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Top 5 Challenges of Implementing Industrial IOT

IIOT INDUSTRIAL INTERNET OF THINGS

IoT solutions have not only conquered our homes in recent years, they are also being used more and more frequently in industry. IoT deployment is diversifying from consumer-based applications to mission-critical applications in the industrial sector and playing a critical role in the next phase of factory automation, which has been called Industry 4.0. The industrial IoT (IIOT) offers companies the full digitization of production processes, cooperating the digital and physical worlds within the factory. The most common scenarios for IIOT include predictive maintenance, intelligent measurement technology, asset management, and fleet management.

 

During this global financial crisis of Covid19, more and more industrial companies are turning to the IIOT to remotely monitor their systems and prevent unplanned downtime. A survey by Microsoft found that 85% of companies have at least one IoT project in the works, and a recent forecast by Million Insights predicted that the Industrial Internet of Things (IIoT) could reach a whopping $992 billion in global spending by 2025. Even though IIOT is transforming the future of manufacturing, it does bring some serious challenges as companies start to accelerate their digital transformation. Here below are listed top 5 challenges that companies have to face in their implementing phase of IIOT.

 

High-Investment and Ownership Cost

The cost of industrial IoT products and their deployment are obviously very high. Sure, one of the main promises of industrial ITO is to improve manufacturing efficiencies and reduce costs through better asset management, access to business intelligence, and productivity gains. However, not only development but support should be considered along with high skill resources who are expert in IoT. So, the overall cost of industrial IoT application implementation is very high. And it’s hard for organizations to justify the cost when they’re not entirely sure what kind of ROI to expect at first.

 

Connectivity

One of the main requirements for adopting the IIOT is having reliable data networks with sufficient capacity. IIoT connectivity should be a forethought before deployment, not an after thought. Studies shows that in 85% of worldwide factories, machines are not connected and are unable to collect / provide data and transmit back to the centre for analysis. One of the main reasons is because legacy devices have a lifespan of 30-60 years and do not support data-driven tools and offer few connectivity options. On the other hand, the number of connected devices is growing at a much higher rate than the network coverage, which creates monitoring and tracking problems. Also, because the Internet is still not available everywhere at the same speed, connectivity issue is the one of the biggest challenges in IIOT deployment. Thus, having scalable IoT network to connect devices and servers is critical for a large scale industrial IoT app.

 

Cybersecurity

Securing industrial IoT devices is a challenge in itself for a number of reasons. With this expansion of the industrial IOT, the attack surface for companies also increases. In case of any successful attack on industrial IOT, not only sensitive data is realised, but it can also cause massive physical damage to machines and bring the entire production of manufacturing companies to a standstill. Thus, security challenges for IIoT technologies are the biggest concern as the breaches affect both individuals and organizations vulnerable to financial and operational damage.

 

When it comes to protection, most cybersecurity protection tools only focus on network and cloud but misses the endpoint and OTA vulnerabilities. “According to IDC, 70% of security breaches originate from endpoints. While organizations may not be able to eliminate all industrial IoT attacks from occurring, OTA vulnerabilities and potential point of entries into endpoint devices should be identified, and devices should be tested using a regularly-updated database of known threats/attacks to monitor device response and detect anomalies”. The ultimate goal for companies and their industrial processes is not only need to adapt to this rapid change but also avoid being target for hacking groups.

 

Data Analysis

A common method for implementing IoT solutions in industrial environments is to expand manufacturing facilities with tools for data acquisition, analysis and visualization. These include sensors, IoT gateways, human-machine interfaces and cloud-based analysis tools that transform raw data from devices into usable insights.

The analysis of data is important to make this voluminous amount of data being produced in every minute via the rapidly growing number of sensors, embedded systems and connected devices as well as the increasing horizontal and vertical networking of value chains, useful. In multiple organizations, this valuable data isn’t being used in a structured and sufficient manner. It is important for business organizations to hire a data scientist having skills that are varied as the job of a data scientist is multidisciplinary and the critical business decisions should be taken effectively.

