Business Goals Drive Data Management for Data Citizens
Executive Summary
This webinar highlights the importance of Business Architecture and data management in various industries. Emphasising the significance of aligning business and data architecture, the principles of architecture and data models, and the implications of data modelling in financial services and retail. Howard Diesel addresses the need for industry-specific value streams, data exchange, and standardisation. Discussing business innovation, consultation, support, and documentation of value streams and common capability mapping. The webinar underscores the role of enterprise architecture, Business Strategy, and information models in driving business success and emphasises the significance of data management and business literacy.
Webinar Details:
Title: Business Goals Drive Data Management - Citizens
Date: 5 June 2024
Presenter: Howard Diesel
Meetup Group: Data Citizens
Write-up Author: Howard Diesel
Contents
Business Architecture and Data Management
Enterprise Architecture and Business Architecture
The Importance of Business Architecture and Data Management
Business Architecture and Data Management
Business Strategy and Architecture
Principles of Architecture and Data Models
Business Architecture Playbook and Data Strategy
Porter Model and Common Reference Model in Business
Documentation of Value Streams
Building a Common Capability Map
Data Modelling and Industry Specific Value Streams
Transportation and Shipping Model
Understanding Industry-Specific Models
Data Management and Business Literacy
Importance of Aligning Business and Data Architecture
Consultation and Business Support
Business Innovation and Data Management
Data Exchange and Standardisation
Business Architecture and Information Models
Implications of Data Modelling in Financial Services and Retail
Document Design and Management in Business Environments
Business Architecture and Data Management
Howard Diesel opens the webinar by discussing the importance of grasping Business Architecture in data management. He highlights this as it influences the focus and objectives of data analysis and analytics. Furthermore, lacking a solid understanding of Business Architecture may hinder effective stakeholder interviews and interdepartmental collaboration, potentially leading to suboptimal outcomes.
Enterprise Architecture and Business Architecture
Enterprise and Business Architecture are crucial for aligning Data and Business Strategies. Business Architecture focuses on identifying stakeholders and delivering value, while business processes detail how objectives are achieved. The Data Management Body of Knowledge (DMBoK) framework aims to integrate business and data management. Managers are responsible for aligning data management with various architectural aspects, improving overall organisational performance.
Data citizens adhere to policies and procedures, including data stewards and Subject Matter Experts (SME). Industry reference models aid in understanding data citizens' roles. Architects must align themselves with Business Architecture and build a data estate that aligns with the overall Data Strategy. Executives oversee and align Data Strategy with Business Strategy to ensure organisational success.
Figure 1 Business Architecture (BIZBOK) & Data Management (DMBOK)
Figure 2 Business Architecture & Data Management Key Topics
The Importance of Business Architecture and Data Management
The importance of delivering value to the business through data-related efforts and ensuring alignment with Business Architecture is outlined. Howard emphasises the role of data citizens in Business Architecture and highlights the need to obtain core business objects from the Business Architecture. Common mistakes in using industry business models are cautioned against, and the significance of connecting enterprise data models to Business Architecture is underscored with real-life examples.
Figure 3 Business Architecture & Blueprints
Business Architecture and Data Management
Understanding Business Architecture is crucial for enhancing one's data architect and manager skills. The Zachman model provides a framework for addressing key questions about the business, progressing from concrete to abstract elements and focusing on the "why" as the most pivotal component. It encompasses core areas like products, value streams, and capabilities and their interconnectedness.
Recognising the relationship between the enterprise data warehouse and Business Architecture is vital. The journey from conceptualising the business to establishing strategic objectives and ideation is fundamental to this understanding.
Business Strategy and Architecture
Howard emphasises the importance of aligning Data Strategy with Business Strategy in Business Architecture. Initiatives should be aligned with the overall business plan for effective implementation. With experience in Business Architecture modelling, an attendee notes that ArchiMate, a modelling tool, has been essential for representing Business Architecture models. In projects, envisioning and building models together is crucial as storing a meta-model for Business Architecture for reference. Additionally, Power BI can be utilised to extract data from ArchiMate, and a transport industry model's Business Architecture encompasses 1886 capabilities.
Figure 4 Business Architecture Journey
Figure 5 Business Architecture Metamodel
Principles of Architecture and Data Models
It is important to note that simply visualising concepts in your head is ineffective. It is essential to connect the dots between capabilities, information, states, and stakeholders, as it optimises the network and translates back into the capability map. It is crucial to align decisions, processes, and strategies for business focus and purpose. The scope of architecture principles encompasses the entire ecosystem, including information management, finance, marketing, and more.
Different industry data models, such as transportation and financial services, play a crucial role in providing Excel sheets with capability maps, value streams, and information maps. These models are not just theoretical constructs, but practical tools that can be used to understand and manage complex business data. Understanding the business and its capabilities is essential for creating effective data models.
