Tag: Techniques

How AI will Affect Business Analysis in 2024

Written by Shaz Shafiq on April 4, 2024. Posted in Articles.

Artificial intelligence (AI) has been making waves in the field of technology, and experts firmly believe that it will continue to grow in 2024. AI has become an integral part of our daily lives, manifesting through voice assistants for customer service, chatbots, market trend prediction, proactive identification of potential health issues, and other such things. In recent times, businesses have increasingly embraced AI to enhance their efficiency and secure a competitive advantage. That is why the importance of artificial intelligence is being taught in business analyst courses.

AI is transforming the business landscape, playing a pivotal role in boosting growth and operational efficiency. It has evolved into a formidable ally, assisting businesses to get data-driven insights, optimize processes, and enhance decision-making capabilities. Once a blurry science fiction vision, AI has become a necessity for modern businesses. Its journey from just a theoretical groundwork in the mid-20th century to corporate boardrooms in 2024 has been astonishing. But how will it affect business analysis and the jobs of professionals in the future?

Well it is reported by Goldman Sachs that Ai could potentially replace 300 million full time jobs world wide.

The good news is Business Analysis as a job cannot be replaced with Ai, but the job can be supplemented with Ai.

The reason for this is, business analysts help organizations effectively implement change initiatives, the biggest change since the emergence of the internet has been the use of Ai, which is set to disrupt many markets. In order for organisations to stay competitive, organisations will have to implement the changes to their processes and embed the wave of Ai, and which role in particular helps organisations implement change? Business Analysts!

Let’s now delve into AI’s role in business analysis and how things will unfold.

Enhanced Data Analysis

AI is capable of processing complex data in large volumes and at a high speed. This will lead to more accurate business insights in less time. AI-powered text analytics tools can quickly analyze unstructured data like social media comments or customer reviews, which will provide valuable insights into customer preferences and sentiment. This allows businesses to make more informed decisions, increasing productivity.

Predictive Analytics

The adoption of AI for predictive analytics is now widespread in the realm of business intelligence. Companies, both large and small, are leveraging AI models to swiftly analyze various data sets, such as sales, customer information, and marketing data. This utilization of AI empowers businesses to proactively anticipate market trends, understand customer behaviors, and identify potential risks.

While predictive analytics has been a part of business strategies for as long as data has been collected, the integration of AI has revolutionized the process. This foresight becomes instrumental in guiding strategic decisions and enhancing the overall efficiency of business analysis procedures.

Automation of Routine Tasks

AI is poised to automate routine and repetitive tasks in business analysis, thereby reducing errors, enhancing efficiency, and liberating human resources. This, in turn, enables business analysts to concentrate on more intricate and strategic facets of their work. The automated tasks encompass processes like data collection and report generation, which, when handled by AI, release valuable time for value-added analysis.

Natural Language Processing (NLP)

AI-powered NLP will allow business analysts to interact with data using natural language. It means that users with business analyst certification as well as non-technical users will find it easier to understand and access complex datasets. This will lead to collaboration between different departments within an organization.

Personalized Business Insights

AI will allow the customization of business insights based on individual user needs. Analysts can receive tailored recommendations and reports, improving the relevance and applicability of the information provided. AI facilitates a highly personalized customer experience by sifting through customer data in large volumes, including purchase history, browsing patterns, and social media behavior. This capacity for thorough analysis helps businesses recognize individual customer preferences, thus tailoring their interactions and recommendations to cater to these specific tastes and requirements.

Improved Decision Support Systems

AI-driven decision support systems are likely to have a great impact and become integral to business analysis. These systems can process large volumes of data, assess various scenarios, and recommend optimal decisions. This will provide valuable guidance to business analysts and decision-makers.

Real-time Analytics

AI will enhance business analysis through real-time analytics capabilities. By leveraging AI technologies, businesses can access the most up-to-date information, empowering them to make informed decisions promptly. The significance of this real-time capability cannot be overstated, especially in the dynamic landscape of market changes. With AI-driven real-time analytics, businesses can respond swiftly to emerging trends, evolving customer behaviors, and market fluctuations, thereby staying competitive in today’s fast-paced business environment. This proactive approach to data analysis ensures that businesses are not only informed but also well-positioned to adapt and thrive in the face of constant change.

