

Web 3.0: The Future of Process Catalogue Management?

Web 3.0 technology, in my view, can be used for new innovations and has the ability to deliver positive change quicker. Specifically, Blockchain technology could allow for a transparent, automatic and secure way to manage a business’ process catalogue.

Traditionally, when analysing processes things like Upper/Lower Specified Limits, Service Level Agreements, and Defects Per Million Opportunities are used to understand whether a process is performing satisfactorily. This requires a BA to take measurements, validate them and then work with the business to pivot the process back to delivering the agreed standard. The typical business trigger event for this is either automatic or internal– it requires a BA to pick up during routine quality testing, or an actor to notice and raise through an agreed mechanism. This is because the process infrastructure is basically storage; it could be coined as “static management”. This means things can be missed, as humans make mistakes and the data does not work for the business, rather the business works for the data.

There have been recent advancements in technology, namely Web 3.0, which can reduce or potentially eliminate the human error element and turn the process catalogue into a dynamic storage, in which the data works for the business. In particular, Blockchain technology offers several features that could transform the way we work.

A Blockchain has several features, such as: Nodes, Ledger, and Wallets. Nodes are users/devices that hold the ledger, in full or in part. The Ledger is the record of transactions that happen across the blockchain and wallets are areas, in crypto blockchains, where the cryptocurrencies are stored.

 

At a first glance, this ecosystem seems locked to currencies, I believe it can be adapted to handle processes. Each process would need to be broken down into its steps and identified by its inputs/outputs and business actors. This dataset is then integrated into a blockchain – with each block containing the data from a single process step. In terms of a traditional process map, the block is the process step and the transaction is the connector lines between two process steps. In process terms it would be Step, Connector, Step; in blockchain terms it would be Block, Transaction, Block.

When the process is run, new unique blocks are added to the chain with the details of that unique process step run, which are then linked to further blocks/steps via transactions, providing a completely transparent and auditable record.

 

This setup has an infrastructure advantage because a blockchain validates transactions through decentralisation, using other blocks already in the chain. It means process rules are embedded in a chain from existing blocks and are then used to validate new blocks, resulting in a guaranteed uniformed process run, as the blockchain would only validate the transactions in accordance with the blocks already there.

The blockchain allows for easy performance monitoring, as each block is recorded with management information as well as process information and this is all in one place, it is easy for an analyst to calculate run times, business actor performance on individual or multiple transactions and process efficiencies.

Once an improvement is identified, the process is updated and released onto the blockchain, then becoming the single-source-of-truth for transaction validation, therefore only allowing the most up-to-date process to be followed by business actors. In this sense, the blockchain is both the governing authority as well as storage for processes.

 

The problem with this is that it is still reliant on humans picking up on the fact that a process is not performing, so whilst we have an enforceable process level to six sigma, we do not have the benefit of removing the human error or time lag associated with a drop in process performance.

This can be resolved using a feature of a blockchain called a smart contract. Smart contracts are automated digital contracts which trigger when the terms and conditions of that contract are met. There is an equivalent document in the business world, which sets out an agreement between two parties to perform in a particular way or to a particular standard under particular terms – a Service Level Agreement (SLA).

The smart contract is the Web 3.0 equivalent to the SLA. However, a smart contract offers much more than just an agreement, it self-executes which means as soon as the terms are met, action is taken with virtually no time lag.

 

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The smart contract is created using an if/when then statement. An example smart contract can be if a customer makes an enquiry and no one contacts the customer in 3 working days, then an escalation notice is sent to the assigned persons manager. As this is automated, as soon as the condition is met, the contract is acted upon – meaning management do not have to spend time reviewing whether the conditions within SLAs, making both service and personal performance management easier.

There are, however, some issues with blockchains which need further consideration to overcome: a large number of transactions can cause lag on the chain, due to the required effort to process them all, meaning slower transaction times. It may mean that this model is best suited to small startups/businesses. Blockchain technology is still new, and therefore is not thoroughly regulated yet, meaning it can be difficult to fit in with current governance structures. This can be tackled by robust risk management and future legislation or policies, meaning this model may be suited to an innovator type business.

