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Adapting your business digital strategies in 2024 with more AI

AI is revolutionizing the way businesses operate in today’s digital age. 2023 was all about harnessing the power of artificial intelligence to drive corporate success. By integrating AI into their digital strategies, companies could unlock a multitude of benefits that help them stay ahead of the competition and drive success, even in uncertain times. From e-commerce giants to small online startups, it became an integral part of businesses across industries. So, if you want your business to thrive in 2024 and beyond, it’s time to adapt your digital strategies and embrace the AI revolution.

AI refers to the development of computer systems that can perform tasks that typically require human intelligence. These intelligent machines have the power to analyze vast amounts of data, learn from patterns and trends, and make informed decisions. One key role of AI in business is automation. With its ability to handle repetitive and mundane tasks, AI frees up valuable time for employees to focus on more strategic initiatives. From customer service chatbots providing instant assistance to automated inventory management systems optimizing supply chains, AI streamlines operations and boosts efficiency.

 

Another vital aspect is predictive analytics powered by ML algorithms. By analyzing historical data, AI systems can identify patterns and predict future outcomes with remarkable accuracy. This allows businesses to make data-driven decisions, optimize marketing campaigns based on consumer behavior analysis, personalize user experiences on e-commerce platforms, and even forecast demand for products or services.

 

Moreover, AI enables personalized recommendations tailored specifically for each customer. By understanding their preferences through behavioral analysis or purchase history examination, businesses can offer relevant product suggestions that enhance engagement and drive sales.

 

The role of AI extends beyond operational efficiency; it also revolutionizes decision-making processes within organizations. Through advanced algorithms and natural language processing capabilities, AI helps executives access real-time insights from multiple sources at lightning speed. This empowers them to make well-informed decisions swiftly – a competitive advantage in today’s fast-paced business environment.

 

Embracing the role of AI in your business strategies opens doors to endless possibilities for growth and innovation while improving operational efficiency across various departments. As we delve deeper into this exciting topic in this blog post, you’ll find practical examples from successful implementations around the globe!

 

Examples of Successful AI Integration in Businesses

 

AI integration has revolutionized various industries, enabling businesses to streamline their operations and enhance customer experiences.

In the retail sector, companies like Amazon have leveraged AI algorithms to personalize product recommendations for customers based on their browsing and purchase history. This not only improves customer satisfaction but also boosts sales by driving targeted marketing efforts.

 

AI-powered chatbots have become increasingly popular in the customer service industry. Companies such as Amazon, Bol, have implemented chatbots that can handle basic customer inquiries, reducing response times and improving efficiency.

 

The healthcare industry has also seen significant advancements with AI integration. For instance, IBM’s Watson Health platform utilizes AI technology to analyze vast amounts of medical data, assisting doctors in diagnosing diseases more accurately and efficiently.

 

In the manufacturing sector, companies like Tesla have embraced AI-driven automation systems to optimize production processes. These intelligent machines can monitor quality control, predict machinery maintenance needs, and increase overall productivity.

 

E-commerce giants like Alibaba are utilizing AI algorithms to analyze consumer behavior patterns and make real-time pricing adjustments accordingly. This allows them to offer personalized promotions tailored specifically to individual customers’ preferences.

 

These examples demonstrate how successful implementation of AI technologies can drive growth, improve operational efficiency, enhance customer experiences, and ultimately lead businesses toward success in today’s digital age.

 

How to Prepare Your Business for the Inevitable Shift towards AI

 

As technology continues to advance at a rapid pace, it’s becoming increasingly clear that AI will continue to play a crucial role in shaping the future of businesses. To stay ahead in this digital age, companies need to start preparing themselves for the inevitable shift towards AI.

 

One way to prepare your business for this shift is by investing in AI-powered tools and software. These tools can help automate repetitive tasks, analyze large amounts of data, and provide valuable insights that can inform decision-making processes. By leveraging these capabilities, businesses can optimize their operations and improve overall efficiency.

 

Another important aspect of preparing your business for AI is upskilling your workforce. As AI becomes more prevalent in the workplace, employees will need to acquire new skills and adapt to working alongside intelligent machines. Providing training programs and educational resources can help employees embrace AI technology and use it effectively within their roles.

 

Additionally, businesses should also focus on building a strong data infrastructure. With AI relying heavily on data analysis, having clean and reliable data sets is essential for accurate predictions and insights. Implementing robust data management systems can ensure that you have access to high-quality data that fuels your AI initiatives.

 

Furthermore, fostering an innovative culture within your organization can greatly facilitate the adoption of AI technologies. Encouraging experimentation with new ideas and providing avenues for collaboration between different teams can lead to breakthroughs in implementing AI solutions tailored specifically to your business needs.

 

Staying informed about industry trends related to AI is vital for any business looking to thrive in the digital landscape. Keeping abreast of advancements in machine learning algorithms or emerging applications of AI can give you a competitive edge over others who may be slower to adopt these technologies.

 

Therefore, businesses must recognize the transformative power of AI as they plan their strategies moving forward into 2024 and beyond. By taking proactive steps such as investing in AI tools, upskilling employees, focusing on data infrastructure, fostering innovation, and staying informed about AI trends, businesses can position themselves in a powerful position.

