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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.

Master Data Strategy: How to achieve greater operational efficiency and improve the customer experience?

Master Data Strategy How to achieve a greater operational efficiency and improve the customer experience

Without a doubt, the corona pandemic has led to a holistic rethinking in many areas of the company. Companies have implemented solutions that make their employees work easier, help them to reduce overall costs, and improve existing business processes and their customer’s experience in parallel. All this can’t be done without good master data. Master data is at the heart of all operational processes. Sourcing, product development, manufacturing, shipping, marketing, and sales all depend on the ability to efficiently collect, manage, and share trusted data on time.

 

Master data management also helps to automate and control error-prone manual processes, enable transparency and insights to make better operational decisions and so organizations can improve the quality of products and services, and accelerate time-to-market.

In order to achieve increased productivity, profitability, and business performance while reducing costs, one must not ignore the quality of the master data, regardless of whether it is customer master data, supplier master data, or article master data. Only superior quality data has a decisive influence on the efficiency of business processes and the quality of corporate decisions. Outdated, incorrect, or missing master data can lead to a loss of sales or weaken the reputation of the customer or supplier.

 

What mistakes can one make in master data management?

 

Management is not involved

Without the support and coordination with the management, the master data management project is doomed to failure. The support of the management right from the start is the only way to dissolve cross-departmental thinking. The senior management officer must ensure that the project team can not only streamline the management of data across departments but also that business processes and procedures can be adjusted across departments if necessary. Such huge changes are rarely received positively, so effective communication in change management is necessary.

 

Master data management is not an IT issue

Master data management is not a technical challenge or problem that only the IT department can solve. This topic must be addressed by the specialist departments. Only the various specialist departments know the content-related requirements for correct and up-to-date data. And they know their own business processes in which the various data are generated or changed. IT can help with the selection and the implementation of MDM solutions, but the specialist departments must take on the technical part here.

 

The long-term vision of the MDM project

As with any project, the MDM project also needs good management within the organization based on a correct goal matrix and a long-term vision for data management. However, this must not tempt you to create the scope of the project in such a way that it is no longer possible to carry it out quickly and efficiently. Agile project management makes it possible for achieving the goals step by step. With an unrealistic project scope, the entire project can quickly fail, and you end up with no result. Most of the time an experienced project manager, possibly external, can help get the project off the ground.

 

Organizational and cultural changes are ignored

No matter how good the project, the goals, and the vision, it will fail if all the different parties in the organization are not brought on board. Those affected and opinion leaders play a key role in the success of the project. The project team often gambles away its own success by doing everything in a quiet little room and in the end, everyone is surprised by the new solution, the result = is rejection. Good change management communication to the affected groups is an essential component of building awareness and support for organizational change and achieving long-term success.

 

The goal of mastering data management is the optimization, improvement, and long-term protection of data quality and data consistency. The main problem is when the master data is stored redundantly in different databases. This leads to time-consuming and costly data comparisons or the introduction of a central MDM system that, as a central data hub, provides the data for all other systems.

Convention 108:  The European Data Privacy and Protection Day

Data Protection The European Data Privacy Day

Last Tuesday, January 28th, the European (annual) Data Privacy Day, 40th anniversary of Convention 108 &15th Data Protection Day, was celebrated among the global Data Protection community.

After many years of negotiation, the Convention for the protection of individuals with regard to automatic processing of personal data (Convention 108) was open for signature on 28 January, the date on which we now celebrate Data Protection Day. This day of action and information is launched following the initiative of the Council of Europe and aims to raise awareness of the handling of personal data. The focus is on data protection, so that as many companies, authorities, associations and people who deal with data protection as possible become clear about the importance of data protection.

 

In today’s digital society, user’s personal data is being collected either by companies or by the government. Especially with the emergence of Big Data, governments and businesses are able to collect and process large amounts of data, leaving millions of people unaware and uninformed about how their personal information is being used, collected or shared. Thus, each year a large number of organizations worldwide support this day of action in regard to protect consumer’s fundamental rights and freedom and to sensitize every European for the privilege of protecting their own privacy.

 

Here below are few tips from on how users can protect their online privacy and devices:

 

  • Moving through everyday life completely anonymously is next to impossible these days. Whether on the phone, shopping in the supermarket, booking a vacation, visiting the doctor or surfing the web – a lot of personal data is gathered. Partly obvious, but often even without our knowledge. Therefore, if you visit a web page where you have to enter sensitive information, make sure that the address in the browser matches the page you are trying to access. If the URL is a random arrangement of letters and numbers or looks suspicious, do not enter any information.

