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From Raw Data to Profitable Insights: Tools and Strategies for Successful Data Monetization

Data monetization has become an increasingly important topic in the world of business and technology. As companies collect more and more data, they are realizing the potential value that this data holds. In fact, according to a report by 451 Research, the global market for data monetization is projected to reach $7.3 billion by 2022. This is achieved through various strategies such as selling raw or processed data, providing analytics services, or creating new products based on the data.

There are many different approaches to data monetization, each with its own unique benefits and challenges. However, many organizations struggle with how to effectively monetize their data assets. In order to effectively monetize data, businesses need the right tools and strategies in place. These tools help collect, analyze, and visualize data to uncover valuable insights that can be turned into profitable opportunities. Below is a short list of essential tools used for successful data monetization.

 

  • Data Collection Tools: The first step in data monetization is collecting relevant and accurate data. This requires efficient and effective data collection tools such as web scraping software, API integrations, IoT sensors, and customer feedback forms. These tools help gather large amounts of structured and unstructured data from various sources like websites, social media platforms, customer interactions, or even physical sensors.

 

  • Data Analytics Platforms: Analytics tools play a crucial role in making sense of complex datasets by identifying patterns and trends that would otherwise go unnoticed. By leveraging these platforms, businesses can gain valuable insights that can be used for decision-making processes. They provide powerful reporting dashboards that allow businesses to visualize their KPIs with interactive charts, graphs, or maps helping them understand how their products are performing in real-time.

 

  • Business Intelligence Tools: These are applications designed specifically for reporting and dashboarding purposes. They allow users to input raw or analyzed data from various sources and present it in a visually appealing manner through charts, graphs or maps.

 

  • Customer Relationship Management Systems: CRM systems are essential tools for gathering customer-related information such as demographics, purchase history or behavior patterns. By analyzing this data, businesses can better understand their customers and tailor their products or services to meet their specific needs.

 

  • Data Management Platforms: DMPs are software solutions that help businesses to store, and manage large volumes of data from different sources. They allow for the integration of various data types, such as first-party and third-party data, which can then be used to create targeted marketing campaigns. It also provides features such as real-time processing capabilities, automated workflows for cleansing and transforming data, ensuring accuracy and consistency.

 

  • Data Visualization Tools: Data visualization tools help businesses present data in a compelling and visually appealing manner, making it easier for decision-makers to understand complex information quickly. These tools provide interactive dashboards, charts, maps, and graphs that can be customized according to the needs of the business.

 

  • Artificial Intelligence & Machine Learning: AI & ML technologies can help organizations extract valuable insights from their data by identifying patterns, predicting trends, and automating processes. AI-powered chatbots also enable businesses to engage with customers in real-time, providing personalized recommendations and increasing customer satisfaction.

 

  • Cloud Computing: Cloud computing provides scalable storage and computing power necessary for processing large amounts of data quickly. It also offers cost-effective solutions for storing and managing data as businesses can pay only for the services they use while avoiding expensive infrastructure costs.

 

  • Demand-side platforms: DSP help organizations manage their digital advertising campaigns by targeting specific audiences based on their browsing behavior or interests. These platforms allow businesses to use their data to segment and target customers with personalized messaging, increasing the chances of conversion and revenue generation.

 

  • Monetization Platforms: Finally, businesses need a reliable monetization platform that helps them package and sell their data products to interested buyers easily.

Data is certainly more than you think! It’s a valuable resource that can be monetized across your organization. So get in touch with us and learn how data monetization can transform your business.

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.

The role of AI-driven Forecast Models in Business Operations

 

In today’s fast-paced business landscape, organizations are constantly seeking innovative ways to optimize their operations and unlock hidden sources of value. Artificial Intelligence (AI) has emerged as a game-changer, revolutionizing various industries with its data-driven insights & predictive capabilities. AI-driven forecast models, in particular, have the potential to transform how businesses make decisions and operate efficiently. In this article, we will explore the power of AI-driven forecast models and their impact on enhancing operational efficiency and value creation.

What are AI-driven forecast models?

AI-driven forecast models are advanced analytical tools that leverage machine learning algorithms and data analysis techniques to predict future outcomes based on historical data patterns. These models can process vast amounts of structured and unstructured data, learning from historical trends and making accurate predictions about future events.

