1. Scalable processes & organization
In this video, we'll explore how scalable processes and organizational structures promote data fluency across organizations.
2. The power of scalable processes
A mature data ecosystem relies on efficient processes as much as infrastructure and tools. Scalable processes form the backbone of a data-fluent culture. They ensure that as the organization grows, the way users work with data remains efficient and effective. These processes cover everything from data collection and development of data products to insights communication and decision-making.
3. Collaboration processes
Data fluency processes often bring together data producers such as data scientists and analysts and data consumers like business leaders and decision-makers. These teams collaborate to define data requirements and generate actionable insights. Processes can establish feedback loops where data consumers provide input to data producers. This collaboration helps refine analyses and ensures that data products like dashboards, reports, and models align with the needs of the organization. Such processes encourage collaboration, clear communication channels and ensure that data insights are relevant, accessible, and comprehensible.
4. Quality control processes
Quality control processes involve the use of data validation checks and automated tests to verify data accuracy and integrity. Data that doesn't meet predefined quality standards can trigger alerts for investigation and correction. In addition, when comes to data product development, version control processes track changes made to data and code to ensure that historical data and analyses can be audited and reproduced accurately. Lastly, data product deployment processes make sure the deployment of new data products is done without issues.
5. Processes for sharing insights
Regular sharing of insights from data products requires aligning refresh schedules with business needs. This process ensures that critical information is consistently and timely communicated to relevant stakeholders. For example, a sales dashboard's data source scheduled to refresh before a 10 am sales review meeting, enabling timely decision-making. In addition, automated processes can trigger alerts or notifications when specific data thresholds or anomalies are detected in important KPIs. This timely sharing of insights enables quick responses to emerging trends or issues.
6. Process standardization
Data-fluent data producers such as analytics teams focus on the standardization of processes to enable scalability and efficiency. For example, analysts can create templates and standardized spreadsheet formats to make it easier to develop financial models and reports. This standardization enables scalability by making it easier to replicate and automate tasks, share insights, and maintain consistency across the organization.
7. The organizational structure
Besides scalable processes, organization is another important aspect of data fluency. Organization in the context of data fluency, refers to how the teams and roles are structured to enable scalable decision-making based on data-driven insights. Let’s see the main structure types of data analytics teams.
8. Centralized organizational structure
First, we have a centralized structure where data expertise is consolidated within a single team or department. This central team, often referred to as the center of excellence, is responsible for all data-related activities across the organization. This structure ensures standardization and centralized control over data practices.
9. De-centralized organizational structure
Then, there is the decentralized structure where data analysts are embedded within various business units. Each unit has its own analytics team responsible for data activities specific to its function. This structure ensures good collaboration, business understanding for the data teams and tailored insights. However, it can lead to fragmentation and inconsistency in data practices.
10. Hybrid structure for data fluency
Mature and typically larger data-fluent organizations often utilize a hybrid structure that combines elements of both centralized and decentralized approaches. In this model, there is a central data team that provides overarching data governance, sets best practices, and manages core data infrastructure. Simultaneously, data analysts while they are part of the central data analytics team, they are focused or embedded into different business units, where they work closely with domain experts to address specific departmental needs.
11. Let's practice!
Now that you understand the significance of scalable processes and organizational structures in promoting data fluency, let's put your knowledge into practice.