 

Skill Gap

Industrial IOT project owners realise that one of the most challenging issues with industrial IoT is the skills gap and how to address this issue. Industry is undergoing a rapid change right now and companies have been raising worries of a lack of technical staff. Absence of qualified staff is impacting many areas within the company. According to Microsoft’s 2019 IoT Signals report, 29% of organizations reported that a lack of resources was one of the top reasons for holding off on IoT adoption. For many manufacturers, finding qualified staff to design, deploy and maintain modern industrial networks and the urgent need to update and transform business operations is huge challenge.

 

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Industry 4.0: Future of Manufacturing

Industry 4.0: Future of Manufacturing

The concept of Fourth Industrial Revolution was first introduced in 2015 by Klaus Schwab, executive chairman of the World Economic Forum. It’s characterised by the increasing digitization and interconnection of products, smart machines, value chains and business models. Since connected machines collect a tremendous volume of data that can not only analyse that data to identify new patterns and insights but also inform maintenance, performance and other issues, that are impossible for a human to do in a reasonable timeframe. Today, Industry 4.0 offers the opportunity to factories across the globe, the opportunity to optimize their operations quickly and efficiently by knowing what needs attention.

 

Here below technologies that are the significant driver for the advance of Industrial Internet Solutions aka, IIOT:

  • The Internet of Things (and services)
  • The Digitization and Interconnection of products and services
  • Virtual and Augmented reality
  • Cloud Computing
  • Intelligent machines and autonomous Systems
  • Big data and Analytics
  • Cybersecurity

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The Internet of Things is nothing new. It is already present all around us and is growing strongly. Every day new things are linked together. This growing list of objects includes everything from your thermostat to the parking meters, household appliances, heating and cooling systems, vehicles, health and fitness monitors, environmental sensors, and more. Many of these devices can already observe their surroundings, exchange information over internet and report this data back to the user.

 

The adoption of IOT in the world of production is also increasing at an exponential rate. By 2025, Industry 4.0 –the Fourth Industrial Revolution–is expected to generate close to $1 trillion in economic value, much of it is unlocked by continuous operational efficiencies through traditional cost-cutting measures, significant transformation in the way goods are produced and delivered and large manufacturers are becoming more streamlined, efficient, and agile.

 

Whether we call it Smart Industry, Factory Automation or Industry 4.0, the goal remains the same, enable companies to produce more efficiently, which translates into an increased efficient manufacturing and production. The aim of Industry 4.0 is to make factories “smart factories”, producing “smart products with autonomous machines that will self-monitor and organize their own maintenance.

 

More and more companies are using the IIOT to capture new growth through increasing production and creating new hybrid business models, exploiting intelligent technologies to fuel innovation, and transform their workforce. It’s quite clear that the Fourth Industrial Revolution represents a fundamental change in the way we live, work and relate to one another. There is huge potential for digitization and innovation to add value to factories and to contribute to the environment and biodiversity.

 

However, to unlock the true potential of this revolution, companies must digitise and integrate their processes vertically across their entire organisation, from product development and purchasing, through manufacturing, logistics and service. Factories and warehouses must adapt and implement a digital mindset, leverage the IIoT and digitalization to become much more agile and efficient in their production lines around the world.

4 Basic Tips for a Successful Transition to the Cloud

4 Basic Tips for a Successful Transition to the Cloud

IT managers nowadays have to deal with a wide variety of challenges that comes with migrating to the cloud. Although cloud usage has become widespread in recent years, some companies still feel that they have not yet reached the full potential of the cloud.

However, the reasons for this are easy to identify, and cloud usage can be optimized using a few basic measures. Transaction to the cloud successfully means having an experienced partner who know exactly your industry requirements and can answer the following questions before the move. Such as how large and complex is company’s data? How important are regulatory considerations? Are company’s current business applications cloud ready? How much your day-to-day operations can tolerate downtime depends on the type of the application involved and what service level agreement does the company require for a cloud environment? If the company decides to change the cloud provider in the future, can the data and applications migrate with them?