 

The Role of AI in Operations

AI plays a crucial role in transforming traditional operational processes. By analyzing complex datasets at unparalleled speeds, AI-driven forecast models empower businesses to make well-informed decisions promptly. They enable organizations to proactively address challenges and opportunities, thereby optimizing various aspects of their operations.

 

  • Extracting insights from vast data: One of the primary advantages of AI-driven forecast models is their ability to process and analyze vast amounts of data from multiple sources. Businesses can gain valuable insights from this data, allowing them to identify patterns, trends, and correlations that were previously hidden or too complex to discover using conventional methods.

 

  • Improving accuracy and reducing errors: AI-driven forecast models boast exceptional accuracy levels when predicting future outcomes. By minimizing human intervention, these models eliminate the risk of human errors and biases, providing reliable and consistent forecasts. Organizations can rely on these predictions to make better decisions and allocate resources more effectively.

 

  • Allocating resources effectively: Resource allocation is a critical aspect of operational management. AI-driven forecast models can help organizations optimize resource allocation by analyzing historical data and predicting demand patterns. This enables businesses to allocate their resources efficiently, ensuring that they meet customer demands while minimizing waste and unnecessary costs.

 

  • Inventory management and supply chain optimization: AI-driven forecast models revolutionize inventory management by predicting demand fluctuations and inventory needs accurately. With this information, businesses can streamline their supply chains, reducing inventory holding costs and avoiding stockouts or overstock situations.

 

  • Predicting customer preferences: Understanding customer behavior is vital for businesses to tailor their products and services to meet customers’ preferences effectively. AI-driven forecast models analyze customer data and behavior to predict trends and preferences, helping organizations stay ahead of the competition and retain their customer base.

 

  • Anticipating market trends: In a dynamic marketplace, predicting market trends is crucial for business survival and growth. AI-driven forecast models leverage historical data and market indicators to anticipate upcoming trends, enabling organizations to respond proactively to changing market conditions and gain a competitive advantage.

 

  • Real-time monitoring and detection: AI-driven forecast models facilitate real-time monitoring of operations, enabling organizations to identify inefficiencies promptly. With instant alerts and insights, businesses can take immediate corrective actions, preventing potential disruptions and enhancing operational efficiency.

 

  • Implementing corrective actions: By pinpointing operational inefficiencies, AI-driven forecast models guide organizations in implementing targeted corrective actions. Whether it’s optimizing production processes or improving customer service, these models provide valuable recommendations to enhance overall operational performance.

 

  • Streamlining processes with AI: AI-driven forecast models can streamline complex processes within an organization, reducing manual intervention and associated time delays. By automating repetitive tasks, businesses can free up resources and focus on strategic decision-making, driving efficiency and productivity.

 

  • Automating repetitive tasks: AI automation streamlines routine tasks, enabling employees to concentrate on high-value activities that require human creativity and problem-solving skills. Automation also minimizes the risk of errors, leading to increased productivity and cost savings for businesses.

 

The Future of AI-Driven Forecast Models

 

  • Advancements and potential applications: As AI technology continues to evolve, so will AI-driven forecast models. Advancements in machine learning algorithms, computing power, and data availability will unlock new possibilities for forecasting accuracy and expand the range of applications across industries.

 

  • Ethical considerations in AI adoption: As AI-driven forecast models become more ubiquitous, ethical considerations become critical. Organizations must adhere to ethical guidelines and principles to ensure responsible AI deployment, safeguarding against potential negative impacts on society and the workforce.

 

Conclusion

AI-driven forecast models are a transformative force in today’s business landscape. By leveraging vast amounts of data and powerful algorithms, these models enable businesses to optimize operations, enhance decision-making, and unlock multiple sources of value. As organizations embrace AI’s potential, they must also address challenges related to data privacy, bias, and ethical considerations to harness the true power of AI-driven forecast models.

How IOT can improve the Project Management Process

The world of project management is rapidly evolving, and with the emergence of Internet of Things (IoT) technology, managing projects has become even more efficient. IoT has opened up a whole new world of possibilities for project managers who are looking to improve their processes and enhance productivity. IoT has the potential to significantly enhance the project management process by providing real-time data, improving communication and collaboration, optimizing resource allocation, and enabling proactive decision-making. Here are several ways in which IoT can improve project management:

IoT can be a game-changer in project management by allowing real-time data collection and monitoring of various aspects of a project. For example, IoT devices, such as sensors and connected equipment, can gather real-time data on various project parameters, including progress, performance, environmental conditions, and resource utilization. This data can be automatically transmitted to project management systems, providing up-to-date insights that enable better monitoring, tracking, and decision-making.

