Introduction: Welcome back to our exploration of At-Scale Design and Development. In this installment, we delve into Layer 2 – Business Decision Support. Business Decision Support focuses on empowering businesses to make informed decisions that impact end users. Let’s delve into the key aspects of this layer.
The Scope of Business Decisions: At the heart of Layer 2 lies the realm of business decisions that directly affect end users. These decisions shape the direction and objectives of a business, and the goal of Business Decision Support is to provide the necessary tools, automations, and insights to enhance the decision-making process. Here are some essential considerations to enable At-Scale Design and Development:
- User Involvement in Decision-Making: The best Business Decision Support systems allow users to be active participants in the decision-making process. Users can choose to be part of the decision initially to establish trust and gradually transfer control to the system. This flexibility enables the system to either fully replace human involvement or augment human judgment, depending on the specific decision context and the user.
- Identifying and Improving Decisions within the Workload: Business Decision Support involves identifying critical decision points within the workload and determining how to enhance them. The introduction of AI powered models and Robotic Process Automation (RPA) play a significant role in accuracy and streamlining decision-making processes respectively:
- a) Identify Decisions Impacting Business Objectives: Thoroughly analyze the workload and identify the specific decisions that directly impact the business objectives. These decisions can range from strategic choices to operational tasks that have a significant influence on the overall success of the business.
- b) Decompose the Human Decision-Making Process: Break down the steps involved in the human decision-making process. This includes identifying the explicit data, information, and factors that inform the decision-making process. At this stage, it is important to acknowledge that, in addition to explicit data, there are other tried and true approaches to creating data that greatly benefit the decision-making process. This encompasses advanced analytics, which involves identifying the problem, researching existing models that address the problem, and determining the data needed to enable these models.
- c) Integrate Robotic Process Automation (RPA): Leverage the capabilities of Robotic Process Automation to automate and optimize repetitive, rule-based tasks and decision-making processes. RPA can perform data entry, data validation, and routine decision-making tasks more efficiently, reducing errors and freeing up human resources to focus on more complex and strategic aspects of decision-making.
- d) Enhance Decision-Making Outcomes: Assess the potential impact of decision support systems and RPA on the decision-making outcomes. The aim is to achieve one or more of the following outcomes:
- i) Higher Accuracy and Speed: By leveraging advanced analytics, AI models, and RPA, decision support systems can significantly enhance the accuracy and speed of decision-making processes. This leads to more precise and timely decisions, resulting in better business outcomes.
- ii) Enhanced Experiences for Customers: Decision support systems, in conjunction with RPA, can improve customer experiences by providing personalized recommendations, streamlining processes, and delivering targeted solutions. This enables businesses to meet and exceed customer expectations, enhancing satisfaction and loyalty.
- iii) Actionable Insights for Employees: RPA and decision support systems empower employees by providing actionable insights derived from data analysis and automation. This enables employees to make informed decisions that drive productivity, efficiency, and innovation within the organization.
- Flexibility and Enterprise-wide Implementation: To accommodate future AI models and ensure scalability, Business Decision Support systems must be flexible and designed for enterprise-wide implementation. This includes incorporating different data pipelining techniques, AI models, and performance optimization methods such as queues, messaging, and caching. Here are some typical considerations to keep in mind when designing data pipelines and incorporating AI models into the Extract, Transform, Load (ETL) process:
- Data Source Integration: Ensure that the data pipelines are designed to integrate seamlessly with various data sources across the enterprise. This may include structured and unstructured data from internal systems, external APIs, databases, cloud services, or other relevant sources. Implement robust connectors and adapters to facilitate smooth data ingestion.
- Data Quality and Cleansing: Prioritize data quality by implementing mechanisms to validate, cleanse, and enrich the data as part of the ETL process. This involves identifying and addressing data inconsistencies, errors, duplicates, or missing values. Establish data quality rules and implement automated data cleansing techniques to ensure the accuracy and reliability of the decision support systems.
- Modular and Scalable Architecture: Design the data pipelines and AI model insertions with a modular and scalable architecture. This allows for easy integration of new data sources, models, and components as the business requirements evolve. Implement a microservices architecture or similar modular design principles to enable flexibility and independent scaling of different components.
- Metadata Management: Implement a robust metadata management framework to track and document the flow of data and AI models within the decision support systems. This includes capturing information about data sources, transformations, mappings, and model versions. Effective metadata management facilitates data lineage, traceability, and governance, ensuring transparency and compliance within the enterprise.
- Performance Optimization: Optimize the performance of data pipelines by implementing techniques such as parallel processing, caching, and message queues. These optimizations help manage large volumes of data efficiently, reduce processing time, and improve overall system performance. Implement data compression techniques and distributed computing frameworks, if applicable, to maximize scalability and processing capabilities.
- Model Versioning and Deployment: Establish a model versioning and deployment strategy to ensure seamless integration of AI models into the decision support systems. This involves implementing practices to track model versions, manage model training and validation, and automate the deployment process. Adopt containerization technologies, such as Docker or Kubernetes, to encapsulate and deploy AI models consistently across different environments.
- Monitoring and Error Handling: Implement robust monitoring and error handling mechanisms within the data pipelines and AI model insertions. This includes real-time monitoring of data flow, model performance, and system health. Set up alerts and notifications for anomalies, errors, or deviations from expected behavior. Implement appropriate error handling strategies to ensure fault tolerance and data integrity.
- By considering these design considerations, businesses can create flexible, scalable, and future-proof decision support systems that effectively integrate data pipelines and AI models. These systems will be able to adapt to changing business needs, accommodate new data sources and models, and enable enterprise-wide implementation.
- Continuous Refactoring and Core Capabilities: At-Scale Design involves the continuous refactoring of point solutions into core capabilities that are accessible to the entire enterprise. In other words, the development time must include the opportunity to integrate into the core capabilities branch. These core capabilities not only provide essential functionality but also include non-functional components such as authorization and accounting.
- Human Interaction and Deployment: Before committing to a core capability, it is crucial to analyze the decision support functionality ability to enable human interaction. This analysis informs the deployment strategy and highlights any necessary skill gaps that need to be addressed for both customers and employees.
By incorporating Business Decision Support at Layer 2 of the At-Scale Design and Development concept, businesses can leverage advanced analytics and data-driven approaches to make informed decisions that positively impact end users. In the next post, we will explore Layer 3 – the Core Technology Stack, which forms the foundation for implementing and supporting these decision support systems. Stay tuned for more insights!
[Note: This post covers only Layer 2 of the At-Scale Design and Development concept. The previous post on layer 1. The subsequent layers will be discussed in future posts.]