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Unveiling the Unsaid: The Power of Subtle Stakeholder Cues

When eliciting information from stakeholders, often what isn’t said can be as significant as what is said. Of course, the information that a stakeholder explicitly mentions is of crucial importance, but often there are subtle and nuanced details that aren’t obvious unless you look for them. Perhaps a stakeholder subtly pauses and looks uncomfortable and avoids answering a specific part of a question. Or perhaps they use a qualifying word like “usually” or “sometimes”. Either way, it is crucial to probe further and find out more.

 

Hearing What You Expect To Hear

Looking out for these clues is important, but is easier said than done, particularly when time is tight. When there are a number of stakeholders to speak to, it is easy to get drawn into a pattern of hearing what you expect to hear. We’ve probably all experienced this: after three people from different departments outline a process consistently, speaking to the fourth and fifth person seems like ticking a box. After all, they are going to just confirm what the first three people have described, surely?

That might be the case, but equally they might have additional insights that the original three did not. There is presumably a reason that they have been selected to participate in the elicitation activities, perhaps because they have a different perspective on things. Yet, it would be easy to let those discussions be swayed by the conversations that have happened before. To almost go on ‘autopilot’ and lead the stakeholder in a particular direction. It is worth being especially aware of those subtle cues and nuances in situations like this.

An Example

Imagine interviewing a stakeholder in a finance team about the invoice payment process. You’ve spoken to other stakeholders previously and you’re pretty sure you know the process:

“So, you get an invoice in, and as long as there’s a purchase order number on there, and as long as it’s approved, it gets scheduled for payment, is that right?”

“So, yes, mainly…. Yeah, mostly that’s it.”

 

It’d be easy to move on from this. It would be easy to assume that they are confirming some of the key decision rules (if an invoice has a purchase order number and has been approved, it gets scheduled).  But the stakeholder has added qualifying words: mainly and mostly.  These are easy to miss, but definitely require probing.

 

Probing might go like this:

“I noticed you said that this is mainly how the process works, are there other circumstances?”

“Yes, only very occasionally though. Sometimes, we get ‘proforma’ invoices that have to be paid immediately. We have a different process for those. Also, there are some cases where we pay up front by credit card. For example, booking training and conference places. That has a slightly different process too.”

 

Suddenly, a whole new set of facts becomes available. If this were a real situation, this would lead to further probing (e.g. “Are there any other times when the process operates differently?”, “Is there just one corporate credit card, and if so who is responsible for it” etc, etc).

 

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Make Sure They Feel Heard

Of course, probing this way is important to ensure that nothing crucial is missed. However, genuinely listening is important for another reason too: to ensure that people actually feel heard.

If you’ve ever had the experience of speaking to someone who appears to be preoccupied, and is perhaps presuming what your responses are, you’ll know that this rarely feels great. As analysts, it’s important that we empathize with and genuinely hear what people are saying.

Listening in this way will help uncover important information. It is a crucial and often taken for granted skill, but one that we can probably all hone and improve upon!

How AI will Affect Business Analysis in 2024

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.

 

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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.

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.