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Mapping Success Together: Tips for Inclusive Process Maps

There are numerous things that we can do to make process maps more inclusive; however, while they tend to go against established practices, they offer a range of benefits to making maps more inclusive for all.

 

A process map should be standardized within a business to map out the steps to achieve a business goal and show sequential steps, tasks, and gateways. There are a few established standards (UML, BPMN, etc.). My aim is not to advocate methods but to encourage inclusive standardization. Consistency is key, as it enables comparison and evaluation and can also assist colleagues with neurodiversities.

Typically, there are two sight-loss personas: low vision and no vision. Low vision is when a colleague has a combination of: fields—the amount of sight you have (half-close your eyes to see top and bottom field loss). The other is acuity, which is how sharp it can be seen (almost fully closing your eyes until the words go fuzzy can demonstrate acuity loss). The huge amount of variation between fields and acuity loss means that it is very hard to get a one-size-fits-all solution to sight loss.

The second persona has no vision. This is typically what you think of when you think of the word “blind.” No vision means no useful vision—you may be able to see something, but you cannot understand it without third-party intervention. Only a small percentage of vision-impaired people have no vision, but it is crucial to ensure inclusivity for them.

Process maps are inherently visual, so the following tips are mostly based on low vision. Low-vision users, with some tweaks, can read process maps if care is taken by the business analysts to make them as inclusive as possible.

 

Firstly, some whole-map tips. Use a clear font, classified as “sans serif.” These are simple, non-fancy stroke fonts that are easy to read, e.g., Arial and Calibri. Bad examples are brush script, harlow solid, and monotype cursive. Another consideration is the font size. Typically, it is best to produce it in large print, size 14, or giant print, size 18. This is not just helpful for those with low vision but will also reduce eye strain for fully sighted users. San-serif fonts are also dyslexia-friendly.

Secondly, make the connectors, or the lines between process steps, consistent in both thickness and color. The thicker the better, as there is more chance of seeing them, but they must also be in proportion to the other objects, or else it will look so odd that few people will engage seriously with the map. This is the balance between usability and accessibility.

 

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Thirdly, low-vision users will zoom in a lot more than they are used to, sometimes only having a few letters on screen. With this in mind, there are two things that must be considered: Navigation: ID codes. By coding every step, data object, or note, you allow a low-vision user to navigate quicker using metadata instead of engaging with the full object. Each object class should be different; typically, I use process steps as numbers, notes as N1, N2, etc., and data objects as D1, D2, etc. Depending on which software you use to map your process, this can also assist any user in searching quickly and efficiently.

Screen real estate is also an important consideration. The more you zoom in, the less you can see the bigger picture. So, if objects are spaced far apart, it’s harder to understand the map. I recommend placing objects close together. Where you have multiple connectors coming out of an object, line them up so they overlap, looking like one connector, and have them branch off with the connector text as close to the break as possible, allowing someone who is zoomed in to be able to follow with ease.

 

Fourthly, color is important. There are several vision-impaired color schemes, such as yellow on black, white on black, etc. These are all highly subjective but share one common feature: they are high-contrast colors. My advice then is not to use similar colors, such as black and grey, white and silver, or white and yellow, as these types of pairings are very hard to see and can be easily missed or unreadable. Neon colors are highly effective, as most accessible technology offers color inversion, and when you invert a neon color, it stays the exact same shade, meaning there can be no misunderstanding in color coding like RAG systems. I advise only using one color scheme, or at most, in the case of impact assessing a process, a RAG for change size and blue for new—all in neon colors.

Finally, for all users, but specifically No Vision users, think about object semantics. By this, I specifically mean connector lines. Accepted practice means that we have no arrow heads on lines, and a double arrow head is assumed. This is presumably to make it look nice. A screen reader, though, has no context for this as there is no semantic instruction to relay to the user. Therefore, adding doule arrow heads will allow the semantic meaning to go through the connector. This is because a screen reader will consider the connector itself, not the thing it is connected to, which is what a person with sight will do. All a screen reader will visualize is a line, and a sighted user will see the line and the objects connected.