 

In summary, Web 3.0 Blockchains can offer improvements to the operation, governance and management of processes. By leveraging features of blockchains, it’s possible to move from a static process catalogue to a dynamic, automatic and smart infrastructure which reacts quicker to changes in business environments, freeing up staff to find other efficiencies or grow the business in other ways. While there are concerns and issues around things like scalability and regulations, it is clear that Web 3.0 technologies can offer new and exciting opportunities.

“BREAKING THE FRAME”: A Paradigm Shift in Problem-Solving

In the realm of business analysis, problem-solving isn’t just a task; it’s a craft. We’re constantly challenged to find solutions to complex issues that impact our organizations’ success. Let us explore a transformative concept in problem-solving: “Breaking the Frame”!

At its core, breaking the frame is about challenging the status quo and approaching problems from a fresh perspective. It’s about stepping outside the boundaries of conventional thinking to uncover hidden opportunities and drive meaningful change.

Consider the “Slow Elevator Story.” Tenants in a building complained about the sluggishness of the elevator, prompting the manager to seek solutions. Traditional problem-solving methods would have led to expensive elevator upgrades. However, by thinking outside the box, a simple yet effective solution was found: installing a mirror in the elevator. This small change altered the perception of time, reducing complaints without the need for costly renovations.

 

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So, how can we apply this concept of breaking the frame to our own problem-solving endeavours?

  1. Reframing the problem: Instead of accepting the problem as presented, dig deeper to uncover its root causes and underlying assumptions.
  2. Diverse Perspectives: Embrace diverse viewpoints and collaborate with colleagues from various backgrounds to gain fresh insights into the problem.
  3. Creative Solutions: Be open to unconventional ideas and approaches that may lead to innovative solutions beyond traditional boundaries.
  4. Holistic Analysis: Consider the broader context surrounding the problem, including external factors, stakeholders’ perspectives, and long-term implications.
  5. Iterative Approach: Adopt an iterative problem-solving approach, where solutions are continuously refined based on feedback and new insights.
  6. Experimentation: Embrace a culture of experimentation, where hypotheses are tested, and failures are viewed as learning opportunities.
  7. Data-Driven Decision Making: Utilize data and analytics to inform problem-solving, ensuring decisions are grounded in evidence and insights.
  8. User-Centric Design: Place the end-user at the centre of problem-solving efforts, empathizing with their needs and preferences.

 

The elevator may or may not be slow, but the point here is “Is there a better or smarter way to solve the problem?” . By reframing our approach to problem-solving, we can uncover hidden opportunities and propel our organizations forward.

In the realm of business analysis, breaking the frame isn’t just about solving problems; it’s about driving innovation and creating value. By reframing our approach to problem-solving, we can uncover hidden opportunities and propel our organizations forward.

 

“If I had a hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 mins thinking about solutions” – Albert Einstein

Therefore, let’s never simply acknowledge the problem as it’s presented. Instead, let’s break free from conventional thinking, explore beyond the established boundaries, and rephrase the given problem to uncover its underlying root causes. By doing so, we can avoid solving the wrong problems and focus on addressing the correct ones.

The key to effective problem-solving lies in embracing creativity, diversity, and a willingness to challenge the norm. Let’s embark on this journey of breaking the frame and revolutionize our approach to problem-solving in business analysis.

The Mind as the Canvas

In the ever-evolving world of business analysis, the ability to convey complex data insights and concepts is paramount. For many, Visualization is a fundamental tool, often associated with software applications such as Power BI, Tableau, or Excel. In these tools an image containing all data points is generated for visual consumption and interpretation.