Advanced Pattern Recognition

AI’s advanced pattern recognition capabilities will enhance the detection of subtle trends and anomalies in data. This can be especially valuable in identifying emerging opportunities or potential risks that might go unnoticed with traditional analysis methods. Whether you have completed your business analyst training or not, the AI can make your task a lot easier and accurate.

Improve Risk Management & Fraud Detection

AI algorithms are designed in such a way that they can autonomously interpret and analyze even the most complex financial data. This allows them to uncover hidden patterns and irregularities that might indicate fraudulent activity. By leveraging natural language processing, data analytics, and machine learning techniques, AI systems can process large volumes of structured and unstructured data, find outliers, and generate actionable insights quickly. This approach empowers businesses, and financial institutions in particular, to detect fraudulent activities early and implement appropriate strategies to minimize the risk.

AI for Business Analyst in 2024: A Great Tool, Not a Replacement

The notion of AI making business analysts obsolete looks unlikely when considering the unique value that these professionals bring to organizations, a dimension that artificial intelligence struggles to replicate. Business analysts play a pivotal role in eliciting, prioritizing, and refining requirements in collaboration with stakeholders—a task that involves nuanced understanding and interpersonal skills that AI currently lacks.

Furthermore, business analysts serve as a crucial bridge between the business and its IT team, ensuring seamless communication and alignment of objectives. Their ability to communicate effectively with stakeholders is not only about conveying information but also about fostering relationships and steering projects towards their goals. This human touch is indispensable in complex organizational dynamics.

The proficiency of business analysts in understanding the end-to-end processes that they learn during business analyst programs, is a multifaceted skill that AI has yet to master. This involves a deep comprehension of organizational intricacies and the ability to navigate the complexities of both the business and technology realms.

Equally significant is the creative problem-solving approach that business analysts bring to the table. While AI excels in data-driven tasks and pattern recognition, the intuitive and creative thinking required for innovative problem-solving is a distinctively human trait that currently eludes artificial intelligence.

In short, the multifaceted roles and responsibilities of business analysts, encompassing collaboration, communication, understanding of business processes, and creative problem-solving, collectively form a skill set that AI, in its current state, cannot replicate. The symbiotic relationship between AI and human professionals, where each leverages its unique strengths, is likely to persist, ensuring the continued relevance and indispensability of business analysts in the foreseeable future.

Take Away

The profound impact of AI on business analysis is evident. The integration of AI technologies is revolutionizing the industry by automating routine tasks, enabling real-time analytics, and providing customized insights. This transformative shift not only enhances efficiency but also empowers business analysts to delve into more strategic aspects of their work.

The ability of AI to adapt to market changes swiftly ensures businesses remain competitive in the ever-evolving market. The use of AI in business analysis promises not just data-driven decision-making but a major change in how organizations leverage information for growth and innovation. The journey into the future of business analysis is undeniably shaped by the capabilities AI brings to the table.

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

Written by Neethu Nair on March 21, 2024. Posted in Articles.

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.

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

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

What Apple’s Vision Pro Tells Us about User Stories

Written by Lucas Marshall on March 6, 2024. Posted in Articles.

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.

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.

What’s The Point of Peer Review?

Written by Christina Lovelock on February 15, 2024. Posted in Articles.

When time is tight and the pressure is on, it feels like ‘peer’ review is a luxury we cannot afford, but what’s the cost of this decision?

What Is Peer Review?

Sharing our analysis outputs (whether this is documents, models, presentation slides or feature tickets) with other business analysts before they are shared with any other stakeholders is the essence of peer review. BA peer reviewers should be able to share useful observations and insights about the output, whether or not they have specific business domain knowledge. If something is not clear to a fellow BA, there is a good chance it will not be understood by customers, suppliers, stakeholders and other recipients.

Why is Peer Review Valuable?