 

To summarize, process maps are visual. We can make them inclusive of low- and no-vision users by adapting our frameworks and standards. Specifically, by looking at font type and size, object layout and identification, color schemes, and the semantic meanings of diagram objects, we can minimize the risk of low- or no-vision users not understanding, thus making the business more inclusive and effective.

These tips are by no means exhaustive nor gospel, so please feel free to use them as a starter for ten, and hopefully they will help you kickstart your own inclusive process map designs!

Business Analysis: How and Why Do I Need To Evolve?

Without a doubt, artificial intelligence (AI) is here to stay and is not going anywhere. Still, it would and has even started disrupting the status quo of many industries and organizations. Well, this is an undisputable, crucial innovation. Still, I would gladly refute Elon Musk’s’ claim that “We will have for the first time something smarter than the smartest human. It’s hard to say exactly what that moment is, but there will come a point where no job is needed” (Marr,2023).

The human factor must be considered in every career path or industry; however, professionals in every space and sphere must evolve with the dynamic and changing environment.

Why do we need to evolve?

Regarding my specialization as a Business Analyst, how and why do I need to evolve?

Recently, there has been a surge in the search for business analysts. This is not because this is a new field; instead, it has existed since the Middle of the Old Stone Age, when the ancestors were able to effectively adapt to the changing natural environment, identify their needs, problems, and opportunities, and develop solutions to make their abode livable and habitable.

 

What is Business Analysis?

According to the BABOK Guide V3, Business analysis enables change in an enterprise by defining needs and recommending solutions that deliver value to stakeholders. Business analysis enables an enterprise to articulate needs and the rationale for change and design and describe solutions that deliver value.

The business analysis field has undergone several nomenclature changes and could be referred to by different names in different industries. Some famous names include Business systems analysis, business process analysis, functional analysis, product ownership, systems architecture, project management, usability analysis, user experience consulting, operations assessment, and technical writing.

 

Business Analysis Requirements

The BABOK Guide v3 views requirements as a usable representation of a need and a design as the usable requirement of a solution. Still, both concepts can be used interchangeably and primarily depend on the context of being used or adopted. Requirements need to be identified, collected, modeled, analyzed, validated, verified, traced, prioritized, managed, and maintained in the lifecycle of a project (Pre and post-project stages). Still, they are all related to a business problem that requires a solution. It could be in the form of an organizational objective that must be met, a business process that needs to be optimized, and an existing solution that needs to be improved or even retired. The BABOK guide v3 defines a Context as the circumstances that influence or are influenced and provide an understanding of a required change. This explains that requirements are broad and depend on the context, such as industry, regulation, project, weather, attitudes, behaviors, etc.

 

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Unpacking BA requirements for Artificial Intelligence

Business analysts should not view themselves as AI experts but understand that they exist to drive change while still understanding the capabilities that AI provides and its complexities. Business analysts must see themselves as a bridge between business needs and AI capabilities.

Understanding the complexities of AI algorithms may and may not be a hard nut to crack; however, with a fundamental understanding of Natural language processing/ machine learning and knowing that most AI tools have been embedded with the critical technology to understand human language, as well as the ability to sieve through large data sets and establish a pattern or relationships, could serve as helpful information. Business Analysis could also establish broader knowledge of AI capabilities.

Also, the core of business analysis is need identification and solution generation. Both are valuable, but the most critical is correctly and efficiently identifying existing needs or problems, thus providing room for developing requirements and generating solutions.

This brings us to the question: can AI help in need identification or problem assessment? Realistically, with established data and available documentation, AI could help identify a need, but Hey, that need would be missing users’ humanity. Whatever solution is generated should provide or enhance satisfaction. However, can AI understand the complexity of the human emotion? With AI, we could develop the goals, desired outcomes, and key performance indicators (KPIs) and define roles and responsibilities, but how can usability be assessed?