However, for Business Analysts who are certified with Sight Loss, this traditional approach of transcribing an externally generated image visualization into the mind can present a barrier to conducting their duties. In order to overcome these barriers it is essential to embrace non-visual representation, not only to ensure the Business Analyst with Sight Loss can complete their job, but by doing so it also develops and encourages many other benefits for the entire business.

 

Using a traditional Visualisation method, namely consuming and transcribing an external image into the consumers mind for analysis and interpretation, presents significant challenges for those who cannot access the external image in the first instance. Visualisation is an internal process and we use external stimuli to reconstruct this in our minds. These mental images can be real or imaginary, for example if I ask you to think of a pink elephant, you can do so, despite it not existing. The objective of having a pre-generated image to transcribe is one of time saving through consistency. By having technology that converts non-visual data into a visual image saves the user from having to do this themselves. Further, it also ensures that every consumer of the image has the same input and is therefore the internal process goes from reconstruction to transcription.

 

Think back to the pink elephant, if two people had to imagine it and compare, there would be differences in the size of the elephant, the ears, the hue of pink, and many other variables. Any question raised by the variability can be removed when transcribing, because you do not have to think about the construction of the image just the result of the image.

 

It is therefore logical to conclude that the essence of visualisation lies in cognitive processing and data communication methods. The communication method traditionally gives a visual representation before entering the mind, which is usually accepted by the brain as fact. There can be no more clearer way to draw out the problems of this than the recent phenomenon of the Changing Dress, which appears either Blue and Black or White and Gold to different people. Both versions are subjectively true. We accept pre-generated images to be true because of various reasons from the size of the dataset the image has been generated from, to the relationship between stakeholders, to the attitude and aptitude of the Analyst.

 

The concept of non-visual data representation is a crucial avenue for enabling not only Analysts with Sight Loss to excel in their roles, but to ensure that the risk of incorrect data insight is minimised.

 

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There are several benefits to not relying solely on a pre-generated image. Firstly, enhanced data comprehension, namely non-visual data consumption that relies on auditory, tactile, and textual methods to convey the knowledge of data as data points, not visual graphics. Utilizing alternative communication methods can allow access and interpretation of complex datasets in a new and engaging way. This approach allows for a deeper understanding of the data, in the same way that reading a book cover-to-cover (i.e. the dataset), instead of just the blurb (i.e. a pre-generated external image) gives a fuller understanding of the content.

 

Secondly, when presenting findings to stakeholders, it can be beneficial for them to understand it not in a mental-visual aspect but as data points, facts, and relationships. This includes verbal descriptions, accessible documents, audio tracks and storytelling (as opposed to story boarding). By doing so, analysts can articulate their insights clearly and persuasively without traditional visual aids or statistical jargon. It can also enable the stakeholders to engage more effectively with the data and can apply their own domain knowledge, further helping the project being undertaken.

 

Thirdly, for those BAs with sight loss, the advancement in technology means that they can process data more effectively with Screen Reader software and tactile graphics, building a graph in the mind. Much in the same way that following instructions on Google Maps and actually walking the route, are two very different experiences. These tools can provide real-time feedback and enable analysts to explore data, scenarios, and outliers effectively, all while maintaining the focus on the data itself, instead of interpretations of data.

 

Fourthly, a further benefit of non-visual communication is increased collaboration and teamwork.  Non-visual communication allows analysts to work seamlessly with both sighted and colleagues with sight loss, to share their findings, develop requirements, and craft compelling data narratives, centred on the concept or data’s intrinsic qualities.

 

Further to these benefits, non-visual communication can encourages innovative problem-solving techniques, because it does not funnel people into thinking visually, it does not bias them towards any particulars, by predisposing them to the stimuli of a pre-generated image. Analysts with Sight loss can apply their unique perspectives to explore different approaches and scenarios, contributing valuable insights to the analysis process without relying on visual cues.

