How can you maintain contract data quality and governance through analysis?

1 Identify data sources and standards

The first step to maintain contract data quality and governance is to identify the data sources and standards that you need to follow. Data sources are the places where you collect, store, and update your contract data, such as databases, spreadsheets, or contract lifecycle management (CLM) systems. Data standards are the rules and guidelines that define how your contract data should be formatted, structured, validated, and documented. For example, you may have data standards for naming conventions, data types, data formats, data models, data dictionaries, and metadata. You should map out your data sources and standards and align them with your contract management objectives and requirements.

2 Perform data quality audits

The next step is to perform data quality audits to assess the current state of your contract data quality and governance. Data quality audits are systematic processes that check your contract data against your data standards and identify any issues or gaps. Data quality audits can help you measure various dimensions of data quality, such as accuracy, completeness, consistency, timeliness, relevance, and security. You can use various methods and tools to perform data quality audits, such as data profiling, data cleansing, data validation, data quality dashboards, and data quality reports.

3 Implement data quality improvement actions

The third step is to implement data quality improvement actions to address the issues or gaps that you found in your data quality audits. Data quality improvement actions are specific steps that you take to correct, enhance, or prevent data quality problems. For example, you may need to update, delete, merge, or enrich your contract data, or you may need to implement new data quality rules, policies, or procedures. You should prioritize your data quality improvement actions based on the impact and urgency of the data quality issues and track the progress and results of your actions.

4 Establish data governance roles and responsibilities

The fourth step is to establish data governance roles and responsibilities to ensure that your contract data quality and governance are maintained and monitored on an ongoing basis. Data governance is the framework that defines who can access, use, modify, and share your contract data, and how they should do it. Data governance roles and responsibilities assign specific tasks and accountabilities to different stakeholders in your contract management process, such as data owners, data stewards, data custodians, data analysts, and data users. You should clearly define and communicate your data governance roles and responsibilities and ensure that they are aligned with your contract management goals and strategies.

5 Use data analysis to generate insights and value

The final step is to use data analysis to generate insights and value from your contract data. Data analysis is the process of transforming, modeling, and interpreting your contract data to answer questions, solve problems, or make decisions. Data analysis can help you gain a deeper understanding of your contract performance, risks, opportunities, and trends. You can use various data analysis techniques and tools, such as descriptive, diagnostic, predictive, and prescriptive analytics, data visualization, data mining, and business intelligence. You should use data analysis to support your contract management activities and outcomes, such as contract negotiation, contract compliance, contract renewal, and contract optimization.

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?