Best Ways to Drive Corporate Growth in Uncertain Times

Are you feeling the pressure to drive corporate growth in these uncertain times? You’re not alone. The current business climate is rife with challenges and uncertainties, making it more important than ever for companies to find innovative ways to thrive. Let’s explore some of the best strategies for driving corporate growth during uncertain times. Whether it’s through diversification, innovation, and adaptation, or strategic partnerships and collaborations, there are plenty of avenues to explore!

The importance of driving corporate growth in uncertain times

In today’s ever-changing business landscape, driving corporate growth is crucial, especially during uncertain times. The ability to adapt and thrive in the face of uncertainty can determine the long-term success or failure of a company.  Driving corporate growth allows businesses to stay ahead of their competitors. By continuously seeking opportunities for expansion and improvement, companies can gain a competitive edge that sets them apart from others in their industry.

 

Corporate growth helps create stability and resilience within an organization. In uncertain times, when market conditions are volatile and unpredictable, companies that have focused on growth strategies are better equipped to weather economic downturns and navigate through challenging circumstances.

Additionally, driving corporate growth fosters innovation. When companies actively seek new markets or develop new products/services, they stimulate creativity within their teams. This not only drives profitability but also enables organizations to remain relevant in changing customer demands. Furthermore, sustained growth leads to increased shareholder value. As a company expands its operations and generates higher profits over time, shareholders benefit from increased returns on their investments.

 

Strategies for driving growth during uncertain times:

  • Diversification: One of the most effective strategies for driving corporate growth in uncertain times is diversification. By expanding into new markets or offering new products or services, companies can reduce their reliance on a single source of revenue and mitigate risks associated with economic uncertainty. Diversification allows businesses to tap into untouched customer segments, explore untapped opportunities, and capitalize on emerging trends.
  • Innovation and Adaptation: Another key strategy for driving growth during uncertain times is innovation and adaptation. Companies that are able to quickly identify changing customer needs and market dynamics can adjust their business models, products, or services accordingly. This may involve leveraging technology to streamline processes, developing new solutions tailored to current demands, or even completely pivoting the business model.
  • Strategic partnerships and collaborations: Collaborating with strategic partners can be a powerful way to drive corporate growth in uncertain times. By joining forces with complementary businesses or industry leaders, companies can access new resources, expertise, distribution channels, and customer bases. Strategic partnerships also provide opportunities for shared knowledge transfer and mutual support during challenging times.

 

Case studies of companies that have successfully driven growth during uncertain times

Case studies of companies that have successfully driven growth during uncertain times serve as valuable sources of inspiration and guidance for businesses seeking to navigate through challenging economic landscapes. One such example is Amazon, which experienced significant growth during the 2008 global financial crisis. Instead of retreating, the company recognized the opportunity to expand its product offerings and capitalize on consumers’ increasing preference for online shopping. This strategic move not only helped Amazon maintain its position in the market but also propelled it towards becoming a dominant force in e-commerce.

 

Another notable case study is Netflix, which faced stiff competition from DVD rental stores when it first entered the market. However, instead of succumbing to industry norms, Netflix disrupted the traditional model by introducing a subscription-based streaming service. By focusing on innovation and adapting to changing consumer preferences, Netflix was able to drive substantial growth even during uncertain times.

 

In both these cases, diversification played a crucial role in driving corporate growth amidst uncertainty. These companies identified new opportunities within their respective industries and capitalized on them effectively. Additionally, they prioritized customer-centric strategies by constantly innovating and adapting their business models according to evolving consumer needs.

 

Strategic partnerships and collaborations are another key driver of growth during uncertain times. Take Uber’s partnership with Spotify as an example – this collaboration allowed Uber riders to personalize their trip experience with music while simultaneously providing Spotify access to millions of potential subscribers. By leveraging each other’s strengths and reaching new audiences together, both companies were able to achieve sustained growth even in turbulent times.

These case studies demonstrate that successful corporate growth during uncertain times requires visionary leadership that embraces change rather than shying away from it. It demands an agile mindset that can identify opportunities amidst challenges while remaining focused on delivering value to customers.

 

By studying these success stories closely, businesses can gain insights into effective strategies for driving corporate growth amid uncertainty – whether it be through diversification efforts or innovative partnerships – ultimately helping them thrive despite unpredictable circumstances

Growth doesn’t happen overnight; it requires ongoing effort. Remember that every company’s path to success will differ based on its unique circumstances. Therefore it’s important to have a well-defined strategy that requires continuous evaluation.

Best strategies for Cloud Cost Optimization

Cloud services have revolutionized how organizations store, manage, and access their data, offering unparalleled flexibility and scalability. However, as with any resource, it’s essential to optimize costs and maximize savings in this virtual realm. A cloud cost-saving strategy involves optimizing the usage of cloud computing resources to reduce overall cloud expenses while maintaining or even improving operational efficiency and performance.

 

When it comes to cloud costs, there are several components that need to be understood in order to effectively manage and save money. One key component is the cost of computing resources, which includes virtual machines, storage, and networking. These costs can vary depending on factors such as usage patterns, data transfer rates, and storage capacity.