 

Once these questions are answered, IT team can choose their cloud partner who can provide a migration plan and offer a cloud customized solution. Keep in mind that performance, security and reliability must be maintained when moving to clouds. Approach the migration in smaller chunks and stay in close coordination with your cloud provider. The goal is for the entire migration to cause minimal disruptions. Here below are few basic tips for a successful cloud migration and management.

 

Prioritize security

In the cloud age, the security of IT applications plays a particularly important role. Before any move to the cloud, IT managers must go through a list of business applications and identify those that they want to migrate. Planning is the key in order to recover any disaster, risk management and other potential situations. As company’s highly sensitive data, which is also used regularly, is moved to these infrastructures or is already stored on the complex architecture of cloud infrastructures, it makes many IT managers sweat.

IBM’s Cost of a Data Breach Report 2020  has shown that despite a nominal decline from $3.92 million in the 2019 study to $3.86 million in the 2020 study, the average total cost of a data breach  was much lower for some of the most mature companies and industries and much higher for organizations that lagged behind in areas such as security automation and incident response processes.

With the right security measures, however, risks and financial losses can be significantly minimized. While you might expect it’s your cloud provider’s responsibility to take all security measures, it’s also one of the biggest responsibilities of the customer to ensure their data is secure. Here are some of the methods we recommend at Storm to keep yourself safe when using the cloud. IT managers can ensure their data is secure by using methods such as multi factor authentication, strong passwords, data encryption and regular backups.

 

Understand and Enforce your Cloud Governance Plan

When implementing cloud services, many companies fail to develop a clear governance plan from the start and then consistently adhere to it. Governance, may be defined as an agreed-upon set of policies and standards, which are based on a risk assessment and inclusive of audit, measurement, and reporting procedures, as well as enforcement of policies and standards. Most security leaks in the cloud are due to weak corporate governance practices. In a multi-enterprise or multi-platform cloud environment, a lack of governance can not only lead to the loss of highly sensitive data, but also to considerable financial losses.

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Therefore, from the start, companies must not only establish and implement chains of responsibilities, authority and communication to empower people but also establishing measurement, policy and control mechanisms to enable people to carry out their tailored roles and responsibilities towards the respective cloud infrastructure.

 

Prepare your IT teams for cloud

Another challenge that IT departments have to face is the lack of knowledge of employees on the subject of cloud infrastructures. Just like any new technology, your employees need to learn specific skills that allow them to successfully work with the cloud solutions you plan to integrate.  For IT departments, the switch to cloud computing requires not only a different skill set but a different mindset. In order to take all the benefits cloud has to offer, it’s impossible for companies to dive into it without prior training and intelligent strategy. A proper training has a significant impact on cloud adoption, and this is especially true for organizations that invest in more comprehensive training. Once employees undergo training, they can understand where their skills fit and where they can contribute.

 

Optimize the cloud performance

Performance optimization is one of the main reasons why companies switch to the cloud in the first place. Performance optimization on key areas including scalability, concurrency, response time and throughput optimization can help you run better on Cloud. In this optimisation company can correctly select and assign the right resources to a workload or application. Simply put, cloud optimization can help you reduce cloud infrastructure cost and improve your application performance. Once the workload performance, compliance, and cost are correctly and continually balanced against the best-fit infrastructure in real time, efficiency is achieved.

 

Conclusion

The change to the cloud does not happen overnight, nor does it happen with the flick of a finger. You have to invest time, resources, and fund to migrate your applications and data to the cloud successfully. Security risks, a lack of governance, a lack of expertise and performance problems are all challenges that discourage many companies from taking this step. However, as long as companies take a few basic measures, they are well on the way to a successful and secure migration to the cloud.

 

Source :

Cost of a Data Breach Report 2020

How can Digital Marketing benefit from Artificial Intelligence?