 

IoT devices also allow project managers to remotely monitor project sites, equipment, and assets in real-time. Through connected cameras, sensors, and wearables, project managers can assess on-site conditions, detect potential issues or delays, and ensure compliance with safety protocols. This capability improves efficiency and reduces the need for physical presence at project locations. Thus, managers can easily access data on from any remote location to monitor performance metrics in real time.

 

In addition, IoT sensors embedded in equipment, machinery, and vehicles can collect data on their usage, performance, and maintenance needs. By analyzing this data, project managers can optimize resource allocation, schedule preventive maintenance, and reduce downtime. This ensures that resources are utilized efficiently, delays are minimized, and costs are optimized.

 

Another benefit of using IoT in project management is its ability to automate routine tasks through machine learning algorithms. These algorithms analyze large amounts of data generated from sensors and make predictions based on patterns identified over time.

 

Furthermore, IoT enables better communication among team members by providing a centralized platform for sharing information and updates. This leads to increased collaboration, as everyone has access to the same data and insights. IoT helps reduce costs associated with traditional project management methods by eliminating unnecessary paperwork and travel expenses. With everything managed digitally through connected devices, there are fewer physical resources required overall. Incorporating IoT into your project management process offers many valuable benefits that ultimately lead to smoother operations and successful outcomes.

 

Another benefit of IoT in project management is improved efficiency. By automating certain tasks with smart devices like sensors or drones, teams can save time and focus on more important aspects of the project. Additionally, data collected from these devices can be used to identify areas where improvements could be made further down the line

 

Conclusion

 

The Internet of Things (IoT) is a game-changer in project management. It is widely expected that the adoption of IoT will continue to grow across industries as more companies recognize its potential and benefits. The IoT market has been expanding rapidly in recent years, with a wide range of organizations implementing IoT solutions to improve their operations, enhance customer experiences, and drive innovation.

By leveraging the power of IoT, project managers can gain real-time insights, improve decision-making, optimize resource allocation, enhance collaboration, and mitigate risks. However, successful implementation requires careful planning, integration with project management systems, data security considerations, and a clear understanding of the specific project requirements and objectives.

The future of project management lies with IoT integration as it enables seamless collaboration among team members regardless of location or time zone. With proper utilization of this technology, businesses will achieve optimal performance levels leading to successful completion of projects within set timelines and budgets.

Digital Trust 2019: Trends in The Artificial Intelligence Era

Artificial intelligence (AI) has rapidly developed in recent years. Today, AI tools are used widely by both private and public sector organizations around the globe. The capabilities of AI now and in the near future are creating extensive and significant benefits for individuals, institutions and society.

The foundation of AI is data and understanding the patterns in these data to make a smarter automated task. The collection of these data is increasingly controlled by regulations and user preferences. Organizations must answer questions such as how to deliver practical compliance with data protection laws and norms when building and implementing AI technology and on the tension between AI and existing data protection legal requirements.

 

If the AI ​​service cannot provide an appropriate level of trust, this data may not be available over time. Without trust, there is no data. Without data there is no AI. As a result, organizations have both an opportunity and an obligation to develop principles, best practices and other accountability tools to encourage responsible data management practices, respect and even reinforce data protection, and remove unnecessary barriers for the future development of these innovative technologies. However, in2019, trust will be essential for success. Below are five forecasts of tensions related with data protection.

 

Trust becomes the new currency

Shakespeare wrote, “Love everyone, trust a few, do no harm to none”. Those vendors who can build long-term trust with their customers have unique added value. Very few will be able to achieve this and thereby increase their enterprise value. Trust will be given a monetary value in 2019 and we will move towards a trust-based economy.

 

Data ethics will become more important as a discipline

The foremost practical question for data ethics is whether there is anything special about data such that collecting, manipulating, and applying it requires a distinct code of ethics. On 20 November 2018, the United Kingdom founded the Center for Data Ethics and Innovation, the first public body to address the “new ethical issues arising from the rapid development of technologies such as artificial intelligence”. The way data is used today is more than just a technical phenomenon. It’s a political, social, and even mythological phenomenon that has consequences for how we organize our lives and express our values. Whatever ethical principles are developed in connection with data, they should account for dynamics that extend beyond technical limitations. Data analytics should be viewed as a phenomenon with consequences beyond technology, and the community should demand that data scientists and practitioners consider those consequences.