 

Another important factor in cloud costs is data transfer fees. Transferring data between different regions or zones within a cloud provider’s infrastructure can incur additional charges. It’s essential to have a clear understanding of how these fees are calculated and consider strategies such as optimizing data placement to minimize these costs.

 

Additionally, many cloud providers charge for outbound bandwidth usage. This means that any traffic leaving your cloud environment will be subject to additional fees. By monitoring and analyzing your outbound traffic patterns, you can identify opportunities for optimization and potential cost savings.

 

One often overlooked aspect of cloud costs is idle resources. It’s not uncommon for organizations to provision more resources than they actually need or forget about those no longer in use. By regularly reviewing your resource utilization and implementing automation tools like auto-scaling or scheduling shutdowns during off-peak hours, you can reduce waste and optimize spending.

 

Licensing plays a crucial role in determining overall cloud costs. Some software licenses may require additional fees when deployed in a virtualized environment or across multiple instances within the same region. Understanding these licensing implications upfront can help avoid unexpected expenses down the line.

Develop a Cloud Cost-Saving Strategy

 

When it comes to managing cloud costs, having a well-defined strategy in place is essential. A cloud cost-saving strategy should not only focus on reducing expenses but also ensure optimal resource utilization and performance.

 

The first step in developing your strategy is to understand your current cloud spend and identify areas of potential optimization. This can be done by analyzing usage patterns, identifying idle resources, and evaluating the performance of different service tiers or instance types. Once you have identified areas for improvement, it’s important to set clear goals for cost reduction. These goals should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, you might aim to reduce overall cloud costs by 20% within six months.

 

Next, consider leveraging automation tools to streamline cost optimization processes. These tools can help automate tasks such as scheduling instances based on workload demands or rightsizing resources based on actual usage data. By automating these processes, you can free up valuable time and resources while ensuring cost savings are consistently achieved.

 

Implementing best practices for cloud cost management is another key aspect of your strategy. This may include regularly monitoring and optimizing storage costs by deleting unused data or implementing lifecycle policies. It could also involve leveraging spot instances or reserved capacity options when appropriate to take advantage of discounted pricing models.

 

To further enhance your approach to cloud cost-saving strategies implementation:

  • Track and analyze spending trends over time
  • Implement tagging mechanisms for better visibility into resource allocation
  • Set up cloud monitoring and alerting to track resource utilization and costs in real time.
  • Assign meaningful tags to resources and use cost allocation tools to track spending by team, project, or department. This helps identify areas where cost optimization is needed.
  • Choose the most cost-effective region and availability zone for your workload. Leverage multi-region redundancy only when necessary for high availability.
  • Regularly review your cloud bills from various providers, analyze usage patterns, and forecast future costs to make informed decisions
  • Evaluate the use of specialized third-party tools that offer more granular insights into spending patterns
  • Ensure your team is knowledgeable about cloud cost management best practices. Training and awareness can go a long way in reducing wasteful spending.
  • Cloud cost optimization is an ongoing process. Continuously monitor and refine your strategies based on changing business needs and technology advancements.

 

Implementing a successful cost-saving strategy in the cloud requires a combination of monitoring, automation, and a commitment to optimizing resources. By developing a comprehensive cloud cost-saving strategy that encompasses all these elements – understanding current costs; setting SMART goals; utilizing automation tools; and implementing best practices – businesses can achieve significant savings while maintaining operational efficiency in their cloud environments.

What is Generative AI & how it’s transforming the Software and Tech Ecosystem

Artificial intelligence is one of the most disruptive technologies of our time. It has revolutionized many industries, from healthcare to finance and beyond. However, it’s not just limited to that – the emergence of generative AI has opened up new possibilities for software and tech innovation.

 

Generative AI, or Generative Artificial Intelligence, refers to a field of AI that focuses on creating or generating new content, such as images, text, audio, or even video, using machine learning techniques. It involves training models to understand patterns and structures in existing data and then using that knowledge to generate new, original content that resembles the training data. Unlike traditional AI models, generative AI can produce original and unique output without human intervention, making it highly valuable in various fields. Generative AI has been transforming the software and tech ecosystem in several ways:

 

Content Generation: Generative AI enables the automatic creation of realistic and high-quality content. For example, it can generate realistic images of nonexistent objects or landscapes, produce coherent and contextually relevant text, compose original music, or even create deepfake videos. Generative AI algorithms can create new pieces of content by learning patterns from existing data, making it easier for businesses to generate creative outputs.This technology has the potential to revolutionize various creative industries such as advertising, design, entertainment, and art.

 

Personalization and Recommendation Systems: Generative AI helps in building more personalized and effective recommendation systems. By understanding user preferences and generating tailored recommendations, it enhances user experiences across various platforms, including e-commerce, streaming services, social media, and news platforms.

 

Simulation and Training: Generative AI allows for the creation of realistic simulations and training environments. This has applications in fields like autonomous vehicles, robotics, and healthcare, where virtual simulations can be used to train and test systems without the need for real-world deployment. It also aids in training models for reinforcement learning by generating diverse and challenging scenarios.