 

In conclusion, within the realm of business analysis, non-visual processing is crucial for individuals who have sight loss to equally participate, but it can also present business-wide benefits. Embracing non-visual approaches empowers all staff members of an organisation to excel in their roles, offering enhanced data comprehension, alternative communication, and adaptive problem-solving techniques that focus on the data itself, not a pre-defined notion. As we strive for inclusivity and diversity in the workforce, it is essential that the business analysis field acknowledges the value of non-visual processing and provides the necessary support and resources for Analysts with Sight Loss to thrive.

 

In doing so, we ensure that all individuals, regardless of their Sight capability, have an equal opportunity to contribute their skills and insights to this dynamic field, with the primary focus on processing as the valuable core of their analysis.

Demystifying MVPs, Prototypes and Others in the BA Landscape

Let’s get some confusion out of the way. There are many different concepts and related acronyms that aren’t always used in the best way. Let’s try to clarify them.

Terms like MVP (Minimum Viable Product), MMF (Minimum Marketable Feature), MLP (Minimum Lovable Product), prototype, and proof of concept are all concepts used in product development, but they serve different purposes and have distinct characteristics. Here’s a breakdown of the differences between them:

  1. Prototype:
  • Purpose: A prototype is a preliminary version of a product used for design, testing, and demonstration purposes.
  • Focus: It primarily focuses on illustrating the product’s design, user interface, and user interactions.
  • Development Stage: Prototypes are created early in the product development process to visualize ideas and concepts before full-scale development begins.

 

      2. Proof of Concept (PoC):

  • Purpose: A proof of concept is a small-scale project or experiment designed to verify the feasibility of a particular technology, concept, or approach.
  • Focus: It concentrates on demonstrating that a specific idea or technology can work in a real-world scenario.
  • Development Stage: PoCs are often done at the very beginning of a project to assess technical feasibility.

     

     3. Minimum Viable Product (MVP):

  • Purpose: An MVP is the most basic version of a product that contains just enough delivery work to satisfy early customers and gather feedback for further development.
  • Focus: It focuses on delivering core functionality to test the product’s intended value to the customers.
  • Development Stage: MVP comes after the idea and concept but before extensive development.
  • Goal: The primary goal is to validate assumptions and learn from user feedback with minimal development effort.

 

     4. Minimum Marketable Feature (MMF):

  • Purpose: MMF is a subset of features within a product that is sufficient to make it marketable to a specific target audience.
  • Focus: It concentrates on delivering features that are essential to meet the needs of early adopters and generate sales or user adoption.
  • Development Stage: MMF typically follows the MVP phase, where you refine the product based on initial feedback and prioritize features for marketability.

 

     5. Minimum Lovable Product (MLP):

  • Purpose: MLP aims to create a version of the product that not only satisfies basic needs but also elicits an emotional response from users.
  • Focus: It goes beyond functionality to provide a delightful user experience and build strong user loyalty.
  • Development Stage: MLP often follows the MVP and MMF stages and is focused on making the product more appealing and engaging.

 

In summary, while MVP, MMF, and MLP are related to the development and release of a product, each serves a different purpose in terms of features, user experience, and market readiness. Prototypes and proof of concepts, on the other hand, are more focused on testing and validating ideas and technologies before committing to full development. In IIBA’s Guide to Product Ownership Analysis (POA®), the concepts of MVP, MMF, and Minimal Marketable Release (MMR) and Minimal Marketable Product (MMP) are introduced in terms of decision-making on what to build. The figure below is from the POA Guide and orders these four concepts. To know more about MVP with PoC and prototyping, check out a great article (with a video interview) with Fabricio Laguna (“The Brazilian BA”) and Ryland Leyton here.

Fig. 2: MVP, MMF, MMP, and MMR in the POA Guide

 

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Business analysis Behind Proof of Concept (PoC)

We may use a PoC when the goal is to make a very small experiment around a business idea, from which we need to assess its feasibility.

A well-known way to explore ideas in an early phase of design is by conducting Design Sprints.

