You're a business architect who needs to make data-driven decisions. What do you need to know?

After over 15 years as a practising business architect responsible for many change and transformation projects into the billions in budget, data is key if you're going to get good outcomes out of what you do - as rubbish in, rubbish out. It's more important imho to apply these 3 principles in your search for this good data that you can re-use... you need to be: ? Problem-specific ? Service-led ? Value-driven

To make data-driven decisions as a business architect, who must to be familiar with various aspects of data analysis and interpretation. This includes understanding statistical methods, data visualization techniques, data science foundations and tools for data manipulation and analysis. Additionally, knowledge of data governance, data quality, and relevant key performance indicators (KPIs) for your specific business domain is essential. Familiarity with data management platforms and emerging technologies such as machine learning and artificial intelligence can also be beneficial. Ultimately, the ability to translate data insights into actionable business strategies is crucial for an effective business architect.

The first call here is to build a fully holistic view of the organization, by mapping all data sources and designing a conceptual Entity Diagram to understand how everything is connected.

As a business architect making data-driven decisions, it's crucial to understand and implement data governance principles, integrate diverse data sources, and possess proficiency in data analysis tools and statistical concepts. The ability to visualize data effectively, align insights with business goals, and explore predictive analytics enhances decision-making. Stay informed about data security and compliance, collaborate with data professionals, and continuously update skills to adapt to evolving technology. Strong communication skills and a commitment to experimentation and iteration are essential for translating complex data findings into actionable insights that contribute to organizational success.

As all business architects know, we should establish a domain model and reach a consensus with clients initially. Particularly, in the approach, identifying domain data accurately is a challenging yet crucial step. Looking back at past experiences in the domain model development, it can be asserted that revisiting the period before IT systemization, especially when business processes were not yet automated, and comprehending the business documents and their flow, is one of the effective methods to construct a domain data model. This process contributes significantly to ensuring the accuracy and effectiveness of the domain data model.

I also find discussing the data analysis (& quality considerations) with a subject matter expert is a must. They will be able to support the early/exploratory analysis work and also help identify any patterns/trending with the results including (if not known to you as the Biz Arch) seasonality, cyclical, operational impacts to the data.

I've seen data / insights communicated well by Performance/Data teams when a clear 'walk' is done through the key stages leading to the insights/decisions to be made (i.e. sources used, quality considerations, analysis etc...) and 'less is more' during the final communication