 

Data Augmentation: Generative AI can be used to augment existing datasets, especially when data availability is limited. It can generate synthetic data that resembles real data, helping to improve the performance and robustness of machine learning models. This is particularly useful in domains such as medical imaging, where acquiring large labeled datasets can be challenging.

 

Creativity and Design: Generative AI provides new tools for creative professionals by automating certain aspects of the design process. It can generate design suggestions, assist in creating artwork, generate variations of designs, or aid in prototyping and iteration. This empowers designers to explore larger design space and enhances their creative workflows.

 

Generative AI is a game-changing technology that has the potential to revolutionize various industries. From creating realistic synthetic images and videos to aiding in drug discovery, Generative AI is transforming the software and tech ecosystem in unprecedented ways. The ability of Generative AI to generate new ideas, designs, and solutions could lead to groundbreaking innovations that can help solve some of our most pressing problems.

 

However, the advancement of generative AI also brings challenges and ethical considerations. Issues such as authenticity, copyright infringement, misinformation, and the potential for misuse of generated content need to be addressed. It is crucial to develop responsible frameworks and guidelines to ensure the ethical and safe deployment of generative AI technologies. Its applications have the potential to reshape various industries and user experiences, but ethical considerations must accompany its development and deployment.

 

The biggest challenges of BigData in 2023

The use of big data is on the rise, with organizations investing heavily in big data analytics and technology to gain insights and improve business performance. With the rapid growth of the internet, social media, and the IoT, the amount of data being generated is increasing exponentially. As a result, there is a need for better tools and techniques to collect, store, analyze, and extract insights from this data.

 

Additionally, the growth of the global datasphere and the projected increase in the size of the big data market suggest that big data will continue to be a critical driver of innovation and growth across various industries. In a study by Accenture, 79% of executives reported that companies that do not embrace big data will lose their competitive position and could face extinction.

 

Advancements in big data technologies such as machine learning, artificial intelligence, and natural language processing are also foreseen. These technologies have the goal to enable businesses and organizations to make better decisions, gain a competitive advantage, and improve customer experiences.

Xorlogics participating Cebit 2016

Here are a few examples of how big data is being effectively used in various industries:

 

  • Healthcare: Big data is being used to improve patient care, disease diagnosis, and treatment outcomes. For instance, healthcare providers can analyze electronic health records to identify patterns and trends that may help diagnose diseases earlier and predict patient outcomes. Additionally, big data analytics can help hospitals and healthcare organizations optimize their operations, such as reducing wait times and improving patient flow.
  • Finance: Big data is being used to identify and prevent fraud, assess risk, and personalize financial products and services. For instance, financial institutions can use big data to analyze customer behavior and preferences, in order to develop personalized marketing campaigns and offers. Additionally, big data analytics can help banks and other financial organizations to detect fraudulent activity and reduce the risk of financial crime.
  • Retail: Big data is being used to personalize the shopping experience, optimize inventory management, and improve customer loyalty. For instance, retailers can use big data to analyze customer behavior and preferences, in order to develop targeted marketing campaigns and personalized recommendations. Additionally, big data analytics can help retailers to optimize their inventory levels, reduce waste, and improve supply chain efficiency.
  • Manufacturing: Big data is being used to optimize production processes, reduce downtime, and improve quality control. For instance, manufacturers can use big data to monitor equipment performance and predict maintenance needs, in order to reduce downtime and optimize production schedules. Additionally, big data analytics can help manufacturers to identify quality issues early, reducing waste and improving product quality.
  • Transportation: Big data is being used to optimize transportation networks, reduce congestion, and improve safety. For instance, transportation companies can use big data to analyze traffic patterns and optimize routes, reducing travel time and congestion. Additionally, big data analytics can help transportation companies to monitor vehicle performance and identify potential safety issues, reducing accidents and improving overall safety.

 

Generally, big data is being effectively used across a range of industries to drive innovation and create value, improve operational efficiency, reduce costs, and improve customer satisfaction. Along with the benefits of Bigdata, it’s challenges cannot be ignored. Here below are few potential challenges that bigdata may face in the future:

 

  • Data Privacy and Security: As the amount of data collected and stored increases, so does the risk of data breaches and cyber-attacks. Protecting sensitive information will be critical, particularly as more businesses move towards storing their data in the cloud.
  • Data Quality: As the volume of data grows, so does the risk of inaccuracies and inconsistencies in the data. Ensuring data quality and accuracy will become increasingly challenging, particularly as the data comes from a wide range of sources.
  • Data Management: Managing large amounts of data can be complex and costly. Businesses will need to invest in tools and technologies to help manage and process the data effectively.
  • Talent Shortage: The demand for skilled data professionals is growing rapidly, and there may be a shortage of qualified individuals with the necessary skills to analyze and interpret big data.
  • Data Integration: With data coming from various sources, integrating, and combining the data can be a challenging process. This could lead to delays in data processing and analysis.
  • Ethical Use of Data: As the amount of data collected grows, it becomes increasingly important to ensure that it is used ethically and responsibly. This includes addressing issues related to bias, fairness, and transparency.
  • Scalability: As the volume of data continues to grow, businesses will need to ensure that their infrastructure and systems can scale to accommodate the increased data load.