Figure 6 Principles of Architecture and Data Models PowerBI Demonstration
Figure 7 Principles of Architecture and Data Models PowerBI Demonstration continued
Figure 8 Principles & Application
Figure 9 Templates
Business Architecture Playbook and Data Strategy
The Business Architecture Playbook is not just a theoretical concept, but a practical tool that involves key steps like understanding Business Strategy, assessing business impact, architecting the business solution, establishing initiatives, and deploying the solution. Howard emphasizes that data use cases are not isolated, but interconnected with business impact and align with business objectives and information impact assessment.
The Data Strategy is not a standalone plan, but a seamless integration with business-driven IT architecture and digital and data areas to support business products. By joining the Business Architecture Guild, one can access metamodels for data citizens, making it a valuable resource for professionals in this field. Identifying the value and focusing on the data value architect is not just a suggestion, but a crucial step in recognising the organisation's value proposition. Furthermore, the model developed by Porter is not just a theoretical framework, but a cornerstone of Business Strategy and development.
Figure 10 Business Architecture Framework (Playbook)
Figure 11 Business Architecture for Data Citizens
Porter Model and Common Reference Model in Business
The Porter value chain, developed by Michael Porter, separates primary business activities like inbound logistics and marketing from support activities such as human resources and technology. This model emphasises the importance of business operations and capabilities in achieving differentiation and competitive advantage. Understanding this model is essential for identifying its relevance to your industry. The common reference model includes stakeholders, value streams, and value propositions.
Stakeholders encompass various supporting departments and roles like accountants and campaign managers, while value streams represent stages connected to data, forming the data value chain. Examples of value streams include acquiring an asset, conducting an audit, and delivering initiatives. Ultimately, delivering value to internal stakeholders is the final result of executing a value stream, with a distinction between the value delivered to internal and external stakeholders.
Figure 12 Porter Value Chain
Figure 13 Stakeholder Map
Figure 14 Stakeholder Map continued
Figure 15 Common Value Streams
Documentation of Value Streams
The documentation of value streams is crucial for understanding their purpose and target audience, with the value proposition serving as a significant Key Performance Indicator (KPI), particularly for external customer deliveries. The value stream encompasses stages such as request and audit, each with specific entrance and exit criteria. Minor KPIs contribute to the major KPI and aid in identifying bottlenecks within the stream. Measurement and comprehension of the value provided by the value stream are vital for progress, and different documentation approaches are utilised based on customer size and project scope. This documentation is especially critical for larger projects with multiple KPIs and a focus on overall value delivery.
Figure 16 Value Streams Example
Building a Common Capability Map
An effective OKR (Objectives and Hey Results) framework involves analysing strategic direction, customer-facing activities, and supporting elements through KPIs to pinpoint areas for enhancement. A maturity assessment can be developed based on an organisation's performance in these areas, while a common capability map can be universally applied, encompassing agreements, customers, orders, partners, and products.
Information concepts are categorised as primary and secondary elements, such as primary concepts like agreements and their related secondary attributes, including terms. Additionally, industry certifications cover specific concepts, measurement methods, quality assurance, and common industry issues, providing comprehensive knowledge and understanding.
Figure 17 Common Capability Map
Figure 18 Common Information Map
Data Modelling and Industry Specific Value Streams
The importance of intangible assets, such as data assets, in Business Architecture and data modelling is emphasised. Howard highlights the significance of comprehending high-level business concepts and entities across various industries. A blueprint of the transportation industry serves as a basis for validating existing models and presents specific value streams related to routes, trips, and shipments. The concept of value streams is utilised for scheduling and establishing checkpoints, ensuring thorough planning and documentation.
Figure 19 PowerBI Demonstration
Transportation and Shipping Model
The shipping process involves entry criteria, engaging a carrier, shipping under the carrier's control, receiving the shipping at the destination, and ensuring the shipping is under the recipient's control. It is essential to have a breakdown of the value stream to measure and assess if the shipping gets lost or if there are any issues with the carrier or itinerary.
Common models inherited from the transport model eliminate the need to start from scratch, and the transportation and shipping model includes tiers such as transportation, material, route management, shipment, trip, network, and conveyor. This model significantly improves dealing with transportation and shipping scenarios, and in mapping the entire business, industry-specific models like the lens of business models are valuable. Decision management, event management, information management, competency management, and work management are all part of the model.
Figure 20 Industry-Specific Value Streams
Figure 21 Value Stream Example
Figure 22 Inherited Common Value Streams
Figure 23 Different Transportation Tiers
Understanding Industry-Specific Models
The port authority model differs from the transportation model, so it's important to establish connections between the two. Mapping in different areas is essential since the port authority is a form of transport. Unique industry specifics in financial services include service portfolios, financial agreements, investment portfolios, transactions, and payments.
Understanding value streams facilitates more effective discussions with the business, while input from citizens and SMEs can help define industry models for easier reference. Building agreements with customers and stakeholders is crucial, although stakeholders may change.