Business analysis work within this process is of extreme importance. First of all, a BA professional may use their facilitation skills to facilitate the entire workshop. When framing the BA scope to the ideation process, their work starts when applying the “How Might We” technique. If you want to know more about the “How Might We” technique, you can check it out here.

 

After ideating a range of “How Might We?’s, the BA work includes facilitating the following workshops, from which the ideas are refined until a (possibly very bad) prototype is built and tested with users. BAs are usually comfortable with conducting the tests. At the end, they gather the insights from the tests and assess the PoC.

 

Business Analysis Behind MVP

When planning to build an MVP, don’t forget you’re targeting and validating if a business idea, through a product, has value for customers that you believe it has. However, before investing in a solid product, the mindset is that you’re making the least effort possible to have something that technically works.

This means that the work around framing a problem and further elicitation, analysis, modelling, refinements, etc. relies on hypotheses to be validated rather than fixed requirements, allowing for greater flexibility and adaptation to changing customer needs.

 

Business Analysis If You’re Not Targeting an MVP

Sometimes, when building an MVP, it is planned in a way that has a minimum set of features that can be delivered to customers. As already discussed, that’s not an MVP but an MMF instead, so the mindset for building it focuses more on scope modelling to decide which features have to be included.

Also, if the mindset focuses more on delighting customers (sometimes disregarding the business value delivered), that’s not an MVP but an MLP instead. The BA work focuses more on user research, interviews, and partnering (if existing) with UX/UI professionals. Personas and empathy maps are commonly used techniques.

 

After that, you may define your strategy to test your assumptions. There are different techniques, which depend on the testing context. And such contexts have different approaches to use.

As a BA, we can support decision-making about the technique to choose. But also to help in setting up those tests.

 

What Apple’s Vision Pro Tells Us about User Stories

The Verge released a story recently reporting how early buyers of Apple’s Vision Pro “spatial reality” headset were already returning their devices to take advantage of Apple’s 14-day return policy window.

But why?

In this article, I’ll recap the issues sprouting up around this nonetheless revolutionary product in order to make a couple of arguments: 1) How Apple’s approach to hardware development may (to a fault) prioritize perceived quality over functional requirements, and 2) What user stories for a hardware/software product may necessitate to make future generations more viable for widespread adoption.

 

Problems Abound in VR, but Did Apple Put Form before Function?

The key issues cited for returns of the Apple Vision Pro are usability issues coupled with a hefty price tag (i.e., $3,500 MSRP).

That’s not to say buyers aren’t blown away by the revolutionary UI and what the device is capable of. Rather, users’ concerns are that—relative to the high price tag and usability issues—they simply can’t justify the expense for a device that presents these usability concerns. The price isn’t worth the experience, in other words.

Consider some of the tweets on X (formerly Twitter) from users describing their experiences with the device and ultimately their reasons for returning their headsets to Apple:

“Can’t wait to return the Vision Pro, probably the most mind blowing piece of tech I’ve ever tried. Can’t deal with these headaches after 10 minutes of use though,” tweets one user.

“Two hours after unboxing my Apple Vision Pro and using it, I decided to box it back up again and return it. It’s quite cool, but there’s nothing in it for me that I’ll use frequently enough to warrant my keeping it,” tweets another.

Virtual reality headsets are a complicated product category and represent an exceedingly difficult problem to solve considering the technical and physical challenges. I’ve reported on how, for example, ergonomics are a known issue.

Consider the problems a VR headset must address:

  • Weight – Obviously, a perfect solution doesn’t yet exist, but as some reviewers have reported, Meta’s redistribution of weight and use of pancake lenses in place of Fresnel lenses in their Meta Quest Pro represent an attempt to resolve UX problems their earlier Quest 2 presented. Considering that VR headsets aren’t a new category, reviewers may have liked to see a better first showing from Apple regarding the ergonomics issues related to weight distribution.
  • Price – With the $3,500 price tag (compared to $999.99 for Meta’s Quest Pro), price is an issue. Certainly, higher grade materials which play important parts of Apple’s industrial design philosophy and sustainability goals contribute to the heavier form factor compared to other headsets that rely on plastics. That said, alternative materials such as recycled plastics represent another way to reduce costs (e.g., potentially by 25-50%) while simultaneously addressing the weight issue.