 

Overall, these challenges could impact the effective use of big data in various industries, including healthcare, finance, retail, and others. Addressing these challenges will require ongoing investment in technologies and skills, as well as a commitment to ethical and responsible use of data.

 

If you are looking for a partner who can give you both strategic and technical advice on everything to do with the cloud, than contact us so we can talk about your cloud project and evaluate the most suitable solution for your business.

How to measure Resilience and success in Machine Learning and Artificial Intelligence models?

ML and AI are powerful tool that can be used to solve complex problems with minimal effort. With the rapid advances in technology, there still exists many challenges when it comes to making sure these models are resilient and reliable.Resilience is the ability of a system to resist and recover from unexpected and adverse events. In the context of AI and ML systems, resilience can be defined as the ability of a system to continue functioning even when it encounters unexpected inputs, errors, or other forms of disruptions.

 

Measuring resilience in AI/ML systems is a complex task that can be approached from various perspectives. Fortunately, there are some steps you can take to ensure your ML models are built with robustness. There is absolutely no one-size-fits-all answer to measuring resilience in AI and ML systems. However, there are a number of factors that can be considered when designing a resilience metric for these systems.

 

  • It is important to consider the types of failures that can occur in AI and ML systems. These failures can be classified into three categories: data corruption, algorithm failure, and system failure. Data corruption refers to errors in the training data that can lead to incorrect results. Algorithm failure occurs when the learning algorithm fails to connect a correct solution. System failure happens when the hardware or software components of the system fail. In other terms it’s also called robustness testing. This type of testing involves subjecting the AI/ML system to various types of unexpected inputs, errors, and perturbations to evaluate how well it can handle these challenges. Thus the system’s resilience can be measured by how well it continues to perform its tasks despite encountering these challenges. A resilient system is one that is able to recover from failures and continue operating correctly.

 

  • It is necessary to identify what creates a resilient AI or ML system. It is also important for a resilient system to be able to detect errors and correct them before they cause significant damage. Usually, the fault injection method makes easier to evaluate how the system response to intentionally introduced faults and if it’s able to detect & recover. With this method, the resilience of the system can be measured by how quickly and effectively it can recover from these faults. It is also mandatory to develop a metric that can be used to measure resilience in AI and ML systems. This metric takes into account the type of failures that can occur, as well as the ability of the system to recover from those failures.

 

  • The performance monitoring of the AI/ML systems cannot be considered insignificant as this monitors the performance of the AI/ML system over time, including its accuracy, response time, and other metrics. The resilience of the system can be measured by how well it maintains its performance despite changes in its operating environment.

Overall, measuring resilience in AI/ML systems requires a combination of methods and metrics that are tailored to the specific application and context of the system. Along with that, we also need to ensure that the data which is use to train ML models is representative of the real-world data. This means using a diverse set of training data that includes all the different types of inputs our model is likely to see. For example, if our model is going to be used by people from all over the world, we need to make sure it is trained on data from a variety of geographical locations.

 

Last but not the least, ML systems need regular training “refreshers” to keep them accurate and up-to-date. Otherwise, the system will eventually become outdated and less effective. There are a few ways to provide these training refreshers. AI/ML systems are typically trained using large amounts of data to learn patterns and relationships, which they then use to make predictions or decisions. However, the data that the system is trained on may not be representative of all possible scenarios or may become outdated over time. One way is to simply retrain the system on new data periodically. In addition, the system may encounter new types of data or situations that it was not trained on, which can lead to decreased performance or errors.

 

To address these issues, AI/ML systems often require periodic retraining or updates to their algorithms and models. This can involve collecting new data to train the system on, adjusting the model parameters or architecture, or incorporating new features or data sources.This can be done on a schedule (e.g., monthly or quarterly) or in response to changes in the data (e.g., when a new batch of data is received).

 

Another way to provide training refreshers is to use transfer learning. With transfer learning, a model that has been trained on one task can be reused and adapted to another related task. This can be helpful when there is limited training data for the new task. For example, if you want to build a machine learning model for image recognition but only have a small dataset, you could use a model that has been trained on a large dataset of images (such as ImageNet).

 

Measuring the resilience of AI/Ml systems requires extended range of tools and expertise. We at Xorlogics make sure to produce the best model with the highest standard of resilience & accuracy. Tell us about your business needs and our experts will help you find the best solution.

Human Machine Partnership – Is 2018 the year of #MachineLearning?

Human Machine Partnerships2018 is all about the further rapprochement of man and machine. Dell Technologies predicts the key IT trends for 2018. Driven by technologies such as Artificial Intelligence, Virtual and Augmented Reality and the Internet of Things, the deepening of cooperation between man and machine will drive positively the digitization of companies. The following trends will and are shaping 2018:

 

Companies let AI to do data-driven thinking

 

In the next few years, companies will increasingly use the opportunity to let artificial intelligence (AI) think for themselves. In the AI systems, they set the parameters for classifying desired business outcomes, define the rules for their business activities, and set the framework for what constitutes an appropriate reward for their actions. Once these sets of rules are in place, the AI systems powered by data can show new business opportunities in near real time.