AI in Digital Marketing xorlogics

 

AI is now more accessible than ever with it’s potential to change business forever. With its positive impact, and being a lot more affordable than before, both big and small companies are able to benefit from the insights and automation options it provides. With the integration of AI brands can not only leverage customer’s data but also anticipate their customer’s next move, understand sales cycles better, correlate their strategies for converting prospects into paying customers and improve the overall customer’s journey with machine learning efforts.

 

As a subset of AI, machine learning involves the analysis of historical data from various business interactions with customers / prospects (for example: when and what was the last time the customer ordered?). As ML has the ability to analyse extremely large sets of data, its being used in the digital marketing departments around the globe to identify sales patterns and increase success factors. Algorithms for ML generate insights via predictive analytics, based on these insights, marketers can either take actions individually or automate AI to do the job. For example, they can automate emails that are aimed at re-targeting your audience, giving you a better chance of a higher ROI.

 

ML is also playing a major role in SEO. Even tough SEO algorithms change across major search platforms, with AL and ML tools, the insights from searchable content may become more relevant than specific keywords in the search process. In order to maintain a high-ranking place on search engine result pages, consider the quality of your content rather than simply the keywords included. By doing so, you’ll be ahead of the game when it comes to future-forward content creation and SEO.

Marketers can also benefit from ML tools to analyse what type of content, keywords, and phrases are most relevant to your desired audience. Once they have the key insight, they can optimize their dialogue and develop engagement across multiple online platforms, to drive brand awareness and create meaningful relationships with leads, prospects, and customers alike

 

The key difference between modern AI-based and traditional outbound marketing strategies is the integration of contextual data, i.e. information that results from the interaction with the customer is not stored in labour-intensive and time-consuming data warehouses but is available in real time and can be used for decision-making. With these customer profiles and interests recorded in parallel to the interaction, the previously defined decision strategies are fed in order to achieve high planning security for high, profitable conversion rates.

 

Artificial intelligence opens up possibilities in marketing domain far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive. In addition to that, AI, robotics and other forms of smart automation will bring great economic benefits and contribute up to $15 trillion to global GDP by 2030.

 

With AI integrated marketing, business can forecast customer behaviour and run data-based campaigns to have remarkable results. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers. Business are creating value through transforming customer journeys by providing immediate response to consumer’s queries or issues. According to McKinney’s study, 75% of customer demand NOW service within 5 minutes of online contact. If business can beat this time, they can convert a ‘visitor’ into a ‘paying’ customer.

Here below are some interesting statistics of AI in marketing:

 

  • According to research from Callcredit, 5 hours and 36 minutes is the amount of time that the average marketing professional spends collating data and getting it ready for presentation. An AI integrated marketing dashboard can do the collating in minutes and give you more time for reporting process. Statwolf
  • 97% of leaders believe that the future of marketing lies in the ways that digital marketers work alongside machine-learning based tools. QuanticMind
  • By 2020, 30% of companies worldwide will be using AI in at least one of their sales processes. Gartner
  • By 2020, 85% of customer interactions will be handled without a human. Gartner
  • For 61% of marketers AI is the most important aspect of their data strategy. MeMSQL
  • 80% of business and tech leaders say AI already boosts productivity. Narrative Science
  • When AI is present, 49% of consumers are willing to shop more frequently while 34% will spend more money. PointSource
  • Large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50% of them currently have one. MIT Sloan Management Review
  • Netflix is saving $1 billion per year by using machine learning to make personalised recommendations- Artelliq
  • 44% of consumers don’t even realise they’re already using AI-powered technology platforms. Pega
  • 45% of end users prefer chatbots as the primary mode of communication for customer service inquiries. Grand View Research

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The ways that ML is being used in digital marketing practices helps organizations to expand their understanding of their target consumers and how they can optimize their interactions with them.  So, when developing appropriate AI applications for marketing purpose, the collaboration between software developers and domain expert’s must not be neglected. The ultimate decision-making competence when it comes to the all-important question of which next best actions are to be implemented in order to achieve the greatest possible marketing success usually belongs to the domain experts. You should have the experience and expertise to incorporate the right NBAs into the self-learning models for decision-making strategies.

 

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