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Bigdata and Analytics Revolution in Transport and Logistics Industry

The transport and logistics sector plays a crucial role in the global economy, ensuring goods are delivered efficiently from one place to another. With the advancement of technology, big data has become an indispensable tool in optimizing operations and improving decision-making processes within this industry.

 

Big data and analytics help organizations optimize their operations, enhance efficiency, improve customer service, and make data-driven decisions. From delivering goods to managing complex supply chains, every aspect of this sector requires precision & efficiency. And that’s precisely where big data and analytics step in as game-changers.

 

Here are some key roles and applications of big data and analytics in transport and logistics:

 

  • Route Optimization: Big data analytics can analyze historical traffic data, weather conditions, and other relevant information to optimize delivery routes. This helps reduce fuel consumption, minimize delivery times, and lower operational costs. Also, by enabling real-time tracking and monitoring, vast amounts can be collected of data from various sources like GPS devices, sensors, and RFID tags. This way companies can have a detailed view of their supply chain network. This allows them to track shipments, monitor vehicle performance, analyze traffic patterns, and make informed decisions on route optimization.
  • Demand Forecasting & Inventory Management: By analyzing customer preferences and historical sales data along with other variables such as seasonality or promotional activities, market trends, businesses can accurately forecast future demand for products or services. This helps optimize inventory management strategies by ensuring adequate stock levels while avoiding overstocking or stockouts.
  • Predictive Maintenance: Sensors and data analytics can monitor the condition of vehicles and equipment in real-time. Predictive maintenance algorithms can predict when maintenance is needed. This helps in identifying and resolving issues quickly and enhancing overall operational efficiency.
  • Customer Experience: Analyzing customer data and feedback can help logistics companies tailor their services to meet customer needs better. This can lead to improved customer satisfaction and loyalty. Additionally, through sentiment analysis techniques applied on social media platforms or customer feedback surveys, companies gain insights into customers’ needs and expectations. These insights allow them to tailor their services accordingly which ultimately leads to higher customer satisfaction rates.
  • Risk Management: Data analytics can assess and mitigate risks associated with supply chain disruptions, such as natural disasters or geopolitical events. Companies can develop contingency plans and make informed decisions to minimize disruptions.
  • Cost Reduction: By analyzing operational data, logistics companies can identify areas where costs can be reduced, such as optimizing warehouse layouts, improving vehicle routing, and streamlining processes.
  • Regulatory Compliance: Big data analytics can help ensure compliance with various regulations, such as emissions standards, safety regulations, and customs requirements, by tracking and reporting relevant data.
  • Sustainability: Analyzing data related to fuel consumption and emissions can help logistics companies reduce their environmental impact and meet sustainability goals.
  • Market Intelligence: Data analytics can provide valuable insights into market trends, competitor activities, and customer preferences, helping logistics companies make strategic decisions and stay competitive.
  • Capacity Planning: By analyzing data on shipping volumes and resource utilization, logistics companies can plan for future capacity needs, whether it involves expanding their fleet or warehouse space.

 

Challenges of Using Big Data in Transport and Logistics

 

  • Data Integration: One of the major challenges in using big data in transport & logistics is integrating various sources of data. The industry generates massive amounts of information from multiple channels, such as GPS trackers, sensors, weather forecasts, and customer feedback. However, this data often exists in different formats and systems, making it difficult to integrate and analyze effectively.
  • Data Quality: Ensuring the accuracy and reliability of the collected data poses another challenge. With numerous variables involved in transportation operations, there is a risk of incomplete or inconsistent data sets that can lead to misleading insights or flawed decision-making.
  • Privacy Concerns: As big data analytics rely on collecting vast amounts of personal information about individuals’ movements and behaviors, privacy concerns arise within the transport and logistics sector. Companies must adhere to strict regulations regarding consent, storage security, anonymization techniques, and user rights protection.
  • Scalability Issues: Dealing with large volumes of real-time streaming data requires robust infrastructure capable of handling high velocity processing. Scaling up existing systems to accommodate increasing volumes can be complex and costly for organizations.
  • Skilled Workforce: Building a competent team with expertise in big data analytics is crucial but challenging due to its niche nature. Finding professionals who possess both technical skills (data mining techniques) as well as domain knowledge (transportation operations) may prove difficult.
  • Technology Adoption: Embracing new technologies like IoT devices or cloud computing for effective collection and analysis presents implementation challenges for traditional transportation companies that may have outdated infrastructure or resistance to change.
  • Data Security: Protecting sensitive information from unauthorized access remains a critical concern when dealing with big datasets containing valuable business intelligence that could be exploited if not adequately protected.

Addressing these challenges requires collaboration between stakeholders to develop innovative solutions tailored specifically for the transport industry’s unique needs.

In summary, big data and analytics are transforming the transport and logistics industry by providing valuable insights, optimizing operations, reducing costs, improving customer service, and helping companies stay competitive in a rapidly changing environment. This data-driven approach is becoming increasingly essential for success in the industry.

Data Management: Cost of poor data quality

Organizations are collecting and generating more information/data than ever before. This information/data is used in almost all activities of companies and constitutes the basis for decisions on multiple levels. But, simply having a lot of data does not make a business data-driven, because issues related to data quality maintenance are infecting numerous businesses. Companies are witnessing that not only the data is growing rapidly in scale & importance but also in complexity. The topic of data quality and what companies should do to ensure a good level of data is one of the biggest priorities within companies that are always being worked on. Since poor data quality affects, among other things, business processes, it can lead to wrong decisions and make it more difficult to comply with laws and guidelines (compliance).

