1 2379 - BUSINESS ANALYSIS IN A DIGITAL TRANSFORMATION CONTEXT Module 11: Business Analysis and Analytics
2 Module Titles Module 12 - Business Analysis and Digital Transformation Course Plan Module 1 – Introduction to Business Analysis Module 2 – Elicitation and Collaboration Module 3 – Strategy Analysis Module 4 – Requirements Analysis and Design Definition I Module 5 – Requirements Analysis and Design Definition II Module 6 – Requirements Analysis and Design Definition III Module 7 – Requirements Analysis and Design Definition IV Module 8 – Requirements Life Cycle Management Module 9 – Business Analysis Planning and Monitoring Module 10 – Solution Evaluation Current Focus: Module 11 – Business Analysis and Analytics
4 • Clearing the assumptions around types of AI technologies, industry terms, implementation approaches, current capabilities, andlimitations • Appreciating changing best practices, methodologies, and tools to apply in business analysis for AI Learning Outcomes for this Module
5 Module 11 – Section 1 Accelerating Artificial Intelligence with Business Analysis
AI in the Enterprise Space ■ AI is an umbrella term used as a catch-all bucket for any technology that strives to perform tasks that would require human intelligence and skills ■ Data Science provides you with a set of tools and techniques to understand the story behind the data in the form of insights which ML can discover and learn from. ■ It is critical for BA professionals to have clarity of terminologies to provide the right alignment when faced with a business problem
6 AI in the Enterprise Space
6 Hype Cycle for Emerging Technologies
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6 AI in the Enterprise Space ■ Predictive Analytics and Recommenders: Google, Amazon, Netflix, and Baidu from search optimizations, movie recommendations and all parts of consumer decisions. Fukoku Mutual Life, for example, is using IBM Watson to calculate policy payouts, and pricing with subtle ML routines modeled for individual customers ■ Chatbots and Voice Assistants: Voice assisted banking transaction with Luvo, a Natural Language AI bot from Royal Bank of Scotland (RBS) and many such bots in write out FSI sectors to streamline transactional processes ■ Image Recognition, Processing, and Diagnostics: Computer vision and related solutions historically challenged by massive data requirements and computational power have emerged a winner in healthcare, biotechnology, automotive simulations, weather research, driverless cars, and many other fields.
6 Business Analysis In the AI Space ■ BAs must learn technology deep enough to know the possibilities offered by the technology, evaluate its utility, applicability, and benefits in specific business situations ■ BA should understand how business, stakeholder, and solution requirements need to be elicited, analyzed, and documented based on the technology chosen to implement a solution for a business situation • The secret recipe for effective AI implementation isto draw out the heuristic knowledge from stakeholders and data that is part of a decision process. • The task of how value is derived out of a unclear situation to rulebased heuristics, and then to a structured algorithm with a primary focus of making the experience or products better remains the forte of BA professionals
6 Machine Learning for BA Professionals
DECISION STRATEGY Identify Stakeholder: An AI engagement will also include knowledge sources and decision makers in this process who might be outside the organization Identify Business Objectives: The task of BA professional remains to direct the effort in the right direction given the opportunity and constraints. Identify Analytical Problems/ Decisions: The analytical entities and data about these entities would be the inputs for an ML solution. Prioritize Decisions: The chain of decisions that are interdependent need to be evaluated on how they affect the overall objective
DATA ANALYSIS AND MODELLING Collect Data: Equipped with some amount of domain knowledge, a BA professional must analyze the type of decisions or questions ML model must answer and evaluate the data needs and sources from where that data can be obtained. Prepare Data: Although a data engineer will most likely work on data manipulation activities, a BA professional is best suited to provide the business context required in bad or incomplete data treatment. Train ML Model: BA professionals can have a working knowledge of the capabilities of different algorithms widely used and should also be able to understand and explain the results from algorithms to stakeholders Optimize and Cross-validate: As a BA professionals the primary task here is to support the data science professionals by providing the relevant business context when the optimization process is underway.
DECISIONS INFERENCE Evaluate Predictions/Model: . The realworld applications still require a second look, and a BA professional is best positioned to provide that context and analysis to evaluate the insights from a model for the business application. Derive Insights: BA professionals can communicate the story behind the data for the business stakeholders. Formulate Business Recommendations:The bottom line for BA professionals in crafting an ctionable recommendation remains the ability to think both at a micro and macro-level on how decisions will impact the organization analytically when utilizing ML outcomes. Integrate with Business Processes: The focus for the BA professional should be a conversation around business value, customer experience, and transformation aspects that need to be clearly thought out. A roadmap or an action plan can be crafted around it
9 Module 11- Section 2 Evolution of Business Analysis in AI Engagements
10 AI and Data-driven Solutions ■ Every business today wants a data vision where insights from data, the predictive and prescriptive capabilities of AI solutions can be harnessed into better decisions and customer experience ■ Business analysis tied to a project-based cycle of requirements discovery, analysis, elaboration, and communication will have to contend with a product-based approach where shades of AI capabilities will be introduced into almost every IT solutions soon
10 Citizen Data Journalists ■ The contributions a BA professional can make is not limited to purely data analysis and modelling phases in the lifecycle of an ML engagement. ■ Both decision strategy and decision inference require a significant contribution from BA professionals. • Just like a true journalist, a BA professional needs to identify the right stakeholders to discover the business problems/ decisions
11 Customer Advocacy 11 ■ AI provides a set of extremely powerful tools and approaches to quantify many areas and objectives of an organization. ■ Even with all power of ML to provide customer value for a segment of one that explains customer behaviour, decisions, and motivations with a hundred percent accuracy is still a far cry. ■ BA professionals within this context must remain the voice of reason in any AI engagement to represent the customer and unearth relevant knowledge through empathy where data might not tell the full story.
12 Context-Aware Solutions ■ Context awareness of solutions has been limited to users directing the IT solutions and apps to remember their choices and preferences, but the new breed of the solution will be more intelligent through AI technologies. ■ Not only simple personalization the business processes will be more context-aware of different customer scenarios. • For example, in e-commerce with targeted recommendations, products and services, contextual marketing, flexible supply chain and channels of your choices will all come together to provide a better experience.
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