In 2019, data ethics as a discipline will become increasingly important in both governments and academia. Most discussions so far about AI ethics have focused on the results of the AI, not the data inputs fed by the AI. These new institutions will focus on inputs and should be taken seriously by providers as they are both a source of best practices and a pioneer for future legislation.

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Everyone will shout, “Trust me!”

‘Trust’ will stand in the slogan or marketing message of all AI and modern technology providers. Although consumers place a high value on trust and integrity, they will only find a few useful landmarks in marketing noise. “Who should I trust?” May not be so easy to answer in the blink of an eye.

 

Surprisingly, consumers will give dubious providers a second chance

Given the hurly-burly and lack of an objective, easy-to-use test of trust, consumers will not really know how to value trust and what action to take when it’s lost. As a result, consumers will not drop these providers in 2019 due to a first breach of trust, which unfortunately continues to support negative business practices. However, these second chances will gradually disappear as consumers learn to answer the question “Why should I trust you?”.

 

Often, data will disappear

This year, some providers that collect data improperly, will get caught and go through legal or/and economic consequences. Others will slip through. But in both cases, nobody will know what actually happened with his data. Many are expected to land in the black market. But on the basis of regulatory requirements of GDPR, industry will be forced to adopt sever guidelines for the use and processing of data.

 

These are all big challenges, as they occur in many technologies with disruptive potential in the early stages. But can the technology industry really talk about trust? As Albert Einstein said, “Anyone who does not take truth in small matters cannot be trusted in large ones”. In 2019, we will see the first generation of AI startups trusted by credible, sustainable, deep-rooted value and not a marketing slogan. Only then will Artificial Intelligence be able to assert itself in a sustainable way to truly change life.

AI IN CUSTOMER COMMUNICATION – 5 PRELIMINARY QUESTIONS

 

AI is set to be a game-changer for businesses across every industry. Artificial intelligence is undoubtedly changing the way companies address and interact with their customers.  Pluq, the increasing adoption of digital language assistants such as Alexa and Co., Siri and Amazon Echo, in private households is leading-edge.

 

A new study by Bitkom and Deloitte on the future of consumer technology showed that, in addition to 13%, who already had an intelligent virtual assistantin 2018, 4% of those surveyed are planning a purchase a voice assistant in 2019 and 27% can imagine controlling devices by voice in the future. The fact that, according to Gartner, 30% of companies will use AI for at least one key sales process in 2020, which encourages AI adaptation. Companies are faced with the huge task of adapting to the increasingly complex communication needs of their customers. Therefore, language assistants are being integrated into more and more devices.

 

Study highlighted the rapid rise of intelligent language assistants in 2018 and in the coming years we will control more and more devices with our voice. Which opens gates to a new billion-dollar market.

 

Despite all the forward-looking tips and statistics, many companies are still wondering how they can ideally use AI for themselves. Also, the costs that result, the impact on employees and customer satisfaction is not really measurable for many.

To get an overview here you should ask the following basic questions:

 

  1. What do customers really want?

Often, when answering this question, it helps to have a closer look at the customer database. Age structure, nature and complexity of incoming requests provide a clear direction. For example, an airline can quickly and efficiently handle the query of travel times with the help of artificial intelligence. But customers still preferer a human contact when questions about insurance details or specific health problems rise.

 

  1. How is automation currently being used?

Automation is not just a topic for companies since the introduction of artificial intelligence. Many have already integrated automatic systems such as IVR (Interactive Voice Response) for telephone inquiries and automated e-mails or SMS into customer communication – systems that have proven themselves so far. Implementing artificial intelligence here is not necessarily the way to go. Rather, one should analyze how existing systems can be improved to meet evolving customer needs. For example, an automated language solution with machine learning in the background could complement an existing solution and offer the customer an improved contact experience.

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  1. How will the employees react?

The biggest fear among employees is that artificial intelligence makes them redundant in the foreseeable future, for example through chatbots, and as a result they lose their jobs. The fear since the beginning of the industrial age, that machines will take over humans and jobs, is the biggest communications challenge. This is further aggravated by; a lack of understanding of AI compounded by confusing communication by various players during the current hype cycle. There is a need for constant communication and increasing awareness to improve the understanding and applications of AI. One just needs to look at history to conclude that every technological change created an explosion of new jobs and services and, overall, generated more wealth for all.

