Food For Thought | Architectural guidelines

In our previous Food For Thought blog we have dived into the business question where to put your data platform, in the cloud or on-premise. No matter your choice, architectural guidelines are crucial for a consistent solution, adding significant business value in every situation.

Crafting a data solution for an organization requires a well-defined architecture and guidelines to maximize its value. Simply providing requested information may seem sufficient, but ensuring a shared understanding of data definitions across domains is crucial. Guaranteeing consistency in the face of change, addressing security concerns, and managing new requests are challenges that demand a consensus on architectural guidelines. Without such agreement, a sustainable solution that effectively handles these issues may prove elusive. Defining architecture and guidelines is about establishing a foundation for seamless adaptation to evolving data and analytics demands in organizations.

Defining architecture guidelines comprises a few subjects; organizational architecture, data architecture & platform enterprise architecture. Establishing these will significantly impact the business value of your data landscape. Let’s elaborate on each item in more detail.

First, your organization should be ready to implement and make effective use of a data & analytics solution. Organizational architecture delineates teams, roles, and responsibilities, ensuring a structured setup conducive to a tailored data and analytics solution. Do you prioritize financial reporting from ERP systems or steer with (near-) real-time data for swift, predictive decision-making? These choices will require a different organizational architecture to produce the best outcome in the end.

The Data Architecture will describe the data definitions, security and processes. With these in place, the entire organization understands data domains and manages the "one source of truth" concept clearly. Data Architecture can be split into 3 topics: data management, data migration & transformation, and data governance.

Data management will enable the effective use of data in the business by describing definitions, naming conventions, business processes and complexity. This will help to understand how data entities are being leveraged by business domains, how they are managed and where they are located. Better data management can improve data quality in some cases. However, the “garbage in is garbage out” principle, remains. Be explicit in the conventions set for the data fields. For example, the rule that every numeric field should be noted as “accountnr”, should be consistent overall. Do not accept other fields like “tax_nr”, “customernum” or “client_number”. Modelling a consistent solution leads to better understanding and more effective use of your data solution.

Data migration & transformation focusses on describing the ability to transform the data from sources, data definitions and business requirements, while keeping data quality at a high standard. It defines where transformations should take place and how changes in data sources are accepted. Also, this defines migration and recovery processes in case of data disruption. Data governance is about the security components of the data. Next to this, it describes; who is responsible for accessing data, who is able to receive data and who is owning the data definitions across the company. Functionally, this is tackled by assigning specific data owners and data stewards in the organization to own these topics.

A robust platform architecture, aligned with enterprise needs, safeguards against shifting priorities, ensuring long-term savings. Clear understanding of requirements prevents investing time in tool demos or concepts partially aligning with the strategy. An ideal platform architecture is scalable, flexible, and resilient, designed to adapt to changes while remaining stable, available, and cost-effective. It should effortlessly accommodate new requirements, enabling easy adjustments or extensions with new features. For instance, gaining real-time insights from manufacturing execution systems (MES) is crucial for proactive decision-making. Designing a platform that seamlessly integrates real-time data processing with batch data processing prevents delays and empowers swift responses to day-to-day operations, averting potential issues. Embracing change and preparing for evolving data requirements, from descriptive to predictive and prescriptive analytics, ensures the platform's readiness for the future.

Defining an overall architectural strategy for your data & analytics platform is not the first topic on your list, if you want to gain insights from your data. However, having an overall architectural strategy is crucial. This will help define structure, be more cost-effective, and preventing miscommunication in your data journey. Moreover, an overall architectural strategy will enable a clear vision for the whole organization and applies focus on working towards the “one source of truth concept”.