You're facing conflicting data analytics and business architecture. How do you reconcile the differences?

Understanding the roots of discrepancies is like being a corporate detective. Investigate the 'data DNA' to find where the mutation occurred. Trace data lineage, scrutinize analytical models, and review architectural assumptions. Sometimes, the 'crime scene' is in the gaps between departments. Look for outdated data, flawed analytics processes, or misaligned strategic goals. Remember, Sherlock Holmes often found the most crucial clues in the most unexpected places.

Diagnosing discrepancies between data analytics and business architecture is like being a corporate detective. It's about tracing the 'data DNA' to find where the mutation occurred. Investigate data lineage, scrutinize analytical models, and review architectural assumptions. Remember, sometimes the 'crime scene' is in the gaps between departments.

Removing noise from the system is important to clearly identifying true business architecture issues. Business relevancy is the first step in identifying what data needs to be managed. Addressing data silos and issues within the silos is the second step. This allows you to create “departmental” level changes that are easier to implement while delivering low hanging benefits quickly. While the silos are working through their issues, the enterprise data and business architecture addressed to meet both the operational needs and the corporate analytical needs.

Aligning objectives is akin to tuning instruments in an orchestra. Each section (data and architecture) must play in harmony for a cohesive performance. Create a 'business symphony' where KPIs are the musical notes, shared across teams. When everyone reads from the same sheet music, the resulting melody is both beautiful and strategic.

The business architecture supports business activities that use and produce data. I would say that corporate goals and directives are important to be understood so that the architecture and data management/governance can produce the desired analytics to run the business. This is where top down and bottom up need to meet to produce credible and understandable data to manage and drive the business. Credible and understandable data allows the business to change the KPIs as it sees fit without having to rebuild or modify either the data architecture or the business architecture.

Data governance is required for credible and understandable data and its maintenance. In order to produce analytics that can be easily shared across teams. Business glossaries, business rules, and standards, all are part of establishing trustworthy data that can be used across the organization.

Implementing data governance is like establishing the rules of the road for your organization's information highway. It's not just about traffic control; it's about ensuring everyone's driving the same make and model of car. Create a 'data constitution' that both analysts and architects swear allegiance to. This common law prevents data anarchy.

To have an effective data-driven organization, the data governance processes have to include the usage of a business glossary. The definitions of metrics used in data anlaytics should be included in this glossary allowing a complete data lineage that aligns all stakeholders in where and how data is collected and maintained and how that data is used in managing the business.

Leveraging tools like Syniti's Advanced Data Migration and Management (ADMM) create a centralized platform to capture and maintain the business glossary, metadata, data standards, and business rules. To be data driven, you need an organization that is data literate and a common platform for everyone to use to keep aligned.

Continuous learning requires both organizational commitment and a platform that is business friendly and can be used by all stakeholders. This should be all a part of the data governance processes.