Risk officers evaluate risks and agreements, and customer service is also important. Effective issue management and dealing with partners are critical in financial services. Finding and mapping an appropriate industry reference model to the organisation and connecting industry data models and enterprise data models is necessary, especially when working in specific industries like transportation and port authority.
Figure 24 Port Authority Business Model
Figure 25 Industry-Specific Value Streams
Figure 26 Value Stream Example
Figure 27 Key Topics for Data Citizens
Data Management and Business Literacy
Establishing connections between data terms can be challenging due to potential synonyms, highlighting the importance of clearly defining terms and business concepts to prevent confusion. Gaining insights into data management's business and financial aspects is crucial for developing business acumen.
Data professionals must ensure alignment between business entity definitions and data management goals. However, conflicts over definitions can arise, particularly when it comes to maintaining a Business Glossary. Lastly, leveraging business measurements, KPIs, and revenue analytics is integral for driving revenue growth and overall business success.
Figure 28 Question 1: Why should I care?
Importance of Aligning Business and Data Architecture
Enhancing customer value propositions and understanding for improved efficiency is emphasised. Howard stresses the need to integrate this improvement into the enterprise data model and Data Strategy. Susan Earley's context diagram is recommended for its focus on principles rather than practical implementation.
For small and medium-sized enterprises, key points included identifying business goals, drivers, and metrics for goal achievement. Additionally, data citizens were advised to align data quality, metadata, and master data practices with their strategic objectives and value propositions. The alignment between business and data architecture was underscored as crucial for project success and value delivery. Creating a data model without adequately understanding and justifying the business's value propositions, capabilities, and information maps is insufficient.
Figure 29 DMBOK Context Diagram
Figure 30 DMBOK Context Diagram - Data Citizen
Consultation and Business Support
In business consulting, providing clients with objective guidance and aiding in enhancing their business operations is crucial. This involves improving clients' vocabulary to discuss processes and capabilities effectively and promoting structured thinking and communication regarding the value chain and basic capabilities to enhance business relationships.
Prioritising business terms over technical data discussions in initial client meetings is essential. Avoiding the blind acceptance of industry models without assessing their suitability for the specific business and industry context is advised to avoid common consulting mistakes.
Figure 31 Consultation and Business Support
Figure 32 Question 3: Common Mistakes
Business Innovation and Data Management
Howard raises questions about the relevance of conforming to industry standards regarding business terminology and communication strategies, suggesting that this may not always lead to a competitive advantage. The gap between business and data architecture and the lack of linkage between the terms used in the enterprise data model and the business model are highlighted.
The attendees and Howard discuss data management and the responsibility for building the Business Glossary, potentially pointing out challenges in implementing a comprehensive model that aligns with business needs. Howard then mentions the challenges faced while implementing a reference model in a local insurance company, which led to an excessive and unnecessary adoption of certain components, potentially indicating pushback from the business side.
Data Exchange and Standardization
The need for data standardisation varies among companies. Port authorities have successfully standardised data about ships and containers, while data exchange companies prioritise enforcing data details. However, some individuals have had negative experiences with data standardisation, citing costliness and unhelpfulness.
Successful implementation examples are found in the South African Reserve Bank, and there is an ongoing discussion of Len Silverstone's work on data models across different industry types. Challenges remain in integrating various data models into a cohesive enterprise data model.
Figure 33 Industry Data Models
Business Architecture and Information Models
Howard focuses on linking Business Architecture and information models, which he notes can be accessed by joining the Business Architecture Guild. Common reference models are available for financial services, government, health care, insurance, and transportation.
Healthcare and insurance can be combined, as seen with Vitality and Discovery, while similar models are available for the telecommunication and retail industries. It's important to selectively utilise parts of the industry models that make sense rather than trying to incorporate everything.
Figure 34 Business Architecture and Information Models templates
Implications of Data Modelling in Financial Services and Retail
In financial services, data modelling incurs high implementation, configuration, and training costs, yet it offers immediate benefits and a clearer sense of direction. This can lead to profound realisations about new data modelling concepts, adding significant value to the business and enhancing decision-making. Comparatively, the fast-paced environment and narrow profit margins in retail contrast with the more deliberate nature of data modelling in other industries. Companies like Amazon use NoSQL databases, which underline the evolving nature of data modelling, emphasising the need to handle increasingly large documents and split and categorise data efficiently.
Document Design and Management in Business Environments
Using JSON queries to query across multiple documents has led to the examination of MarkLogic. Document design entails more than just transferring on-screen content into the document. The significance of management capabilities and maturity in professional data management is emphasised, along with the disappointment in the declining use of Enterprise Performance Management frameworks such as balanced scorecards.
Howard, closes by covering the challenges organisations face in implementing concepts like OKR, leading some to opt for simpler financial management approaches. He emphasises the value of maintaining scorecards and being agile in addressing business challenges, along with the limitations of OKRs and the holistic view of balanced scorecards in evaluating organisational performance.
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