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GDPR with CIAM: THE devil is in the details

The EU General Data Protection Regulation (GDPR) has been in effect since May 25, 2018, It fundamentally changes the requirements for the processing of personal data and gives EU citizens significantly more control over their personal data – no matter where and how it is processed. Organizations around the world must respect certain guidelines on how to deal with the personal data of EU citizens. Anyone who does not fulfils their obligations risks a fine of up to four percent of the annual turnover achieved worldwide or 20 million euros. With that being said, still many the technical requirements of the EU General Data Protection Regulation seem difficult for companies to implement. An overview of what they are and how Customer Identity & Access Management (Customer-IAM or CIAM) paves the way to compliance.

GDPR with CIAM: THE devil is in the details

The articles of the Basic Regulation essentially define how data is collected, stored, accessed, modified, transported, secured and deleted. So, in the age of digital change, companies must find the right balance between compliance with the legal requirements on the one hand and effective customer care on the other.

 

Not only they must give data subjects extended opportunities to have a say in what happens to their personal data. But also, the person responsible requires documented consent from the data subject for the collection, storage and use of the data. Thus, all personal data must be secured using appropriate technical and organisational measures, depending on the probability of occurrence and the severity of the risk. Here below are few main issues that companies are faced with on the road to compliance:

 

  • Insufficient consent of the data subjects: The previously required basic level of consent to data use, including the opt-out procedure, is no longer sufficient under the provisions of the GDPR.
  • Data silos: Personal data is often stored across multiple systems – for example for analysis, order management or CRM. This complicates compliance with GDPR requirements such as data access and portability.
  • Lack of data governance: Data access processes must be enforced app by app via centralized data access policies. These policies are designed to give equal weight to consent, privacy preferences, and business needs.
  • Poor application security: Customer personal data that is fragmented and unsecured at the data layer is vulnerable to data breaches.
  • Limited self-service access: Customers must be able to manage their profiles and preferences themselves – across all channels and devices.

 

A robust Customer Identity & Access Management (Customer IAM or CIAM) solution are able to solve many of these seemingly insurmountable problems in no time.bThese solutions are able to synchronizes and consolidates data silos with tools such as real-time or scheduled bi-directional synchronization, the ability to map data schemas, support for multiple connection methods/protocols, and built-in redundancy, failover and load balancing.

 

CIAM solutions also facilitates the collection of consent across multiple channels and allows searching for specific attributes. Along with enabling mandatory enforcement of consent collection based on geographic, business, industry or other policies, they also offer the customer the opportunity to revoke their consent at any time. CIAM solutions give customers the ability to view, edit, and assert their preferences across channels and devices through pre-built user interfaces and APIs.

Most of the time these solutions include numerous centralized data-level security features, including data encryption in every state (at rest, in motion, and in use), access to recording restrictions, tamper-proof logging, active and passive alerts, integration with third-party monitoring tools, and more more.

 

In this way, a suitable CIAM solution helps to put many technical requirements of the GDPR into practice. And it even goes beyond the requirements of the basic regulation to create safe, convenient and personalized customer experiences – the basis for trust and loyalty.

 

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Règles pour les entreprises et les organisations

 

Business Automation & Multi-Cloud Management: Micro and Maxi trends for 2021 and Beyond

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The year 2021 is all about transformation processes, primarily resulted by the exceptional situation we’ve witnessed in 2020. As 2020 caused a major shift in how business and IT teams operate, the development around COVID-19 was and still is a great challenge for all organizations. In addition to classic customer service, IT service, in particular, is confronted with more tasks and service requests. So that the workforce can work productively and quickly, the IT service needs intelligent tools for automation. Many changes have been on the agendas of IT departments for several years and vary from micro changes that affect the big picture to maxi changes that will affect future generations of employees.

 

In this continuously changing environment, organizations are exploring new ways to operate and drive growth. Each year, Gartner, Inc. releases a series of studies mentioning trends/predictions that will impact the business environment, IT, and technology in the coming years. Here below, we’ve gathered the most relevant trends to the IT automation market to help IT, professionals.

 

“Hyper automation is irreversible and inevitable. Everything that can and should be automated will be automated.” Brian Burke, Research Vice President, Gartner

 

  • By year-end 2025, over half of the world’s population will be subject to at least one internet of behaviors (IoB) program (private, commercial or governmental).
  • By 2025, 50% of enterprises will have devised artificial intelligence (AI) orchestration platforms to operationalize AI, up from fewer than 10% in 2020.
  • By 2025, 40% of physical experience-based businesses will improve financial results and outperform competitors by extending into paid virtual experiences.
  • By 2025, half of the large organizations will implement privacy-enhancing computation for processing data in untrusted environments and multiparty data analytics use cases. 
  • By 2024, organizations with IT teams that understand the needs of customers will outperform other organizations’ customer experience metrics by 20%.
  • 2023, 40% of all enterprise workloads will be deployed in cloud infrastructure and platform services, up from 20% in 2020.
  • By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
  • By 2025, most cloud service platforms will provide at least some distributed cloud services that execute at the point of need.
  • By 2025, customers will be the first humans to touch more than 20% of all products and produce.
  • By 2024, organizations will lower operational costs by 30% by combining hyper-automation technologies with redesigned operational processes.
  • By 2024, 80% of hyper-automation offerings will have limited industry-specific depth mandating additional investment for IP, curated data, architecture, integration, and development.
  • By 2024, more than 70% of the large global enterprises will have over 70 concurrent hyper-automation initiatives mandating governance or facing significant instability.