Many business analysts have a tendency towards perfectionism, and the longer we work on something without feedback, the more disappointing it is to receive any feedback, however constructive. It is much harder to accept and incorporate feedback on a polished final draft than an early rough draft. We need to share our work early in the process to be able to influence our own thinking and approach, and prevent us making significant errors or omissions.

Why is Peer Review Valuable?

Peer review is valuable from multiple perspectives.

#1 The producer

The person who created the output. We all bring assumptions to our work, whether this is a single piece of acceptance criteria, complex model or large document. The producer gets the benefits of a fresh perspective and the opportunity to catch errors and drive out assumptions. A peer review should be a way to improve quality without any worry of reputational or relationship risk. It also provides the opportunity for increased consistency across BA products and to learn lessons from other business areas.

#2 The peer reviewer

There is always something to gain from seeing how someone else works, whether they have more or less experience than us, and wherever they sit in the organisational hierarchy. So as well as making a valuable contribution to our colleague’s work we are likely to learn something through peer reviewing.

#3 Stakeholders

Any business, dev team or project stakeholders that will also be asked to review/validate/approve the deliverable will benefit greatly if a peer review has already taken place. Some errors and ambiguities will have been addressed, saving them time and increasing their confidence in the quality of the output.

The Review Triangle

This model reminds us that the highest number of errors should be spotted and rectified by the person creating the output, as part of a specific review phase.

Self-Review

The model emphasises the need for self-review, which is a separate activity to creating the analysis output, and involves:

Standards check (adherences to templates, branding and guidelines)
Assumptions check (provide key, glossary etc.)
Accessibility check (e.g. readability, compatibility with assistive tech, alt text for pictures)
Sense check
Error check
Spelling and grammar check.

Moving from a ‘creating’ to ‘reviewing’ mindset can be achieved by taking a break, and switching to reviewing when we return, or moving to a different device as this often forces us to look at something differently.

Peer Review

Peer review is also about adherence to standards, and is valuable even when the reviewer does not have relevant subject matter knowledge. They should be able to identify and challenge assumptions made, spot logic knots and highlight the use of acronyms and jargon. They can also share insights and observations from their own experience, such as level of detail and formats preferred by different internal audiences.

Stakeholder Review

Stakeholders should not be faced with errors that we could have easily caught through self or peer review. This does not mean we should only share perfect and complete outputs, but that we give stakeholders the best chance of spotting significant gaps and fundamental errors by removing low level distractions.

Many stakeholders find it difficult to simply ‘ignore’ spelling and other small errors. This level of error can undermine their confidence in our analysis.

Depending on the number of stakeholders and the complexity of the output, it is often better to do a group review exercise (synchronous) rather than a comments based (asynchronous) review. This is for several reasons:

Confidence everyone has actually seen what is being reviewed/validated/agreed
Prevents multiple stakeholders making the same (or conflicting) observations
Changes can be discussed and agreed.

Conclusion

The benefits of peer review, to individual BAs, the internal BA community and to our stakeholders and customers is significant. Attempting to ‘save time’ by avoiding this activity is a false economy. Organisations that aspire to be a truly ‘learning organisation’ encourage and enable effective peer reviews. Where organisations don’t place emphasis on this, BAs can choose to role model this commitment to quality and learning, and lead by example.

Further reading

Delivering Business Analysis: The BA Service Handbook, D Paul & C Lovelock, 2019

Prints, Processes, and Pitfalls: More Than Just Process Design!

Written by Adrian Reed on February 14, 2024. Posted in Articles.

I was recently planning the logistics of an upcoming client workshop. I needed 12 copies of a document printed and spiral bound, and I visited the website of a printing company that we’ve used many times before for such tasks. The website had changed, and unfortunately I couldn’t complete the order. For some reason the website was saying it couldn’t deliver to my address.

I’m pretty sure I know why this is. I live in Portsmouth, on the South Coast of the UK, and to the uninitiated, some Portsmouth postal codes look similar to postal codes used on the Isle of Wight. I suspect some courier firms don’t deliver to the Isle of Wight (or charge extra as it’s an island with no roads connecting it to the mainland). This leads to some online sites (incorrectly) lumping some or all of the post codes together and tag them as an ‘exception’. This is really, really, bad design, but it definitely happens.