 

The “IQ” of objects is increasing exorbitantly

 

Computing and networking items over the Internet of Things are becoming increasingly cost effective. The embedding of intelligence into objects will therefore make gigantic progress in 2018. Networked device data, combined with the high levels of computing power and artificial intelligence, will enable organizations to orchestrate physical and human resources automatically. Employees are becoming “conductors” of their digital environments and smart objects act as their extension.

 

IQ of Things

 

AR headsets ultimate comeback in 2018

 

Its economic benefits have already been proven by augmented reality (AR). Many teams of designers, engineers or architects are already using AR headsets. Whether to visualize new buildings, to coordinate their activities on the basis of a uniform view of their developments or to instruct new employees “on the job” even if the responsible instructor cannot be physically present at the moment. In the future, AR will be the standard way to maximize employee efficiency and leverage the “swarm intelligence” of the workforce.

 

AR headsets

 

Strong bond of customer relationship

 

Next year, companies will be able to better understand their customers through predictive analytics, machine learning (ML), and artificial intelligence (AI) and use these technologies to improve their customer first strategies. Customer service will perfectly maintain the connection between man and machine. It will not be first-generation chatbots and pre-made messages that address customer concerns in the service, but teams of people and intelligent virtual agents.

 

Deeper Relationship with Customers

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The “Bias Check” will be the new spell checker

 

Over the next decade, technologies such as AI and Virtual Reality (VR) will enable those responsible to evaluate information without prejudgment and make decisions in an entirely balanced way. In the short term, AI will be used in application and promotion procedures to bring out conscious or unconscious prejudices. VR is increasingly being used as an interviewing tool to cover the identity of applicants with the help of avatars. “Bias checks” – “prejudice checks” – could become the standard procedure in decision-making processes in the future, just as spell-checking is today when it comes to writing texts.

 

Bias check

 

The mega-cloud is coming up

In 2018, an overwhelming majority of companies will adopt a multi-cloud approach and combine the different cloud models. To overcome the associated cloud silos, the next step will be the mega-cloud. It will interweave the different public and private clouds of companies in such a way that they behave as a single holistic system. With the help of AI and ML, this IT environment will be fully automated and consistently evaluated.

 

mega-cloud

 

IT security is becoming more important than ever

 

In today’s increasingly connected world, IT security companies need more than ever to rely on third parties. They are no longer individual instances, but parts of a bigger whole. Even the smallest errors in any of the connected subsystems can potentiate to fatal failures in the entire ecosystem. In particular, for multinational corporations, it’s a must in 2018 to prioritize the implementation of security technologies. This development is further fueled by new regulations, such as the GDPR regulation of the EU.

 

 

E-sports gaming industry ready for mainstream

 

Not least driven by virtual reality, the phenomenon of e-sports for companies in the media and entertainment industry 2018 finally become a fixture. Millions of other players and viewers are jumping on the bandwagon and making continuity e-sports mainstream for 2018. This phenomenon is representative of a bigger trend: even original physical activities such as sports are digitized. In the future, every business will be a technological business, and people’s free time will be shaped by networked experiences.

 

“People have been living and working with machines for centuries,” says Dinko Eror, Senior Vice President and Managing Director, Dell EMC Germany. “In 2018, however, this relationship is reaching a whole new level: man and machine will be more intertwined than ever, and that will change everything – from the way we do business to the design of leisure and entertainment.”

Business under pressure: Employees want same simple access to #CloudApplications as consumers

KEYFINDINGS - TWO FACTOR authentication

 

According to Gemalto research, nearly two-third (64%) of IT executives admit their security teams are considering using consumer-level access to cloud services in enterprise IT. The reason behind is the increasing spread of cloud applications and the use of different devices in companies.

 

Gemalto’s Identity and Access Management Index 2018 shows that the majority of them (54%) believe the authentication methods implemented are not as reliable as those used on popular sites like Amazon and Facebook application. For the index, more than 1,000 decision-makers from the IT sector were interviewed worldwide.

 

Due to the increasing number of cloud applications, more and more employees are performing their activities remotely. Thus, the pressure to strengthen the authentication mechanisms, while maintaining the user-friendliness increases. IT decision makers are therefore keen to introduce a “consumerized” filing process. In fact, 70% of IT professionals believe that consumer authentication methods can be adopted to secure access to corporate resources.

 
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Despite these findings, 92% of IT managers express their concern that employees could also use their personal credentials in the work environment. However, 61% agree that they still do not implement two-factor authentication to access their networks and are thus easily exposed to cybercrime attacks.

 

At the same time, there seems to be a belief that new approaches to cloud access will solve these issues. Of respondents, 62% believe cloud access management tools could simplify the user sign-in process. On the other hand, 72% say that behind the idea of introducing a cloud-based access solution is the desire to reduce the risk of massive security breaches. In addition, 61% of respondents believe that inefficient identity management in the cloud environment is a key driver behind the adoption of a cloud-based access management solution. This in turn illustrates that scalability and management overhead are also high on the IT staff worries list.

