As many third-party logistics (3PL) companies transition to a data-driven approach, it’s essential to underscore the importance of your data management practices. How you organize and store data impacts everything from how fast you can access information to which metrics are available. Many data-forward 3PL companies have begun implementing a data vault model to address this strategic decision. The data vault model allows them to address industry-wide challenges such as disparate data, lack of visibility into what is happening, reworking of analytics when acquisitions occur, and slow retrieval or transfer of information.

To assist you in determining the best possible way to organize your data, we will outline the benefits of a data vault model for 3PLs and highlight four use cases to illustrate the benefits for better decision-making.

What is a Data Vault?

A data vault model is known for its practice of separating your data’s primary keys, relationships, and attributes from each other. Let’s say you want to analyze which customers are moving the most loads through you. The relationship between the customer and the load would be stored in one table, while the details about each load and customer would be stored in two separate, but related tables.

Structuring data in this manner helps you account for changing relationships within your data and seamless integration of new data sources when acquisitions or business rules inevitably change. Additionally, it enables quicker data loading through parallel streams and automatically stores historical data. For more details on what a data vault model is and the benefits it provides, check out this blog by 2nd Watch.Data Vault Model for 3PLs

Streamlined Data Warehousing: Simplifying Centralized Data with Data Vault

Data vault makes it easier to build a data warehouse with accurate, centralized data. The built-in relationships between data vault entities (hubs, satellites, links) make it easier to build a data warehouse. Structuring your data model around flexible but integrated primary keys allows you to combine data from various source systems easily in your data warehouse. It helps you ensure the data loaded into your reporting is not duplicated or out of date.

A lack of a data governance strategy often means that reporting is inconsistent and inaccurate. It reduces executives’ visibility into departments throughout the organization and limits your ability to create effective reporting because data is disjointed. Implementing a data vault model inherently accounts for centralizing your source data and enforcing primary keys. This will not only allow you to offer better reporting to customers, but it has also been found that accurate data is key to shipping accuracy. A strong data warehouse will further your internal analytics abilities by unlocking dashboards that highlight key metrics from revenue to cost-per-pound or on-time performance.

Dynamic Data Integration: Adding Sources, Updating Rules with Data Vaults

Data vault models make it easy to add new data sources and update business rules without interrupting access to data. A data vault model enables you to centralize data from various sources, while still addressing their differences such as load frequency and metadata. This is accomplished by storing the primary keys for an entity in one table, then creating attribute tables (satellites) specific to separate source systems.

Under a traditional model, most of this data would be held in one table and would require changes to the table structures, and therefore interruptions to data in production, each time a new source system is added. A scalable data model, like data vault, allows you to quickly adjust data delivery and reporting if your customers expand to new markets or merge with another company. Not only will this satisfy your current customers, but it is additionally a quality many logistics companies seek when choosing a 3PL partner. Accommodating multiple source systems and implementing business rules flexibly is key for any 3PL company’s data solution.

Accelerated Data Access: Parallel Loading with Data Vault Models

Data vault models allow for parallel loading, which gets you and your customers access to data faster. Data vault separates its source systems and data components into different tables. In doing so, it eliminates dependencies within your data and allows for parallel loading, meaning that multiple tables can be loaded at once rather than in a sequence. Parallel loading dramatically reduces the time it takes to access refreshed data.

Many 3PL companies offer customers access to high-quality reporting. Implementing a data vault model to load data quicker allows customers to gain insights in near-real-time. Furthermore, key metrics such as order accuracy, return rates, and on-time shipping percentage rely on timely data. They either require you to respond to a problem or could become inaccurate if your data takes too long to load. The faster you access your data, the more time you have to address your insights. This ultimately enables you to increase your accuracy and on-time shipments, leading to more satisfied customers.

Challenges and Considerations of Implementing a Data Vault Model for 3PLs

While implementing a data vault model offers numerous benefits for third-party logistics (3PL) companies, it is important to consider the potential challenges and factors that need to be addressed. Here are some key considerations to keep in mind when implementing a data vault model for your 3PL operations:

  1. Initial Investment: Implementing a data vault model requires an initial investment in terms of infrastructure, software, and expertise. It is essential to allocate resources and budget accordingly to ensure a smooth implementation process.
  2. Data Governance and Data Quality: The success of a data vault model relies heavily on effective data governance and data quality management practices. Establishing robust governance processes, data standards, and data cleansing routines is crucial to maintain data accuracy, consistency, and reliability within the data vault.
  3. Impact on Existing Systems and Processes: Introducing a data vault model may require modifications to existing data systems, processes, and workflows. It is important to carefully plan and assess the potential impact on current operations, including data integration, ETL processes, and reporting mechanisms. Adequate testing and validation should be conducted to ensure a seamless transition and minimize disruptions.
  4. Scalability and Future Compatibility: Consider the scalability of the data vault model as your 3PL business grows and new data sources are introduced. The model should be designed to accommodate evolving business requirements, additional data volumes, and changing data sources. It is also important to assess the compatibility of the data vault model with emerging technologies and future advancements in data management and analytics.
  5. Expertise and Training: Implementing a data vault model requires expertise in data modeling, database administration, and data integration. Ensure that your team or organization has the necessary skills and knowledge to effectively design, implement, and maintain the data vault. Providing training and continuous learning opportunities can help maximize the benefits of the data vault model.

Careful planning, collaboration, and ongoing maintenance are essential to unlock the full potential of a data vault model and drive better decision-making within your 3PL organization.

Data vault models automatically save historic data required for advanced analytics

Whether you are looking for more advanced forecasting or planning to implement machine learning analytics, you will need to rely on historical data. Satellite tables, mentioned previously, store attribute information. Each time a feature of an order, a shipment, an employee, etc., changes, it is recorded in a satellite table with a timestamp when the change occurred. The model tracks the same information for changing relationships. This data allows you to automatically tie larger events to specific attributes involved when the events occurred.

3PL companies without data vault models often lose this history of attributes and relationships. When they pursue initiatives to find nuanced trends within their data through advanced analytics, their implementation is roadblocked by the task of generating adequate data. Alternatively, 3PL companies with a data vault model are ready to hit the ground running. Having historical data at your fingertips makes you prepared for any advanced analytics strategy.

2nd Watch has vast experience integrating 3PL companies’ key financial and operational data into a centralized hub. This immediately enables quick, reliable, and holistic insights to internal stakeholders and customers. Furthermore, it lays the groundwork for advanced predictive analytics that allow your teams to proactively address key industry challenges, including late deliveries, volatile market rates, and equipment failure.

Reach out to 2nd Watch for assistance getting started with data vault or evaluating how it may fit in with your current data strategy.