With AI in business analysis requirements comes data quality, security, and privacy requirements. Every requirement generated for BA activities must answer these 3 data prongs. How reliable is the requirement gathered? If a requirement is trustworthy, it could speak to its quality. Was the requirement confirmed, verified by necessary stakeholders, and validated to align with identified needs?

To achieve these three tasks, the requirements must be specified and modeled to fit the organization’s environment with due consideration of the stakeholders involved. The modeling can be in the form of matrices or even diagrams, for which AI could be beneficial. Still, the prompts must be correct, which reflects the data quality and reliability. Using AI to generate, specify, or even model requirements (inexhaustive) would lead to data security and privacy prongs.

Privacy and security are critical issues in the professional world, not just business analysis. Before every BA task, how AI should be adopted and what data should be provided as AI prompts need to be addressed. There is a need to protect user privacy and define adequate security measures, as IT systems are susceptible to attacks. Privately owned AI tools can still be attacked; strict security and privacy rules must be strictly followed.

This is also very important as some requirements can serve as Unique selling points for a specific business or even a trade secret. In this situation, the use of AI might be optional.

 

Conclusion

Knowing that the Business analysis role will continue to evolve as a context evolves or dictates or even as a business dictates while putting Artificial intelligence as an addition in a context, it is recommended that the requirements generated in previous contexts be adequately managed and maintained for reuse. When done correctly, this would enhance knowledge sharing as AI could help create a central repository for past project requirements, thus making it easier for business analysts to learn from past experiences and build on existing knowledge, which could lead to overall project success.

 

Overcoming 3 Common Challenges of Business Process Modelling

Identifying and depicting business processes is the first step towards understanding the current state and developing a plan for the future. Business analysis activities are often oriented towards enabling and supporting change. The most important aspect of having a process model is that it enables a business to quickly see how well all the different aspects of the business are aligned to achieve common goals. When there is misalignment, it becomes evident very quickly in the model, and the business can plan how it will deal with getting properly aligned again.

Business analysts, using primarily elicitation and modelling techniques, try to find out the means by which an organization carries out its internal operations and delivers its products and services to its customers.

 

However, process modelling and analysis can be tricky. Below are some challenges:

 

  1. Figuring out the tasks

It’s difficult to obtain information about the complete process when there are many engaged departments. Usually every part of the process is aware of the specific tasks and activities in which they’re involved, but they miss the whole picture. Frequently, after the process modelling has been finalized, the engaged actors can holistically understand the end-to-end process.

  • Trying to figure out first who is involved and the starting and ending points of the process is crucial in order to drill down and find the details for each step. It may be a good idea to begin with the most experienced actors or those who have a helicopter view. It is more than important, however, to validate your insights against other sources of information to be sure that you have captured accurate information.
  • Having information about the industry context may be helpful, as the basic business processes among organizations in the same industry have things in common. This, of course, does not mean that the specific organization’s parameters should not be taken into consideration.

 

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  1. Systems Thinking

Consistency, It’s a necessary verification criteria in process identification and modeling. The steps and tasks involved in the process should make sense as parts that form wholeness, not as independent elements. In order to meet deadlines and get immediate results, business analysts frequently reduce the amount of time spent understanding the context. Delivery of value through a process modelling initiative will be limited, as long as we think analysis is about figuring out just specific characteristics of a solution that are already predefined in their minds. Systems thinking is a vital mindset that allows a business analyst to understand the as-is state and communicate it in a way that will be commonly understood by all stakeholders. This is an essential step in defining the future state.

 

 

  1. Understand how the process fits into its environment.

If a model doesn’t define how it fits into its environment, it will struggle, and its likelihood of resounding success is greatly diminished. Understanding the fit of a process within its internal and external environment is a complex, multi-faceted exercise. A business analyst needs to understand who the actors are, what their needs are, and how they can be reached. The business also needs to know who its suppliers are. Only when all relationships between the internal and external environment are understood can the business analyst ensure it is shaping an effective process model.