 

“These results highlight the IT-related issues of finding a balanced solution that combines the need for a simple, easy-to-use login process with the security you need,” said Francois Lasnier, SVP for Gemalto’s Identity and Access Management. Although there is a need to facilitate operations for employees, this is a fine line. IT and business managers would be best advised to first identify the risks and vulnerabilities associated with the various applications used in their organization and then use the appropriate authentication method. In this way, they can ensure a user-friendly login process while maintaining access security.

 

With the increase in remote access, the cloud and secure access to applications have become important factors for businesses. As a result, nearly all (94%) of respondents believe that cloud-based access management plays a key role in bringing applications to the cloud. 9 out of 10 people believe that ineffective cloud access management can cause issues such as security issues (52%), inefficient use of IT staff time (39%), and increased operational costs and IT costs (38%). Although cloud application protection is paramount, only three of the 27 applications a company uses on average are protected by two-factor authentication.

 

Study also highlighted that the rapid growth of cloud applications brings many benefits to businesses, but also brings with them a high degree of fragmentation in terms of their ability to provide access security to numerous cloud and in-house applications. Without effective access management tools, it can increase the risk of security breaches, insufficient visibility into access events, and non-compliance, as well as hinder a company’s ability to scale in the cloud.

DevOps, Integration and Deployment- Why is this important and how to achieve results?

New technologies often have a hard time in the beginning. As always, a large number of doubters are contrasting to early users and adaptation. We still remember today the difficulties that VMware had with the acceptance of its virtualization concept in the early years, which increased in importance only after a few years and today plays a central role in IT.

 

A similar enlargement seems to be happening to DevOps at the moment. This technology stayed a hot subject for several years, but it has not arrived everywhere yet. But the willingness to use DevOps is growing steadily and the market is clearly moving upwards. Because as nowadays everything is changing faster and faster, existing applications must constantly be adapted, at an ever-increasing pace. Concerning the numbers of the present situation, the annual report of “State of DevOps Report 2017” reflects that the sum of employees in DevOps positions has doubled since 2014. Complications also often occur between developers and operational teams. The DevOps approach is a good way to overcome these problems.

 

What is DevOps? Well, more than a methodology for software development, DevOps is a culture, which is necessary to meet the current needs of companies in the development of software, websites, applications, etc. In the traditional model, the requirements for software were clear and carefully defined in advance. The definition of the product itself was also stable. The developers were responsible for the coding of the software, and the operational teams then had to implement it on the company’s systems or the web.

 

Sure, there are industries that are DevOps-savvy. Companies, for example, who have already taken the first steps in terms of digital transformation and develop their own applications and software. Meanwhile, companies that are still at the beginning of their digital transformation and do not yet run DevOps are asking themselves, “What is DevOps at all, what has Digital Transformation to do with it, and why do we need that?”

 

The Digital Transformation reveals internal company problems in DevOps implementation

 

The need for DevOps in itself arises only through the use of new IT technologies. The development and operational teams of the company that was previously completely independent of each other are brought up to work together. Optimizing this cooperation for the benefit of the company is the basic idea. IT is the ideal example for this. Traditionally, it has always been a stand-alone entity that ultimately provided only IT services to the rest of the company but otherwise had little intersections with other departments. Chronically overloaded, the IT of many companies had even isolated itself and developed a genuine hatred to many new IT-related requirements of the users, which was not seen as the core task of IT. Everything that was not part of the job of providing IT services was literally ironed out, for whatever reason.

 

At the same time, the value of digital applications has increased. The Internet in general, cloud computing, e-commerce, mobile apps, social media companies today offer companies many new ways to grow their businesses. However, the in-house IT is rarely responsible for the development of these opportunities, but they are mostly software developers who are employed in new in-house development departments and work more with marketing than with IT. This obvious gap between software development and IT operations teams is forcing the management of many companies to better integrate these departments in order to better implement innovative ideas.

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This is necessary because the current structure of collaboration between development and IT is a real drag: developers are motivated to provide new applications and functionality, but their responsibility ends when the software is handed over to IT operations. And the Operations team plays in software development so far anyway no role, but only in the provision. Thus, the goals for developers and operations are in many cases totally contradictory, and the lack of cooperation between the two has a strong negative impact on the development and implementation of IT projects involving both sides.

 

The goal of DevOps practices is to eliminate these issues so that companies can implement new, digital projects faster and better. Thus, any company that seeks to implement such projects as part of its digital transformation can benefit from DevOps.

 

How is DevOps implemented in practice?

 

Of course, implementing DevOps successfully in practice is easier said than done. Implementing DevOps is far more complicated than just putting together the initial syllables of two words. Also, it is not enough just to buy a new technology or platform to fix the problem. The implementation is rather on two levels, the organizational and the technological level. Both levels need to be planned as part of a company-specific DevOps integration to work seamlessly together.

 

The integration of development and operations succeeds on an organizational level as a company identifies processes and practices that make teams work together more effectively. Technologically, DevOps seeks to automate the process of software delivery and infrastructure changes. Once automated, processes take much less time out of the IT department and greatly accelerate the delivery of new software. With the extra time, IT teams can more actively focus on new projects, and development teams can dramatically shorten their development cycles. In order to automate processes and improve development, there are several DevOps platforms whose implementation can make sense.