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  • Use a VPN and make sure that you remain anonymous online. A VPN helps you to hide your IP address and gives you anonymous access to the Internet. Even tough your data is visible, a VPN encrypts it entirely, routes your connection through secure remote servers, and masks your IP address. With its protective shield, your online data is difficult to crack and almost impossible to track.
  • Implement a Multifactor authentication (MFA) / Two-factor authentication (also known as 2FA). It’s an authentication method in which a user is granted access only after confirming the identities required of users by combining two different factors: something they know, something they have or something they are. If at least one of the components is missing or incorrectly specified during an authentication attempt, the identity of the user will not be established with sufficient certainty and access to the asset will be protected against identity fraud.
  • Whether smartphone, tablet or PC, you should always have an up-to-date virus protection and a firewall. Windows itself has rudimentary virus protection on board with Windows Defender, which is activated by default. However, it does not always do well in laboratory tests. If you don’t want to afford a full-fledged Internet security suite with a combination of firewall, virus protection and surf protection, you can have something similar cheaper or for free.
  • Connecting on a free WiFi can be attempting for anyone. But we must acknowledge that these networks are not secure enough to use when logging into secure systems or transmitting sensitive information (financial data, credit card numbers, etc.). We must access to this sensitive data via their secure connection at home or enable 4G for a secure connectivity outside.
  • Use a password manager to create secure passwords. A good password management is the key of cyber protection. Access to the applications containing crucial data must be protected with a strong password that contains at least eight characters, among which there must be capitals, low case letters, numbers and special characters. Avoid using same password on multiple applications / websites / online accounts. Instead use a secure password when you visit a new website / application.

 

The right to the protection of personal data, is a fundamental right laid down in Article 8 of the EU Charter of Fundamental rights and in article 16 of the Treaty of the Functioning of the EU. The right to the protection of personal data may be considered as one of the most important human rights of the modern age. In fact, topics such as Big Data, Artificial Intelligence, Machine and Deep learning, Internet of Things, Ubiquitous Computing, Surveillance and Data Transfer are examples that demonstrate the need for a right like this. This right also preserves the dignity and self-determination of an individual.

 

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Credential Stuffing Attacks: How to Protect Yourself

GLOBAL INTERNET USERE 2020

 

As we look forward to the hopefully great year ahead, let’s rewind a year that seems to have more episodes than Game of Thrones. Companies all across the globe were put into a huge test followed by a rapid shift from office working to home-office prompted by the coronavirus pandemic. Nearly overnight, organizations worldwide had to enable remote workforces to support their business requirements. COVID-19 has in many ways unleashed a new set of challenges and/or accelerated existing challenges, such as ransomware, data breaches, API attacks, cyber-fraud and unemployment frauds, within global enterprises.

 

It’s clear-cut that technology and security teams struggled in 2020 to respond as quickly to the changing environment as did the cybercriminals, who took advantage of an environment of unexpected change, extraordinary shifts in employees’ working process, and technology used by governments and worldwide companies.

 

Akamai reports that global internet traffic has grown by as much as 30 percent in 2020, while Statista observed that only in October 2020, online traffic across 20 different industries increased by 1.5% compared to the reference period in January 2020. Online transactions increased by 26.7 % compared to 2019. Another report from Cisco predicted there will be 5.3 billion total Internet users (66 percent of the global population) by 2023, up from 3.9 billion (51 percent of the global population) in 2018. That’s nearly two-thirds of the global population with Internet access.

 

The internet has gained more popularity in 2020, internet users are currently growing at an annualized rate of more than 7%, equating to an average of more than 875,000 new users each day. With this massive increase in the global internet traffic, Akamai is seeing over a hundred million of these attacks every day, with a peak of nearly 300 million a day, only in its own customer base. On the one hand, the internet is serving humanity, on the other hand, it has become the best place of like phishing, fraud, identity theft, Email Spoofing, bullying, cyberstalking, Malware, computer viruses.

 

In the history of cybercrime, the global coronavirus pandemic has added the credential stuffing problem to it extend. For those who don’t know, in this attack method, a cybercriminal tries a large number of stolen credentials on multiple websites. In order to gain unauthorized access to as many user accounts as possible to carry out attacks or fraudulent activities.