 

  1. How to find the right AI solution and how should an implementation work?

There are already a variety of AI solutions for various functions, including, for example, Natural Language Processing (NPL). You should basically get an overview of the solution providers – especially those who have a platform with interfaces to different AI solutions in their portfolio. It is essential, however, that there is a precise idea of ​​the existing communication infrastructure and the improvements to be achieved in customer communication. For example, cloud-enabled contact center vendors and specialized integration offerings with an end-to-end AI package can bridge the gap between existing functionality and the AI ​​skills needed to meet existing needs.

 

  1. How is one prepared for the future?

AI will inevitably play a major role in the future of customer contact. But there are many details to consider when planning implementation – even though the customer base is not yet fully receptive to this technology, the rapid development of AI and the ability to address more complex issues can lead to that acceptance which will increase significantly in just a few years. Also, the increasing adaptation of consumers to these types of interfaces will increase their acceptance to, and expectation of, this technology. Long-term planning should therefore always leave room to introduce new innovations as soon as they can offer defined added value.

 

This is precisely why Cloud-based contact center and integration technologies are available that are inherently capable of adapting flexibly to new developments and introducing new third-party connectors. This open technology has the advantage of reducing the risks for future AI and contact center planning and provides the ability to introduce functionality as needed. This avoids being late for a new innovation and losing valuable competitive advantages.

 

Impact of Artificial Intelligence on the Future of Labor Market

Impact of Artificial Intelligence on the Future of Labor Market

Disruptive changes to business models are having a profound impact on the employment landscape and will continue to transform the workforce for over the coming years. Many of the major drivers of transformation currently affecting global industries are expected to have a significant impact on jobs, ranging from significant job creation to job displacement, and from heightened labour productivity to widening skills gaps. In many industries and countries, the most in-demand occupations or specialties did not exist 10 or even five years ago, and the pace of change is set to accelerate.

Artificial Intelligence (AI) is changing the way companies used to work and how they today. Cognitive computing, advanced analytics, machine learning, etc. enable companies to gain unique experience and groundbreaking insights.

 

AI is becoming ever more dominant, from physical robots in manufacturing to the automation of intelligent banking, financial services, and insurance processes – there is not a single industry untouched by this trend.

Through the advances in AI, people and businesses are experiencing a paradigm shift. It’s crucial that companies meet these expectations. As a result, artificial intelligence (AI) is becoming increasingly important to simplifying complex processes and empowering businesses like never before.

In such a rapidly evolving employment landscape, the ability to anticipate and prepare for future skills requirements, job content and the aggregate effect on employment is increasingly critical for businesses, governments and individuals in order to fully seize the opportunities presented by these trends—and to mitigate undesirable outcomes.

 

AI: Impact on the labor market

 

Whenever we discuss AI, opinions usually vary widely. The issue always separates those who believe that AI will make our lives better, and those who believe that it will accelerate human irrelevance, resulting in the loss of jobs. It is important to understand that the introduction of AI is not about replacing people but expanding human capabilities. AI technologies enable business transformation by doing the work that people are not doing so well – such as quickly, efficiently and accurately processing large amounts of data.

 

The relationship between humans and AI reinforces each other. Although one of the analyst studies suggests that around 30% of global working hours could be automated by 2030, AI can help by taking on the monotonous and repetitive aspects of current workers’ work. Meanwhile, these employees will focus on the types of work that are more strategic or require a more analytical approach. However, this also requires the retraining of the existing workforce at a certain level.

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This new way of working has begun to affect the job market: in fact, it is expected that the development and deployment of new technologies such as AI will create millions of jobs worldwide. In the future, millions of people will either change jobs or acquire new skills to support the use of AI.

 

AI skills: The Gap

 

While the AI ​​will be responsible for a significant transformation of the labor market, there is currently a gap between this opportunity and the skills available to the current workforce. When companies experiment with AI, many realize that they do not have the proper internal skills to successfully implement it. For the workforce, new education and skills are needed to adapt jobs to the new opportunities of AI. In return, new trainers are needed. AI technologies require the development and maintenance of new advanced systems. People with knowledge and experience in these new areas are in demand.

 

There is currently no agreement on who will take the responsibility to qualify current and future workers. Companies, governments, academic institutions and individuals could all be held responsible for the management of this retraining. To meet the current and future demand for AI, companies should create opportunities for their current employees to continue extra education-training so that they become the group of workers who will monitor and manage the implementation and use of AI with human and machine interaction. Only when all these different groups take responsibility, the workforce will be able to effectively develop the necessary AI skills and take the companies to the next level.