 

Identifying misalignment issues and understanding problems and opportunities for the business can be triggered by process analysis via modeling. Through effective process modelling, the following questions can be answered:

  • What processes does the business currently maintain?
  • How do the processes fit within their environment?
  • How do the processes create and maintain value in the external and internal environments?
  • What is the gap between the as-is and to-be states?

How Generative-AI Can Help Modernize Your Legacy Software

Legacy applications, those trusty workhorses that have powered your business for years, can start to resemble a classic car.  They might be reliable, but they lack the sleek design and efficiency of newer models.  Maintaining them can be expensive, and they often struggle to keep pace with evolving security threats and changing business needs.  A study found that 70% of enterprises still grapple with legacy applications, hindering their ability to innovate and adapt. But unlike a car you can trade in, replacing these applications entirely can be a costly and disruptive endeavor.

Here’s where Generative AI swoops in, offering a revolutionary approach to legacy system modernization. Imagine a tool that can analyze your aging codebase, understand its functionality, and then generate modern, efficient code that replicates its core functionality. That’s the magic of Generative AI!

 

7 Warning Signs Your Legacy Software Needs Modernization (and How Generative AI Can Help)

1. Frequent System Crashes and Performance Issues: Legacy software, built with older technologies, might struggle with increased data volumes and user traffic. This can lead to frequent crashes, slow loading times, and a frustrating user experience.

Role of Generative AI: It can analyze code bottlenecks and suggest optimizations to improve performance. It can also help identify areas for code modernization to handle larger datasets efficiently.

 

2. Security Vulnerabilities: Outdated coding practices and unpatched vulnerabilities can leave your legacy software exposed to cyberattacks. This puts your company data and customer information at risk.

Role of Generative AI: It can analyze code for known vulnerabilities and suggest potential fixes. It can also help developers stay up-to-date on security best practices by generating code that adheres to secure coding standards.

 

3. Incompatibility with Modern Systems and Devices: Legacy applications might not integrate well with newer software and hardware, creating data silos and hindering operational efficiency.

Role of Generative AI: It can analyze APIs and suggest code modifications or generation for seamless integration with modern software development. This allows your legacy application to communicate and exchange data effectively.\

 

4. High Maintenance Costs: Maintaining legacy software can be a significant drain on resources.  Bug fixes, code updates, and compatibility issues can require a dedicated team of developers with specialized knowledge of the aging codebase.

Role of Generative AI: It can automate tasks like code documentation and code refactoring. This reduces the need for manual maintenance and frees up developers to focus on more strategic initiatives.

 

5. Lack of Features and Functionality:  Legacy applications might lack the features and functionalities of modern software, hindering your ability to compete and meet evolving customer needs.

Role of Generative AI: It can analyze user interactions and suggest improvements to the UI/UX. It can also generate code snippets for modern UI frameworks, allowing developers to create a fresh and user-friendly experience.

 

6. Difficulty in Finding Developers with Legacy Expertise: As technology advances, developers with expertise in older programming languages and frameworks become scarce. This can make it challenging to find qualified personnel to maintain and update your legacy application.

Role of Generative AI: It can bridge the knowledge gap by automatically generating code that replicates the core functionality of the legacy application. This allows developers with modern skill sets to contribute to the modernization process.

 

7. Limited Scalability: Legacy applications might not be able to scale to accommodate future growth or increased demand. This can stifle your business potential and hinder your ability to expand.

Role of Generative AI: It can analyze code for scalability bottlenecks and suggest optimizations. It can also generate code for integrating with cloud platforms that offer greater flexibility and scalability.

 

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Step-by-Step Process to Modernize Legacy Software with Generative AI

Step 1: Identify Target Applications:

Begin by prioritizing which legacy applications require modernization the most. Focus on systems critical to business operations or those causing significant pain points.

Step 2: Inventory and Analyze Existing Systems:

Document your current technology stack, including programming languages, frameworks, and databases used by the legacy application. Analyze the codebase to understand its functionality and identify areas for improvement.