 

Transactional bots are getting popularity among hackers, as they act as agents on behalf of hackers. Bots aren’t typically created to compromise just one individual computer, they’re designed to infect millions of devices. An attacker first identifies websites with valuable accounts such as credit institutions, online shops, etc. In the Darknet, he then acquires lists of stolen login data and rents a botnet to automatically check the account list on the advised website. If a credential stuffing attack is successful, the attacker either sells the new, validated access data in the darknet or uses it himself. With the stolen data, fraudsters can then, for example, log into third-party accounts and carry out financial transactions.

 

Attacks like these often have very unpleasant consequences for the concerned companies and institutions, Application failures due to impaired web performance (73%) and recovery costs (63%) are among the greatest burdens, but also lower customer satisfaction, lost sales and financial losses (all-around 40%) have a significant weight (Akamai).

 

Here below are the few possible ways to protect yourself against credential stuffing attacks.

Protecting yourself from credential stuffing is pretty simple if you use the same password security tips that security experts have been recommending for years. For effective protection against bot attacks, it is recommended in the first step not to reuse the passwords, use a password manager, enable two-factor authentication, and get your leaked password notifications.

 

By using a unique password for each online account, you can protect all of your accounts at once. Because even if one of your passwords gets leaked, it can’t be used to sign in to other websites. As remembering strong unique passwords, for each account you have, is a nearly impossible task, the use of a password manager is recommended. It can not only remember your passwords but also generate strong unique passwords. Additionally, don’t ignore the 2-factor authentication. It is much more difficult to hack your account if you’re using two-factor authentication on it. As it adds an additional layer of security to the authentication process, it harder for attackers to gain access to your devices or online accounts.

 

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2021: Intelligent Data Management Will Enable the Future of Your Business

2021 Intelligent Data Management Will Enable the Future of Your Business

The EU’s GDPR has a major impact on the data privacy ecosystem. The regulation is an essential step to strengthen individuals’ / Business fundamental rights in the digital era we are living in. After two years of the introduction of the GDPR, the following question still arises: What will 2021 bring in terms of data management and data protection? According to Gartner, by 2023, 65% of the world’s population will have its personal data covered under some kind of modern privacy regulations.

 

It’s predicted that the technology for the preparation, control and administration of data will become much more efficient so that data is available more quickly and reliably. With the focus on foundational components of data integration, data governance, and data preparation the effectiveness of big data projects can be improved. With the right technology, data management can also drive enormous business value and support digital transformation. It’ll certainly help organizations to better manage the availability, usability, integrity, and security of their enterprise data.

 

Data has evolved over the years and will continue to evolve. Today’s organizations are data-centric; they accumulate enormous amounts of information in many different formats. Those who are unprepared to deal with the amount of data will be left behind compared to those ready to welcome all business opportunities that big data has to offer. Here below are 5 main areas that play a huge role in the good preparation of data management.

 

  • Data orchestration

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A frequently used term in the sales and marketing domain for whom data has a high priority as their data is the foundation of just about everything, they do. Simply put, data orchestration is the automation of data-driven processes that includes data preparation, making decisions based on that data, and taking actions based on those decisions. Data and API integration and data movement need to grow together to support all kinds of DataOps (data operations) methods. It’s a process that often spans across many different systems, departments, and types of data. It also requires a combination of different technologies that ensure a central data flow. This is the only way to orchestrate data-related activities – across different locations, on-premise or in the cloud.

 

  • Data discovery

In this process, relevant data insights are uncovering and transferred to the business users who need them. A comprehensive directory for searching, making available, saving, and interpreting data and other objects is becoming more and more important. Advanced Analytics enables the automation of mundane data management tasks and frees up resources to actually generate added value from the data. With the right use of data discovery tools, even the non-IT staff can easily access complex data sets and draw out the information they need. This process of knowledge discovery can be performed by anyone, without the technical know-how that was required in the past.

 

  • Data preparation

“Data preparation is one of the most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms, Rita Sallam – Research Vice-President at Gartner.” However Artificial intelligence (AI) has solved this problem by creating the basis for advanced data transformation and by enabling automatic cleansing and consolidation of data. This enables users without any prior technical knowledge to use data.