 

In the change of time

 

In summary, one can safely say that sooner or later, AI will lead to a redesign of workplaces. We assume that innovative options can be harnessed in more and more industries.

Above all, AI is a transformative force that needs to be channeled to ensure that it benefits larger organizations and the social cause. We should all be overwhelmingly involved and elaborate in making the most of it.

GDPR: Artificial Intelligences’ Major Blockage

The data protection and privacy law, which came into effect across the EU on 25thmay have a great impact on companies building machine learning systems. We know that in order to build these systems, companies’ needs large amount of data, but Big data is completely opposed to the basis of data protection.

 

According to the EU Data Protection Regulation, companies must meet three specified transparency requirements (along with other suitable safeguards) in order to better inform data subjects about the Article 22 (1) type of processing and the consequences:

 

  • inform the data subject purpose of data storage;
  • provide meaningful information about the logic involved; and
  • explain the significance and envisaged consequences of the processing.

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The logic behind is to be aware of how far this transparency provision is interpreted and whether companies have to fear or not.

 

AI is omnipresent: From the analysis of large and complex data sets such as genome data in medical research, Predictive Policing in the police and security sector to digital language assistants such as Apple’s Siri or Alexa of Amazon. Even fitness apps are increasingly relying on the use of AI and machine learning in order to be able to offer each user a tailor-made training plan optimized for them.

 

This trend has not gone unnoticed by politicians. After the European Commission presented a European concept in the field of artificial intelligence at the end of April, the parliamentary groups also took part on 26 June 2018 , to discuss over recommendations for action in the handling of artificial intelligence – especially in legal and ethical terms – by the summer break in 2020. The AI ​​concept of the European Commission also provides extensive research and development measures with the aim of promoting AI innovation in Europe.

 

Even if the use of AI does not necessarily have to be associated with the evaluation of personal data in every case of application, for example in banking and insurance, it also stays suitable for the comprehensive evaluation of personality traits (so-called “profiling” / “scoring”). According to European data protection authorities, an example of profiling and classifying is the following:

 

“a business may wish to classify its customers according to their age or gender for statistical purposes and to acquire an aggregated overview of its clients without making any predictions or drawing any conclusion about an individual. In this case, the purpose is not assessing individual characteristics and is therefore not profiling.”

 

It is therefore astonishing that the concepts of the EU Commission with regard to the data protection measurement of AI use have so far remained rather vague for companies.

 

Regardless of the admissibility of a particular procedure, these transparency obligations are often seen as extremely critical in the light of the protection of trade and business secrets. The reason for this is that the person concerned must also be provided with “meaningful information about the logic involved” and it is still unclear to what extent and to what amount this information is to be given. The key question is whether the person in charge, ie the company using the AI, is only required to explain the principles and essential elements underlying an automated decision-making process descriptively, or whether the disclosure of calculation formulas, parameters and algorithms can actually be demanded from this.

 

In any case, with the view expressed here, there is no obligation to disclose formulas and algorithms from the GDPR. The transparency provisions of the GDPR therefore only require “meaningful information about the logic involved” of automated decision-making, but not the actual publication of these logics. According to this, the responsible party owes only a description of the principles underlying an automated decision-making process, that is to say about the fundamental laws by which an algorithm makes decisions. The purpose of the GDPR obligations is therefore not (as often represented) to enable the concerned person to recalculate the results of an automated decision-making process, for example the “score” of the concerned person. This would require, for example, the specific calculation formula and the calculation parameters. Rather, in the context of the transparency provisions, for example in the context of a privacy policy, the data subject should only be given the opportunity to obtain advance information on the extent to which his data is processed by a particular service provider and, if appropriate, to look for alternatives.

 

This view is not contradicted by the requirement of “meaningfulness” of the required information. On the other side, for the average user, a comprehensible description of the underlying processes may represent a greater added value than the disclosure of the mathematical-technical logics themselves. Only by then a generally understandable description can meet the requirements of the GDPR. This requires that all information to be provided must be provided in an intelligible form and in a “clear and simple language”.

 

In summary, the GDPR lurks no real danger for the protection of know-how. Rather, their admissibility requirements and transparency obligations in the use of automated decision-making are consistent and appropriate: Human individuals should not become the ordinary “ball” of machines. If machines make automated decisions without being checked by professionals for precision, it can lead to insignificant results as well.

