How can you improve transportation data quality and integrity?

2 Define your data standards

The next step to improve your data quality and integrity is to define and implement your data standards, such as formats, codes, rules, and policies. You need to ensure that your data is consistent, accurate, and meaningful across your systems, processes, and stakeholders. For example, you may want to use common data formats, such as XML or JSON, to facilitate data exchange and integration, or use standardized codes, such as GS1 or ISO, to identify and track your products, locations, or carriers. You can use data dictionaries, schemas, or catalogs to help you define and document your data standards and ensure their compliance and alignment.

3 Integrate your data systems

The third step to improve your data quality and integrity is to integrate your data systems, such as ERP, TMS, WMS, GPS, EDI, or IoT devices. You need to ensure that your data is accessible, available, and up-to-date across your systems, processes, and stakeholders. For example, you may want to use data integration tools, such as ETL or API, to automate and streamline your data flows and transfers, or use data validation tools, such as checksum or encryption, to verify and secure your data transactions. You can use data dashboards, reports, or alerts to help you monitor and manage your data systems and ensure their performance and reliability.

4 Cleanse your data regularly

The fourth step to improve your data quality and integrity is to cleanse your data regularly, such as daily, weekly, or monthly. You need to ensure that your data is correct, complete, and current across your systems, processes, and stakeholders. For example, you may want to use data cleansing tools, such as filters, rules, or algorithms, to detect and correct your data errors, such as missing, invalid, or inaccurate values, or use data enrichment tools, such as lookup tables, reference data, or external sources, to enhance and update your data attributes, such as address, contact, or status. You can use data quality reports, scores, or indicators to help you measure and improve your data quality and integrity.

5 Train your data users

The fifth step to improve your data quality and integrity is to train your data users, such as employees, partners, or customers. You need to ensure that your data is used, shared, and maintained properly and responsibly across your systems, processes, and stakeholders. For example, you may want to provide data literacy, education, or guidance to your data users, such as how to enter, access, or analyze your data, or what are the best practices, standards, or policies for your data management, or use data feedback, reviews, or suggestions to encourage your data users to report, resolve, or prevent data issues. You can use data culture, governance, or stewardship to help you foster and support your data users and ensure their engagement and accountability.

6 Review your data strategy

The sixth step to improve your data quality and integrity is to review your data strategy, such as your goals, objectives, or priorities. You need to ensure that your data is aligned, relevant, and valuable for your business performance and customer satisfaction. For example, you may want to evaluate your data outcomes, impacts, or benefits, such as how your data helps you optimize your transportation costs, efficiency, or service, or use data analytics, insights, or actions to leverage your data for your competitive advantage, innovation, or growth. You can use data benchmarks, KPIs, or ROI to help you review and refine your data strategy and ensure its effectiveness and sustainability.

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?