 

User Stories and Understanding Evolving Needs

If you’ve seen the 2023 film, Blackberry, about how the once-dominant smartphone predating the iPhone (and later competitive offerings from Samsung), you know that the one thing the titular product from Research in Motion (the company that invented the product category) is that getting there first doesn’t mean staying there indefinitely.

The case of Blockbuster versus Netflix tells a similar story, where a giant who’s become the dominant force in the marketplace is complacent, slow to innovate (due to their complacency), and is disrupted.

In the case of Apple, they weren’t there first in the VR category. They also weren’t the first to the smartphone category, but in the case of the smartphone, they completely redefined the category.

Have they innovated enough while addressing known user problems in the category?

Certainly, Apple has created a revolutionary product, but as Mark Zuckerberg points out, the device doesn’t provide an experience so leaps and bounds ahead of its competitors that it warrants the price and the persistent UX problems.

In short: The Apple Vision Pro isn’t to the AR/VR product category what the iPhone was to the smartphone category.

 

 

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What User Stories May Have Detailed: Ergonomics at the Center of Industrial Design to Solve Known User Problems

Chief among the user problems with the Apple Vision Pro is the ergonomics problem.

Considering the Verge report of customers returning their Vision Pro headsets with complaints of discomfort relative to the weightiness of the device for extended periods, it’s safe to say Apple hasn’t cracked the ergonomics problem on their first try.

But it’s also safe to say it’s a problem no manufacturer has truly solved, but Apple’s form factor doesn’t help. Consider, for example, how weight has been a known problem in VR applications studied by scholars.

Future iterations of these devices should seek to address the known ergonomics problems users are experiencing.

 

Example of Ergonomics-First Industrial Design

To stray away from VR headsets for a moment to talk just to ergonomics and how to approach solving real-world ergonomics problems, let me offer an example.

Heavy-duty power tools provide one real-world application where ergonomics are of heightened concern—we’re dealing with workers’ livelihoods and safety in situations that are inherently dangerous, after all. Characteristics of ergonomic power tools typically look to address a combination of weight, shape, and grip to provide a form factor that is as-comfortable-as-possible relative to the application.

Consider some of the common causes of musculoskeletal disorders like trigger finger (e.g., overexertion and repetitive motion) from a person’s finger becoming locked in a bent position as the result of repeatedly gripping and pulling the trigger of a crimper, for example.

Equipped with this known issue, the M18™ FORCE LOGIC™ 12 Ton Utility Crimper introduced a high-capacity muscle testing system to design the tool to require less than eight pounds of trigger release, which is 75% less than other crimpers, while also delivering an improved center of gravity and a significant weight reduction to the tool. What’s more, it requires 47% less muscle effort to use.

The example from Meta’s Quest Pro of redistributing the batteries to address the balance issue in earlier iterations is one that shows promise and Apple may take notice when addressing their own device’s weight problems.

 

Bottom Line

We may not have cracked the ergonomics problem associated with VR applications, but Apple may look at existing heavy hitters in the category, like Meta, as they tweak their own device’s shortfalls.

Outside of consumer applications, AR and VR offer exciting prospects for productivity enhancements in industries that could stand to gain in productivity like AEC: Studies have looked at the use of VR in safety training (e.g., articles have been published in Applied Sciences, additional research has been produced by California Polytechnic State University, and conference talks have been given on the subject).

If Apple can address the ergonomics and cost issues by prioritizing user needs, their Vision Pro headset may be the construction wearable of choice companies use to onboard new employees, train apprentices, and conduct safety demonstrations in the application to provide greater educational outcomes for the next generation of construction professionals.