 

  • Model management

Model Management technologies help organizations consistently and safely in developing, validating, delivering, and monitoring models that create a competitive advantage. The focus is to put the central control of all models in a single application instead of the separate management of individual models. In view of the fact that many analytical models never go into production or quickly become obsolete (model decay), it is important that companies can quickly and easily register new models, adapt, track, evaluate, publish, regulate and document them.  Previously, model management referred just to monitoring production models, but it’s beyond that. Models drive new breakthroughs and operational improvements for businesses. According to a McKinsey study, organizations that leveraged models extensively showed a 7.5% profit margin advantage over their peers, whereas those that did not use models had a 2.5% profit margin deficit compared to their peers.

 

  • Data governance

“A data governance plan, supported by effective technology, is a driving force to help document the basis for lawful processing.” Data protection laws require companies to have data governance programs that provide “data privacy by default” and define policies, roles, and responsibilities for the access, management, security, and use of personal data. If they do not proactively advance standards and programs, they not only run the risk of contradicting legal requirements but they could also lose the trust of their partners/customers. With the use of advanced analytics and artificial intelligence in decision-making, they are therefore even more challenged to bring transparency to the algorithms.

 

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Understanding Challenges Related to Technologies in the IoT Field

Understanding Challenges Related to Technologies in the IoT Field

 

The Internet of Things (IoT) indicates the use of connected devices and systems to leverage data gathered by embedded sensors in machines and/or other physical objects. For consumers, the IoT has the potential to deliver solutions that dramatically improve energy efficiency, security, health, education and many other aspects of daily life. For enterprises, IoT can underpin solutions that improve decision-making and productivity in manufacturing, retail, agriculture and other sectors.

 

The number of connected devices to the internet is increasing each year at an alarming rate. As there are a lot of emerging applications and evolving technologies in the field of IoT, the challenge is to increase with these growing trends in applications and technologies.

Studies shows that the IoT fields is facing a lot of challenges. From bandwidth and battery problems in small devices, power disrupts related to the devices and configurations to Data Security, Data Control & Access, no uniform standard/structure, and Large Volume of Data produced.

 

Data Security

Data security in terms of IoT is defined as a necessity to ensure availability and continuity of a certain application and to avoid potential operational failures and interruptions with internet connected devices. As we increasingly connect devices to the Internet, new opportunities to exploit potential security vulnerabilities grow.  The threats here could come in different levels such as at the device level, network or system/application levels. They also come in a variety of ways such as using random attacks such as Distributed Denial of Service (DDoS) and malicious software. Different devices such as sensors, RFID tags, Cameras, or network services (WSN,Bluetooth) could be vulnerable to such attacks which in turn can be used as botnets. Home appliances such as refrigerator, TVs can also be used as botnets to attack this and similar devices. Thus companies must ensure that IoT devices and related data services are secure from vulnerabilities, especially as this technology is becoming more pervasive and integrated into our daily lives.

 
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Data Control & Access/Privacy

It is known that IoT applications produce large volume of data in a faster rate from different devices and these smart devices collect and process personal information. But knowing what risks these devices have, how the data is produced and used, who owns and controls it and who has access to it are some privacy questions that one need to ask while getting the services of these devices. It is obvious that the data that is produced from these devices face privacy concerns from the users. The concerns most of the times could come in two forms, first where personal information about the individual is controlled, identified, and the owner does not know who access it or to whom it is known. Secondly, the individuals’ physical location can be traced and be known his/her whereabouts, hence violating privacy. This shows that privacy is one of the basic challenges in the IoT field as is anywhere in the IT field and companies must overcome it with the help of GDPR regulations.

 

No Uniform Standards/Structures

IoT is comprised of different components such as hardware devices, sensors and applications. These different components are manufactured and developed by different industries. When these components designed to be used in IoT solutions, they need to exchange data. Problems arise while trying to communicate because the standard used in one product is not used in the other and it creates communication or data exchange problems which may delay the expansion of IoT products. The problem is not only in the design of devices, but also in the internet protocols used today. The absence of uniform standard/structure for different technologies used in IoT is one challenge for the field.

 

Large Volumes of Data Produced

This is another challenge in IoT, i.e the data produced from various sensor and mobile devices is heterogeneous, continuous, very large and fast. These produced data need to be processed instantly before it is expired. Managing these kinds of data is against the capacity of traditional databases. As the number of connected devices is expected to increase in the future, the data produced from these devices is going to increase exponentially and a good analytic platform and storage systems are needed.

 

The Internet of Things involves a complex and evolving set of technological, social, and policy considerations. IOT is happening now, and there is a need to address its challenges and maximize its benefits while reducing its risks.

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