I was trying to place the order on a weekend, so I waited until Monday and went to contact the company by phone. I tried to phone shortly after 9, and then again at 9.30, and then again at 9.45. No reply. So, even though I’d used this company many times in the past, I just moved on to another supplier. And in fact, I’ll probably use this new supplier in the future, too. So the original printing supplier has lost a customer and it doesn’t even know that. Plus, it missed the opportunity to get feedback about the defect on their website… I wonder how many other cities/postal codes are affected? How many other sales are being routinely lost?

Considering The Customer’s Pivotal Moments In Process Design

As a business analyst, this experience made me think about process and operational design. While the example above was an example of bad design, it is impossible to design an IT system, interface or process that truly caters for every situation, nor (in most situations) would you usually want to. Sure, some call centers might have a process which defines the detailed steps to take if the President of the United States calls from a satellite phone while onboard Air Force One and asks for a message to be passed urgently to the CEO… but not many!

The point here is that there will be certain types of situations that are:

Predictable, but very unlikely and/or uniquely complicated
Difficult (or impossible) to predict, with unknown levels of likelihood or complexity
Unintended, where with the best will in the world (and lots of testing) still something unexpected has happened which has led to an unintended consequence

The first set (predictable) are deliberately not fully catered for by a process as they are either so unlikely that spending time specifying them is overkill, or they are so uniquely complicated that anything beyond broad guidelines can’t be issued. I’d imagine that large companies have a “respond to media request” process which ensures that any inquiry from a TV station or newspaper gets to the right person. The broad process will be structured, and the response will likely be logged in a consistent way. However, how the response is formulated is probably somewhat variable, and more likely subject to guidelines and principles than a strict process. Responding to a request for a photo of the CEO to accompany a “top 10 CEOs” article is likely to be somewhat different to responding to notification that a documentary will be airing showing evidence of corruption within the company!

The second set of (difficult or impossible to predict) conditions can’t be catered for as they are unknown, or the effort of trying to predict them is so great that it is prohibitive. The final set (unintended consequences) are, by their nature, unpredicted! The key here is to find them when they occur and rectify not just the individual case, but the root causes. Taking my printing example, had I got through to the first printing company, I suspect they’d have quoted me via phone and manually processed the order. Great—except the website is still faulty and swathes of other customers might be affected. Understanding what needs to change to prevent the issue happening again is key.

So, what aspects can be considered when designing customer journeys, IT systems and/or processes to cater for these types of situations?

Flexibility, Feedback and Responsiveness are Key Factors

Assuming an organization wants to handle these types of cases, it’s key to design processes with feedback mechanisms built in. Feedback should of course include opportunities for customer or user feedback, but it can also include feedback generated by the process itself.

Take the printing company example I mentioned earlier. As a nationwide printing firm, they are almost certainly finding that there’s been a minor drop in Sales (Portsmouth is a relatively big city, but probably not big enough that the drop in printing sales would ring any warning bells) and the distribution of where they are sending parcels has changed. A curious analyst diving into the data might say “hmmm, it’s odd, there are entire cities where we are no longer sending parcels… maybe we should look into that”. Making sure diagnostic data is captured and examined is important, and this is so much more than just performance data.

It’s also important to ensure there’s a viable support option and, yes, this does usually mean ensuring someone can speak to (or communicate somehow with) a human being when they need to! That support person or team needs to have sufficient autonomy and be empowered to raise issues for investigation. A team that just “raises tickets” and passes them on to others is unlikely to cut it.

Finally, it’s important to note that processes will need to change and this should be expected. Building in responsiveness to the environment is important. Expectations will change, the way people communicate will change and so forth. By designing processes with this in mind, and ensuring they are owned, reviewed and adapted when needed, is a small but important step towards agility. As BAs, we can often nudge towards this way of thinking, and every step in the right direction is a good thing!