Big Data Analytics in Logistics and Transportation

Any business is critically dependent on information. Without up-to-date data and analysis, it loses its competitive ability and customers, which leads to a decrease in profits or financial losses.

?? A common user goes online to search for information in 60.3% of cases.

Logistics companies rely on data more than any other sector.

That is why it is critical for industry representatives to have constant access to massive amounts of information and be able to identify the data that will help the business scale and overcome challenges.

For convenience, you can use analytical tools that automatically extract information from various sources or leverage systematized data lakes and big data technology.

Today, you will learn more about why big data analytics is so important for the logistics segment, what challenges it solves, and how to work with it in general.

The Importance of Big Data Analytics for the Logistics Segment

Next year, the amount of data in the network will be about 181 zettabytes. However, the analysis does not take into account AI trends, so the final volumes will likely be much higher.

?? What do these numbers mean?

There are about 1.9 billion websites, 600 million blogs, studies, surveys, and over 6 million daily posts. All this data can be used for business analytics.

However, basic tools and techniques can't handle this massive volume of information. So, you will need data lakes and big data technologies, which can handle terabytes of information in a short period.

Advantages of Using Big Data Analytics?

Surprisingly, the benefits of implementing big data analytics are almost identical to conventional BA, including:

• Improving segment understanding.
• Reduction of operating costs.
• Increasing business efficiency.
• Optimization of processes.
• Leveling of risks.

However, the significant difference is the scale of the analysis, the detail of the results, and the wider range of applications of the knowledge gained. Let's dive into these advantages for the logistics business.

Improving Segment Understanding

The logistics industry may seem stable, but its inner workings are changing almost every day. New technologies emerge, services diversify, and methods of working with clients and marketing techniques evolve.

You need streaming analytics from reputable data sources to keep abreast and adapt to changes in time. And given the amount of information on the internet, big data analytics is what you need to stay in the loop. It provides near-instant data, so you can swiftly gain new insights and turn them into profits.?

Reduction of Operating Costs

With big data analytics, you'll be able to better manage your business processes. For example, use case studies to automate work tasks.

In this way, you reduce your dependence on resources, including employees, and thus reduce staffing costs. Also, you can reduce the number of errors in your work, accelerate the achievement of certain goals, and reach a new level of company productivity.?

Increasing Business Efficiency

Big data analytics helps you make informed decisions based on historical and current data. The main advantage is that you can accumulate information from numerous sources, structure it, and use the insights to gain a competitive advantage.

For example, you can personalize offers for customers, generate ideas for limited promotions, and use trends to improve the performance of your logistics business.

Optimization of Processes

With the insights obtained from big data analytics, you can restructure the processes, both internal and external. In particular, you can automate them like startups or more progressive market players do.?

And you already know the benefits of automating processes: order processing, demand forecasting, or building optimal supply chains.

Leveling of Risks

Here is a fact: big data analytics helps logistics businesses avoid common mistakes. It allows you to make decisions based on historical and up-to-date data. In other words, it eliminates the problems associated with mechanical inaccuracies, haste, or false assumptions.

However, in practice, the big data analytics mechanism works only when an entrepreneur makes preparations and builds the right infrastructure. I will talk about this further.?

Algorithm for Working with Big Data

Big data analytics requires a structured approach.

Here’s a roadmap to guide you:

1. Analytics architecture design.
2. Implementation of tools.
3. Validation of data sources.
4. Creation of templates and filters.
5. Adaptation of results.

First, you need to create or acquire a data lake to store the information for analysis. A typical database won't work because the volume of data is too large, and the formats aren't standardized.

Analytics Architecture Design

Firstly, you prepare a list of information sources and find cloud storage with the best rates and available capacity. You also should deploy the working environment, set up APIs, and organize a security system for data, and connections.

I recommend focusing on a relatively simple architecture with a limited number of actions that will speed up analytics processes and prevent failures.

Implementation of Tools

Next, integrate big data analytics platforms. These can be either custom solutions or systems like Microsoft BI with additional customizations.

Whenever possible, use AI-based tools with an LLM trained to analyze data in the context of its value to your logistics organization.?

Validation of Data Sources

Don't aim to collect all the information available online. Include only valuable and relevant data, verified and confirmed.

This will help maintain information purity in the ecosystem and minimize risks. In particular, it will help neutralize the influence of unverified data on the decision-making process. Otherwise, you may face financial and/or reputational losses.

Creation of Templates, Filters?

Big data analytics involves processing huge amounts of information. Therefore, it is impossible to do without robust algorithms, systems, and filters. These filters should separate valuable data from digital clutter.

This means your algorithms should be able to identify numbers, specific words, years of publication, or context. For data collection, you need analytical templates. Business analysis experts can help create them, using AI to develop effective algorithms for processing and interpreting data into reports.

Adaptation of Results

A raw dataset has unstructured information, at least if we're talking about data lakes. That's why, after completing the main research processes of big data analytics, you need to bring the results to a convenient and understandable form.

There are external tools with a user-friendly interface. They quickly transform the data, organize it, and structure it in the form of tables, documents, and infographics.

Conclusion

Big data analytics is a mechanism for handling huge amounts of information: collecting, storing, processing, and structuring it.

That's why classical methods like BA usually don't work here.

To get the most out of it, you need to:

• Build a quality analysis strategy.
• Prepare a secure cloud working environment.
• Identify reliable data sources.
• Analyze the available information.
• Reorganize the data into a convenient format.

Only then, you can rely on the results to make decisions, optimize processes, scale, or transform your logistics business.

Do you already use big data analytics in your company?

If so, please share your cases and experience in the comments!