Mining data for actionable insights can help businesses reduce operational costs, eradicate inefficiencies, and eliminate risks. But these benefits can only be realized if there’s a process for transforming data from its raw form into actionable insights, and if the data used in that process is clean and accurate.
Shippers enjoy access to a massive volume of logistics data, but that data is often received from many different sources, each using its own taxonomies and terminology. The data may be inconsistent, inaccurate, or incomplete, leading to complicated data structures and overwhelming processing initiatives that don’t guarantee accurate reporting.
Predictive analytics on trusted and reliable logistics data reveal hidden opportunities for cost savings and service enhancements across the supply chain. But without thoughtful processes, managed by qualified experts to ensure the trust and confidence of data you rely on for analytics, you may be making inappropriate decisions that could do more harm than good.
Data refinery for logistics provides a solution to these problems. It enables shippers to derive accurate insights from raw logistics data by processing, validating, standardizing, and correcting it. It captures the lineage of your data for transparency to sources and the quality of processes used to clean it.
Clean data enables shippers to confidently identify and resolve business problems, recognize and repeat innovative solutions, and drive business initiatives forward.
What is a Data Refinery?
A data refinery is a tool for simplifying, structuring, and correcting large data sets. It processes data from various sources, standardizes the structure of data, corrects errors to ensure accuracy, and exports meaningful information that can be used for data-driven decision-making.
In a data refinery process:
- Data is received in its raw form from multiple pipelines and various sources.
- Raw data is collected and stored in a central repository.
- A data refinery collects the raw data and refines, processes, corrects, and stores it.
- An audit trail is established, showing the sources of data and measuring the quality of the methods used to refine it.
- Dedicated data scientists proactively manage the quality and consistency of data refinement methods for continuous improvement.
- Trusted data is delivered through a user-friendly interface via report, query, dashboard, or other visualization.
There are two major benefits of using a data refinery for processing and accessing data:
- Reports are generated much more quickly than those queried from raw, unprocessed data, because data is processed and standardized long before it’s requested. This eliminates time-consuming transformation and computation processes, and enables real-time reporting.
- Reports are clean, accurate, and reliable because data refineries use both machine-learning and human intervention to process and categorize data, and to identify and correct errors. Over time, the refinery learns how to handle various taxonomies, and how to identify and correct inaccuracies and omissions.
The data refinery allows businesses to quickly derive meaning from massive amounts of data, and have confidence that the insights gathered are based on accurate and complete data sets.
The Advantages of a Data Refinery for Logistics
Shippers receive an exploding volume of unstructured logistics data from customers, vendors, and suppliers. The format of the data varies from source to source, leading to massive stores of inconsistent, inaccurate, and incomplete data. This is complicated further by acquisitions, mergers, and expansions.
Making decisions from that raw data is a fruitless exercise. If the data used to make the decision is inaccurate, the result of the decision is likely to be ineffective.
With the data refinery, logistics companies can finally make use of their data by standardizing it automatically, and identifying/resolving errors and omissions programmatically. Audit trails demonstrate the quality of data refinement methods applied. Dedicated data scientists proactively monitor data quality of key concepts for continuous improvement.
The data refinery allows shippers to form a clear picture of logistics performance:
- Forecast Demand – Accurate data leads to accurate forecasts. Use historical data to anticipate future inventory and labor needs. This eliminates wastes like unnecessary labor costs and inventory surpluses, reducing overall operational costs.
- Eliminate Delays – Trusted outputs from the data refinery can be mined to identify patterns that result in delays. With these patterns identified, delays can be minimized or eliminated, leading to reduced costs and improved customer satisfaction.
- Reduce Overhead – Trusted data can expose expensive, unnecessary, or inefficient processes so that they can be eliminated or revised. The data can also be used to form hypotheticals for alternative options to reveal hidden savings opportunities. Those savings can improve profitability or be passed on to customers, allowing you to attract more business.
- Enhance Control — Reliable data will accurately identify and quantify control risks in your logistics spending more quickly and easily. This may include invoices that are not your liability, overcharges on contracted services, services that were never delivered or waived in the contract, and potential duplicate payments.
- Identify Improvement Opportunities – Refined data is available in real-time, allowing it to be mined consistently for new insights. This allows you to monitor the health of operations over time; to seek constant improvement, savings, and solutions; and to identify critical risk factors before they become major problems.
Additionally, a data refinery creates a central source of truth for business data. Individuals, managers, and leaders throughout the company have access to the same data, and they can use that data to communicate issues, brainstorm solutions, and recommend opportunities for growth.
Using a Data Refinery for Continuous Improvement
Logistics processes are notoriously complex, guided by multiple touchpoints, global operations, and strict regulations. Unstructured data from many sources, with varying degrees of quality and consistency makes intelligent oversight difficult. But complicated business processes don’t have to equate to complex and unusable data.
With the data refinery for logistics, data-driven decision-making can become the simplest part of your business.
At Trax, we maintain a data refinery for logistics that is tailored to delivering analytics solutions for businesses that rely upon the global logistics industry. Let us guide you to informed decisions and continual improvement with clean, accurate, trusted, and reliable data. We’ll use the data refinery we’ve developed from decades of experience with thousands of global service providers to enable analytics solutions you can use with confidence to grow your business, enhance control, and streamline operations.