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Tag: AI

BATimes_May15_2024

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.

 

BATimes_Apr01_2024

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.

BATimes_Apr4_2024

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.