1 Choose the right software

When investing in VSM software, it is important to do research and compare different options. Consider the ease of use and customization, data integration and compatibility with other systems, data validation and verification features, data backup and recovery options, data encryption and access control mechanisms, data compliance and auditability features, as well as customer support and training resources. Ensure that the software meets your needs, budget, and standards.

2 Define your data sources and requirements

Before you begin mapping your value streams, you must define your data sources and requirements. Data sources are the systems, databases, or documents that provide the information you need to map your processes, while data requirements are the specifications, rules, and formats that govern how you collect, store, and use your data. To define your data sources and requirements, you should identify key stakeholders and data owners involved in your value streams; determine the scope, purpose, and objectives of your VSM project; map out the data flow and dependencies across your value streams; identify the data elements, metrics, and indicators necessary to measure your value streams; establish the data quality criteria, standards, and expectations for your data; and document your data sources and requirements in a clear and consistent manner.

3 Collect and analyze your data

Once you have defined your data sources and requirements, you can begin the process of collecting and analyzing the data. Data collection involves gathering the data from your data sources and entering it into your VSM software. Data analysis is the interpretation, validation, and transformation of the data into meaningful insights for your value streams. To ensure that data collection and analysis are done correctly, it is important to use reliable and consistent methods and tools that follow the specified requirements and formats. Additionally, any errors, inconsistencies, or gaps in the data should be checked and corrected. Appropriate methods and techniques should also be used for data analysis, as well as clear and effective visualizations to communicate the data findings.

4 Review and update your data

Data is not static, but dynamic and evolving. As your processes, systems, and environments change, so does your data, which is why you need to review and update it regularly. This includes monitoring and measuring data quality and performance, comparing results with value stream goals and targets, identifying and resolving any issues or anomalies, incorporating feedback from stakeholders and users, making changes to data sources, requirements, or methods, and documenting and communicating data changes and updates.

5 Protect and secure your data

Data is a valuable asset, but also a potential risk. If your data is compromised, corrupted, or misused, it can have serious consequences for your value streams, organization, and customers. To protect and secure your data from internal or external threats, you should encrypt data in transit and at rest, implement strong authentication and authorization mechanisms for data access, apply role-based or need-to-know access policies for data users, monitor and log data activities and events, detect and respond to any data breaches or incidents, and comply with relevant data regulations and standards.

6 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?