How do you manage the complexity and diversity of workflows in your analytics environment?

1 Define your workflow goals

Before you start any workflow, you need to have a clear idea of what you want to accomplish, why, and how. This will help you align your workflow with your business strategy, prioritize your tasks, and measure your progress. You should also define your workflow scope, inputs, outputs, and deliverables, and communicate them to your team and stakeholders. Having a well-defined workflow goal will keep you focused and motivated throughout the process.

4 Test and validate your workflow results

Testing and validating your workflow results are essential steps to ensure the accuracy, reliability, and relevance of your analytics insights. You should test your workflow at different stages, such as data quality, data transformation, data analysis, and data visualization, and use appropriate methods and metrics to verify your results. You should also validate your results with your stakeholders, and solicit their feedback and suggestions. Testing and validating your workflow results will help you avoid errors, biases, and inconsistencies, and enhance your credibility and trustworthiness.

5 Share and reuse your workflow assets

Sharing and reusing your workflow assets can help you save time, resources, and effort, and foster a culture of collaboration and learning in your analytics environment. You should document and store your workflow assets, such as data sets, code, scripts, models, reports, and dashboards, in a centralized and accessible location, and use standard formats and naming conventions. You should also share your workflow assets with your team and other users, and encourage them to reuse and modify them for their own purposes. Sharing and reusing your workflow assets will help you leverage your existing knowledge and skills, and create new value and opportunities.

6 Review and optimize your workflow performance

Reviewing and optimizing your workflow performance is a continuous process that helps you identify and address the strengths and weaknesses of your analytics workflows. You should monitor and measure your workflow performance using relevant indicators, such as speed, quality, cost, impact, and satisfaction. You should also analyze and compare your workflow performance with your goals, expectations, and benchmarks, and identify the gaps and areas for improvement. You should then implement the necessary changes and enhancements to your workflow design, tools, methods, and results, and evaluate their effects. Reviewing and optimizing your workflow performance will help you achieve higher levels of efficiency, quality, and value in your analytics workflows.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?