 

Organizations around the world gather so much data that sometimes it’s impossible for them to differentiate the valuable and outdated or inaccurate data. Studies have also shown that the data stays stuck in different systems in inconsistent formats, which makes it unreliable or impossible to share with other team members. According to Gartner’s research, “the average financial cost of poor data quality on organizations is $9.7 million per year.” In other words, the cost of poor data quality is 15% to 25% of revenue.

MASTER DATA MANAGEMENT

Having quality data means getting the right answer to every question. This requires that data is constantly checked for errors, redundancy, and usability. In addition to avoiding errors and gaps, it is also about making data available to every concerning person in a uniform way and making it as easy to use as possible. Master data management (MDM) helps companies to ensure that their data is accurate, trustworthy, consistent, and shareable across the enterprise and value chain by enabling greater data transparency & empowering you to drive better decisions, experiences, and outcomes that benefit your business and your customers.

 

Basically, master data management creates added value on two levels: on the one hand in the administrative areas, for example through more efficient master data maintenance processes or also in IT projects; on the other hand, through increased transparency in the operational areas and thus improved controllability. The benefit in mastering data processes is reflected, among other things, in the reduced effort involved in searching for data, less internal coordination effort, and the fact that there is no duplication of work when changing data or making initial entries. Furthermore, clean master data forms the basis for scalable automation options and reduces the effort for migrations.

 

Mastering your data challenges also delivers a significant competitive advantage. And as the pace of innovation accelerates, the importance of mastering your data will only be beneficial for your business. The benefits of MDM in the administrative and operational areas as well as for compliance ultimately increase the competitiveness of companies. Last but not least, good data quality ensures the satisfaction of customers, suppliers, and employees.

Top Strategies to Improve and Increase Data Quality

Top Strategies to Improve and Increase Data Quality

 

Organizations face enormous amount of pressure when it comes to face the issue related to data quality. Businesses can only make the right data-driven decisions if the data they use is correct. Without sufficient data quality, data is practically useless and sometimes even dangerous.

 

Regardless of whether your data is structured or unstructured or your data is on-premises or in the cloud, it needs to be on top to deliver business value by ensuring that all key initiatives and processes are fueled with relevant, timely and trustworthy data. Because bad data quality not only costs time and money, in the worst case, it even leads to significant revenue losses.

 

But despite its importance of having data quality, the reality in many of today’s organizations, data quality has been voted among the top three problems for BI software users every year since the first issue of The BI Survey back in 2002.

 

What is data quality?

Defining data quality depends on the needs of each organizations. It can differ from one business to another. As a poor quality of data, especially of customer data, quickly leads to serious problems, therefore for some organizations, it can be ensuring that customer contact data is up to date so that deliveries are received in a timely manner. For other organizations, it could be filling prospects profiles that can be helpful with marketing segmentation effort. Serval factors are being used to determine the quality of data, such as accuracy, completeness, relevancy, validity, timeliness and consistency.

 

Here below are few examples to clean up and improve the consistency and reliability of your data:

 

  • Understand the purpose of your data

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IT department should work with other departments of company to align and acknowledge the problems and negative impact that company can face because of missing or erroneous data. Even though a lot of data today are generated, companies must make a strategy about what data is been collected and for which purpose the gathered data can be used because the collected data should ultimately exist for a business or mission purpose.  For this purpose, they must work to identify incomplete, faulty or multiple existing customer data, because very often, in different departments, different inventory data exists for the same customers. So, paying attention to an error free data can lead to increase data quality.

 

  • Get a Data Control Supervisor from a Qualified Department

Data Control supervisors play a crucial role in the success of a data quality mission. They come from a specialist department and know how to oversee the development and use of data systems. They can discover efficient ways to organize, store and analyze data with attention to security and confidentiality. He is also responsible for creating and enforcing policies for effective data management, formulating management techniques for quality data collection to ensure adequacy, accuracy and legitimacy of data, devising and implementing efficient and secure procedures for data management and analysis with attention to all technical aspects. His goal is to ensure that information flows timely and securely to and from the organization as well as within.

 

  • Implement a priority list of undesirable data

Today many companies are using different equipment (IOT) that records vast volumes of sensor data. Unfortunately, not all the gathered data in company is valuable. Therefore, Data Control supervisor must perform quality checks in order to reject undesirable data. To do this, he must be able to respond to following questions: How and by whom was the data generated? Which users are accessing it? For what purposes are they used by which applications? Which costs cause faulty data?

 

  • Prevent duplicate data

Duplicate data refers to when the same information is somehow input as two separate entries by different people or teams

In the presence of duplicate data, it is very hard to pull out exact results or CRM and Marketing campaigns and can create serious issues when you’re creating automations, looking up buying patterns, or putting together a target audience. So, Data Control supervisors must make sure that company is using a data management software that regularly checks the data for duplicates and cleans it to ensure that their data is clean, has quality, and is reliable to work with.

 

  • Perform regular checks on your data to uncover anomalies

If you want to understand and ensure your data quality, you have to perform regular checks to see if there’s no “bad-data”. Reviewing your data will help you to understand if the gathered data aim for organisations objectivity. As getting 100% data accuracy is not the final objective, Data Control supervisors must be able to pull-up the insights from the data to it’s main goal. Improving data quality is an ongoing process and it takes time to get it right.

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