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.”

Artificial Intelligence and the Corporate World Transformation

Worldwide Analytics Cognitive AI and Big Data Predictions

Worldwide, companies collect and own huge amounts of data in the form of documents. Due to a lack of digitization, these can often not be served for business processes – or only with a huge manual effort behind. These documents usually contain important and business-critical information, so the loss or even the time delay in gathering information can have a major impact on the success of a business.

 

However, with the rapid advances in automated text capture cognitive technology, organizations are now able to easily digitize, classify, and automatically read their unstructured business documents for transfer to relevant business processes. With such fully automated solutions, companies can not only save time and money, but also greatly improve the data quality in their systems and massively accelerate response times and important decisions.

 

Especially computer vision has evolved enormously in recent years. The ability to quickly recognize and process text on each device has greatly improved since the time when documents had to be scanned and analysed with OCR technology. This rapid development is also reflected in the numbers in the industry: IDC predicts that the world market for content analytics, discovery and cognitive systems software will reach $ 9.2 billion by 2019 – more than twice as much as in 2014. To make the most of these market changes, IT solution providers need to better serve the rapidly growing needs of machine learning and artificial intelligence (AI). Only then can they meet the customer requirements of tomorrow and remain relevant.

 

Employees in the center of each business

 

There is a groundless fear that artificial intelligence automation solutions could replace skilled employees in companies. Despite or because of solutions based on artificial intelligence, well-trained employees are needed who understand the core values of the company as well as the technological processes. People have qualities that AI solutions depend on, such as empathy, creativity, judgment, and critical thinking. That’s why qualified employees are essential for the success of a company in the future as well.

 

Companies as drivers of digital transformation

 

Businesses first and foremost require systems that support and relieve their professionals of their day-to-day routine work, enabling them to work more productively and creatively. Above all else, modern systems must be capable – on the basis of past experience – of learning behaviour independently and of making suggestions for the future course of action. To do this, companies need professionals who are able to lead these systems to enable automated workflows in the first place.

 
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Robotic Process Automation (RPA) and machine learning drive the automation of routine repetitive tasks. RPA software is a powerful solution for more efficient manual, time-consuming and rule-based office activities. They reduce throughput times and at a lower cost than other automation solutions. In addition, artificial intelligence will make more types of these tasks automatable. The combination of RPA and machine learning will undoubtedly create a large market segment with high demand; namely for the identification of processes and their intelligent implementation.

 

The next five years

 

It is expected that once companies have automated various tasks through the use of artificial intelligence, they will increasingly want to monitor and understand the impact of these processes on their organization. As a result, they will undergo a fundamental change over the next three to five years. This is mainly due to the convergence of RPA and AI in the following areas:

The use and advancement of RPA will entail a wave of machine learning advancements, such as: for task automation or document processing. Even processes that affect basic decision-making benefit greatly from RPA. Use cases traditionally associated with capturing data from documents, on the other hand, will converge with ever new document-based RPA use cases. AI technology is now being used more widely and offers advantages for the identification and automation of processes as well as their analysis.

 

AI will also lead to the automation of basic tasks performed today by qualified staff. It will have a major impact on the composition and size of the workforce of companies, especially in the fintech, health, transport and logistics sectors. Above all, companies from all industries benefit from optimized processes for customer relations. However, authorities can also offer citizens quicker reaction times and improved service through intelligent automation.

And finally, robotics is much more than just R2-D2 or C-3PO. Software robotics will think much faster than most people, penetrate the work environment in companies – in data and document capture, RPA, analytics and for monitoring and reporting – intelligent and situational.

 

Ready for change

 

Businesses need to prepare for the age of AI today to stay successful. This requires a significant shift in the required skills in the company. Above all, it is up to the employees to be open to the new technologies and to see them as an opportunity to gain competitive advantages.

In general, intelligent systems will do more work in the future. For example, in the case of lending, the role of the person in charge will continue to decline because the system will be able to independently make intelligent decisions based on the borrower’s previous financing behaviour. Ultimately, the clerk only has to worry about rule-based exceptions. This will relieve the loan officers of many routine tasks, allowing them to spend more time on customer care. Overall, this significantly increases bank productivity.

A further shift in competence results from the fact that the process requires less human control and expertise. As software becomes increasingly knowledgeable, it becomes less dependent on employees. This means that their duties are smaller, but at the same time more responsible.

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