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Why Centralized Data Management in Logistics is Essential?


Data Management in Logistics article cover.

It’s no longer a question of whether to integrate data across your wider networks but how to leverage centralized data management in logistics to gain a competitive advantage. 

Fragmented and incompatible data adds time and weight to logistics operations decisions that today’s customers simply don’t have the patience for. Centralized data is non-negotiable to support automated tasks, data reusability, data sharing, and the implementation of other logistics technologies. But what centralized storage systems are logistics teams using?

Data Lakes and Warehouses, What’s the Difference?

Data management in logistics can be tricky. Let’s clarify some terms:

  • Big data—logistics operations’ structured, semi-structured, and unstructured data collection ready to be mined and utilized in advanced analytics applications, such as machine learning and predictive analytics.
  • Data lakes—for permanent storage of large data volumes. Structure of data? Any. There is no set limit on the size of an account or a file, and there is no established use for it yet. You can query data from the data lake as needed. Purpose? Rapid storage. End customer? Data scientists and engineers. Keep in mind: Without the proper care and attention, a data lake may become a swamp of worthless, disorganized data with no clear source or ID. In addition, since the data lacks structure, it’s easy to access and modify it. For this reason, there must be frequent data governance.
  • Data warehouses—also permanently store massive volumes of data. Structure of data? Processed, formatted, filtered, and organized. Purpose? Inventory management, order fulfillment, and fleet distribution. End customer? Logistics and operations teams—although it’s likely finance and marketing will use elements of the data too. Keep in mind: Difficulties can arise when departments seek multiple data uses—impacting the data storage structures and standards. What’s more, structural restrictions make data warehouses complex and costly to alter. It necessitates ongoing cleansing, transformation, and data integration.

To make a simple parallel: Imagine a lake (your storage space), and you throw in a floaty (structured data), armbands (semi-structured data), and goggles (unstructured data). A dam, that opens on demand, connects the lake to a river (an application) letting those first two objects glide through. While the goggles—that sunk to the bottom—didn’t float alone, a diver (a data scientist) could easily swim across and collect them. 

In a warehouse, things are a little different. Once the objects are stacked above each other, moving around takes some time to get what you need from the bottom. Nevertheless, with some pre-determined structure, warehouses, too, can easily deliver parcels of structured data—or floaties—between one another.

Benefits of the Data Storage Systems

This image explains what is the difference between main types of centralized data management in logistics: Data Lake vs. Data Warehouse

When to Use a Data Lake?

Data lakes exist to capture all your data, even if it doesn’t have a purpose yet, enabling time travel for data scientists. Raw data is kept in its original format, suitable for complex data such as the Internet of Things (IoT) device logs and various photo, video, and audio formats.

They can also incorporate transactional, structured data from ERPs and customer relationship management (CRM). Once the information has a purpose, data scientists can also set the data’s structure and organization to move it to a data warehouse.

When to Use a Warehouse?

A data warehouse puts great effort into selecting the data it will eventually store before storing it. The stored data’s processed nature makes it much easier to comprehend—good for front-end systems used by non-tech employees.

It receives data from relational databases, transactional systems, and other sources and stores it across several organized, individual files. Then it is accessed through SQL clients and applications such as business intelligence (BI) tools and ERPs.

Why Centralized Data Management in Logistics is essential?

Both lake and warehouse storage strategies help centralize data management in logistics so that various business units may use it for analysis and insight-gathering. The good part about data lakes is that they allow IT teams to pick and choose the different metadata, storage, and computing technologies they wish to deploy based on the demands of their systems. They are the do-it-yourself equivalent of a data warehouse with much more flexibility.

  • No more data silos: A centralized data lake provides seamless use and access from multiple authorized stakeholders, removing data duplication and creating a single version of the truth.
  • Foundation for advanced analytics: As they store various datasets and formats, data scientists can train AI and ML models with more expansive sets of complete data. 
  • Quality data and real-time decision-making: Data lakes can leverage large data quantities, build a solid ground for data cleansing, and deep learning algorithms to provide real-time decision support.
  • Scalability: The versatility of storing structured and unstructured data from multiple sources makes data lakes extremely scalable. They are cheaper to scale too.