Step 3: Code Refactoring and Optimization:

Utilize GenAI tools to analyze the codebase and suggest automated refactoring options. This can involve removing redundant code, improving code readability, and optimizing for performance.

Step 4: Modern UI/UX Design:

Use GenAI to analyze user interactions and data to identify opportunities for improving the user interface and user experience. Generate code snippets or mockups for a modern and intuitive design.

Step 5: Incremental Modernization:

Modernize the legacy application in phases to minimize disruption and risk. Start with smaller, less critical functionalities and gradually work your way towards core components.

Step 6: Continuous Integration and Delivery (CI/CD):

Implement a CI/CD pipeline to automate code testing and deployment. This ensures rapid integration of GenAI-generated code with minimal errors.

Step 7: Monitoring and Performance Analysis:

Continuously monitor the performance of the modernized application and address any potential issues promptly. Utilize AI-powered monitoring tools for proactive problem identification.

The Pitfalls Of Efficiency: Process Improvement Is A Balancing Act

Business analysis work often involves improving processes. This might include simplification of a process, reengineering or automation. When used well, IT can be used to enhance (or even completely rethink) a process. The ideal outcome is to design a process that is quicker, more convenient and more cost-effective than what it replaces.

 

When aiming for efficiency, it’s important to ask “for whom are we optimizing this process?”. This might sound like an odd question to ask, but often there’s a fine balancing act. A process that appears very efficient for a company might actually be very inefficient and inconvenient for its customers. Standardizing a procurement process might create internal efficiencies for the company involved, but might place additional work on the company’s suppliers.

 

An Example: “No Reply” Secure Email

I was recently a customer of a company that would send correspondence via secure email. I’d receive a notification via regular email, and I’d then need to log in to the company’s secure email portal to read what they had sent me. This was fine, except the emails they sent were all from a ‘no reply’ address.  While the secure email system they had implemented literally had a ‘reply’ button, there was a disclaimer on every email they sent which said “don’t reply, as we won’t read what you send us” (OK, it wasn’t that blunt, but you get the idea!).

This led to the crazy situation where the only way of replying to their secure emails was to either call via phone (and queue for 45 minutes), or put a reply in the mail.

 

This is an example of a situation where convenience and savings are predominantly biased towards the company, with some minor benefit for the customer. Prior to sending secure email, they would put correspondence in the regular mail. Moving this to an electronic platform presumably saves in printing, postage and stamps. It’s of marginal benefit to customers too, as they receive correspondence quicker (providing they look at their email regularly).

But the real customer benefit would have been to be able to correspond and reply with the company via secure email. Ironically, by implementing the solution the way that they did I suspect their ‘no reply’ mailbox is actually full of replies from customers who didn’t read their disclaimer!

 

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There is no “right”, it’s a balance

As a customer, I found the situation frustrating, but there is no inherent or universal ‘right’ answer here. It might be that the company in question had deliberately chosen not to accept incoming secure email for compliance reasons, or perhaps they feared they’d be flooded with lots of customer inquiries as they are now ‘too easy’ to contact (although I’d argue that if this is the case then there’s probably a bigger root cause they ought to be contending with!).

 

The point here is that it should be a conscious balancing act. It is all too easy to create a situation that is more efficient for one group of stakeholders, but actually worse for another. An employer who decides to streamline their process for employees who need to claim travel expenses might decide that they can save time if they ask their employees to input more data at the time they submit their claim. If they get the employee to select where the expense was incurred, the amount of sales tax that was included in the expense, the category of cost and so forth, then this saves time later. Yet an employee who isn’t a tax expert might find this frustrating (“Is train travel exempt, or zero-rated for sales tax?”). Of course, in reality this will likely affect the quality of data too, as people try their best (but don’t know which of the different tax code options to choose).

This is a specific example, but it highlights a wider point: it’s important to consider process improvements from the perspectives of the stakeholders impacted. This involves considering what efficiency as well as effectiveness looks like for each key group.

As with so much in business analysis, stakeholder identification, engagement and empathy is key!