 

DevOps – part of the Digital Transformation

 

A company’s IT can make an important contribution to the success or failure of an organization. An important role for the future of an organization plays in this regard, the digital transformation, which is often led by the IT but must also include other parts of the company. DevOps is one of the means to successfully implement the Digital Transformation internally, as it provides a way to seamlessly integrate all parts of the IT environment into one project. But it’s not just about technology, it’s about corporate culture and internal processes. Organizations need to reunite these three areas to be in the fast lane when it comes to digital transformation.

CISCO: Cloud networking trends

Essential characteristics of cloud

The annual Cisco Global Cloud Index (2016-2021) shows that data-center traffic is growing rapidly due to increasingly-used cloud applications. According to the study, global cloud traffic will reach 19.5 zettabytes (ZB) in 2021. This is an increase of 6.0 ZB compared to 2016, which is 3.3 times higher, with an annual growth rate of 27%. In three years, cloud traffic will account for 95 % of total traffic, compared to 88 % in 2016.

According to the study, both B2C and B2B applications contribute to the growth of cloud services. For consumers, video streams, social networking, and web search are among the most popular cloud-based apps. For employees, it’s ERP, collaboration and analysis solutions.

 

Security and IoT as a growth driver

Increasing IoT applications, such as smart cars, smart cities, connected healthcare and digital care, require a highly scalable server and storage solutions to meet new and expanded data center needs. In 2021, there will be 13.7 billion IoT connections, compared to 5.8 billion in 2016, the study said.

In the past, security concerns were a major barrier to cloud usage. Improvements in data center control and data control reduce the risk to businesses and better protect customer information. New security features coupled with tangible benefits from cloud computing, such as scalability and efficiency, play an important role in cloud growth.

 

Hyperscale Datacenters Growth

The increasing demand for data center and cloud capacity has led to the development of hyper-scaled public clouds based on Hyper-scale data centers. The study predicts that there will be 628hyper-scale data centers worldwide in 2021, compared to 338 in 2016, nearly the double. In three years Hyperscale data centers will have:

  • 53 % of all data center servers (2016: 27 %)
  • 69 % of the computing power of data centers (2016: 41 %)
  • 65 % of data center data stored (2016: 51 %)
  • 55 % of all datacenter traffic (2016: 39 %)

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The growth of data center applications is exploding in this new multi-cloud world. The predicted increase requires further innovation, especially in the public, private and hybrid cloud sectors.

 

Virtualization of data centers and cloud computing growth

By 2021, 94 % of the workloads and server will be processed in cloud data centers, the remaining 6 % in traditional data centers. All data center workloads and server instances will more than double (2.3x) between 2016 and 2021, while cloud-based workloads and server instances will almost triple (2.7x) over the same period).

The density of workloads and server instances in cloud data centers was 8.8 in 2016, rising to 13.2 by 2021. In traditional data centers, density increases from 2.4 to 3.8 over the same period.

 

Big Data and IoT fuel data explosion

Worldwide, the amount of data stored in data centers will increase almost fivefold, from 286 Exabytes in 2016 to 1.3 ZB 2021 (4.6x, with annual growth of 36%). Big data will grow almost 8x, from 25 to 403 EB. In 2021, it will contain 30 % of all data stored in data centers compared to 18 % in 2016.

The amount of stored data in devices in 2021 will be 4.5 times higher at 5.9 ZB than data stored in data centers. Mainly due to the IoT, the total amount of generated data (which will not necessarily be saved) will reach 847 eg by 2021, in 2016 it was 218 eg. This generates more than 100 times more data than saved.

 

Applications contribute to data growth

By 2021, Big Data will account for 20% (2.5 ZB annually, 209 EB monthly) of data center traffic, compared to 12 % (593 EB annually, 49 EB monthly) in 2016, video streaming will account for 10 % of data center traffic, compared to 9 % in 2016. Video will account for 85 % of data center traffic to users, compared to 78 % in 2016, internet search will account for 20 % of data center traffic, compared to 28 % in 2016, social networks will account for 22 % of data center traffic, compared to 20 % in 2016.

 

SaaS is the most popular cloud service model by 2021

By 2021, 75 % (402 millions) of all cloud workloads and server instances will be SaaS-based, compared to 71 % (141 million) in 2016 (which represents 23 % of annual growth).

16 % (85 millions) of all cloud workloads and server instances will be IaaS-based, compared to 21 % (42 million) in 2016 (which represents 15 % annual growth).

9 % (46 millions) of all cloud workloads and server instances will be PaaS-based, compared to 8 % (16 million) in 2016 (which represents 23% annual growth rate).

 

As part of the study, cloud computing includes platforms that provide continuous, on-demand network access to configurable resources (e.g., networks, servers, storage, applications, and services). These can be quickly deployed and shared with minimal management effort or interactions with service providers. Deployment models include Private, Public, and Hybrid Clouds. Cloud data centers can be operated by both service providers and private companies.

 

The key differences between cloud data centers and traditional data centers are virtualization, standardization, automation, and security. Cloud data centers offer higher performance, higher capacity, and easier management compared to traditional data centers. Virtualization serves as promoter for the consolidation of hardware and software, greater automation and an integrated approach to security.

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