These days, we have so much data in transportation & logistics. How do we turn this data into KPIS that make it more manageable and more importantly, make it actionable.
In this webinar, Metafora VP of Technology, Steven Godfrey chats with Bill Zenk, Principle & Practice Leader from TrueNorth Risk Solutions Group and Megan Thirtyacre, TrueNorth Data Analytics & Business Intelligence Lead. They discuss data strategy, data management, industry challenges with data, and Metafora's integration platform Socket.
In case you missed it, or just want a refresher, you can catch the full webinar hosted by TruthNorth Companies below, and we're sharing some of the key takeaways in this article.
What is Data Management?
Data Management is the practice of collecting, protecting, and storing an organization's data so it can be analyzed for business decisions.
Data Strategy
An organization’s data strategy ties into your company’s overall business strategy and tech strategy and includes everything from the roles that support it to the architecture that organizes it and the processes that manage it.
We organize data strategy into these four pillars:
1. Business Strategy
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How is the business going to use the data? Define how your data strategy will reinforce and advance your business strategy.
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What are the key objectives? What are the KPIs and metrics that will need to be addressed? A quality data strategy aligns to measurable business objectives in both short and long term.
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You’ll want to periodically review your data strategy and business strategy to ensure continued alignment.
2. Organizational Roles
- Data strategy is not only an IT responsibility! It encompasses the entire organization
- Defines key stakeholders and roles and responsibilities across the entire organization
- Avoids data duplication
- Define data owners
3. Data Management
- Defines data governance, stewardship, quality, master data and data integration
- Focuses on Master Data Management (MDM) to create single, primary records
- Consistent view of business critical data
4. Data Architecture
- Defines the tools and processes that will be used to manage and analyze data
- Cloud services make this easier than ever using data ingestion to centralize storage and analysis
Data Management
Data management is about how to support the data in your environment and how to optimize the use and efficiency of the data. You want to make sure that data that is coming in or already lives in your environment is being used effectively.
Data Governance and Stewardship
- Management and oversight of data in an organization
- Ensures data is accessible, usable, and trusted
- Ambassadors of data for the organization
Data Quality
- Ensures data is complete, consistent, and accurate
- Normalization is often necessary as data originates from many different sources. You want your data to be in a similar, usable format. This ensures that data can be analyzed effectively.
- Provides timely access to data
Data Integration
- Provides the connection from the source system data to the target systems
- Opportunity to enrich data to improve its value
- Reduces errors to avoid rework and increases efficiency
- You want to automate integration as much as possible.
Data Architecture
Data architecture is equally simple and complex. Data architecture is focused on three things:
- Source system: Getting data out of the source system and into a data repository or a transition system. This could be through a change data capture, legacy data from mainframes, ETL, APIs. There are a lot of tools that can be used there.
- Proposed system: This refers to data storage. This could be a MDM, a data lake, a data warehouse.
- Downstream Applications: This is where you get into analytics, visualization, and reporting. This is where you really can discover insights that enable you to make data-driven decisions.
What are some industry challenges that can be tackled with quality data management practice?
- Data Silos: Data is everywhere! Many transportation companies have been built on and grown from acquisitions. Technology has often kept the silos divided rather than bring them together.
- Data Transformation: Bringing the data together is not easy. Ensuring data quality and planning for normalization and transformation is a huge challenge.
- Managing Integrations: The cost of managing enterprise integrations is high and increases friction of expansion and growth. If you have a lot of disparate systems and are thus having to manage a lot of one-to-one integrations, this constrains scalability.
How can we address these data challenges in transportation & logistics?
Metafora developed Socket to directly address many of the issues with integrations in transportation and logistics, and ultimately to enable carriers, brokers, shippers, and tech vendors to make better use of their data.
Socket is a configurable platform developed to enable easier, faster integration between shippers, brokers, and carriers, and technology providers while offloading maintenance and management of the integrations.
With Socket, you get to offload these otherwise time-consuming tasks, so you that you can focus on the outputs of your data: the visualization, the insights, the stuff that really matters.
What are some examples of the data that Socket can help you connect from your disparate systems?
Loss Analysis Data
Accidents per million miles (MM)
By company
By location
Loss frequency by type
Loss severity by type
Driver Turnover Analysis Data
Driver Turnover Ratio
By company
By location
Drivers added
Drivers termed
Revenue per Truck Data
By company
By location
By month
Total revenue
Compliance, Safety, Accountability (CSA) Analysis Data
Violation points per truck by location
# of violations
# of inspections