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Demand Forecast Powered by Machine Learning

Demand Forecast Powered by Machine Learning

 

The business landscape is rapidly becoming more global. Largely due to improvements in communicationsand increasing globalization which are dramatically impacting the way business is managed. No area of a business is more affected by the trend of a global business environment than the supply chain. Supply chain logistic, known as the backbone of global trade, is a network of many partners involved such as customer, dealers, manufactures,transportation, external warehouse,suppliersand inventory. Sometimes a delivery comes along with delay, sometimes there is something wrong in a package, delivered article is different to ordered article and sometimes a shipment is lost. This is annoying for all sides. It costs time, energy, money and sometimes even the customer. Challenges for decision-makers in supply chain management are growing due to the widely networked supply chains and the constant change in the environment of companies.

 

In fact, many companies are facing hurdles in their existing business processes and technologies that aren’t flexible enough to deal with “large and global” business environments. Therefore, areas such as manufacturing, distribution, sourcing of materials, invoicing and returns are impacted by the increased integration of a global customer and supplier base.

Supply Chain specialist must deals with long-term planning in terms of location, make-or-buy decisions, supply relationships, capacity dimensioning, logistics strategy and general tasks along with cost optimization in structuring of the logistics and production processes. Hence, in order to initiate the demand forecasting, it’s highly recommended to understand the workflow of machine learning modeling. This offers a data-driven roadmap on how to optimize the development process.

 

Operational inefficencies in SCM often lead to potential revenue losses, increasing costs, and poor customer service, ultimately diminishing profits. With the help of AI, machine learning techniques are able to forecast the right number of products or services to be purchased during a defined time period. In this case, a software system can learn from data for improved analysis. Only good data produces good results!

Data interpretation is a vital part of supply chain management and demand forecast as it’s used to improve your ability to estimate future sales, reduce shortages and overstock. Once the data is interpreted correctly, both in national and international trade results in having the right products at the right time in the right number at the right place.
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So, for demand forecasts that are generated by self-learning algorithms require data that is closely related to sales. However, in order for machine learning to achieve a high quality of forecasting, a certain amount of quality data is required. The result of ML process depends solely on the quality and quantity of data provided.

To ensure that the data is up to date, the input data should not be older than 5 years. Data selection can be a special hurdle before using machine learning methods, because it can be very time-consuming. In connection with the data quality, it must be ensured that there are only a few missing values of the data records in the input data, otherwise the machine learning model may generate incorrect results. Data preparation is necessary for successful implementation and definitely pays off later. If the data record does not have sufficient data quality, it must be prepared through an intensive process and carry sufficient information for qualitative algorithms and for a good forecasting performance.

 

The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Therefore, ML fed with qualitative data can generate precise forecasts and thus ensure a secure basis for planning. The resulting benefits, such as reducing inventory levels and simultaneously optimizing the ability to deliver, also improve the operating result. ML uses learning algorithms to recognize patterns and regularities in data and is able to adapt automatically and independently through feedback and thus react to changes.

 

Compared to traditional demand forecasting methods, machine learning not only accelerates data processing speed but provides a more accurate forecast, automates forecast updates based on the recent data in order to create a robust system.

HyperScale and Data Management

A hyper-scale data center is mega-sized data centres that have a huge number of computers, network hardware, cooling systems, and are able to support thousands of physical servers and millions of virtual machines. As the technology is growing at exponential rates, big providers such as, Amazon Web Services (AWS), Microsoft Azure, Google, IBM Cloud, Oracle, SAP etc are redefining hyper-scale data centres for data storage and optimising speed to deliver the best software experience possible.

 

In this evolving IT landscape, for customers as well as companies, the question arises of which strategy they want to use for their data storage. They can choose from the following two options, either they can outsource and move their data to an external data center facility, either they can choose to move their traditional ‘physical’ data center to the clouds, means instead of investing in their physical hardware’s, they may opt to rent server in clouds. With this process of outsourcing, companies can reduce payroll costs and ensure that they remain up to date with the latest technological advances.

 

This need of outsource has created the requirement of hyperscale data center, which can operate, analyse and model the massive amounts of data flowing through their systems and offer insights into user behaviour that can be used to generate further income streams. As traditional architectures are not suited to adapt to new cloud centric infrastructure and operations, outsourcing Hyperscale represents a lot of advantages. Companies can benefit from a flexible and customizable IT infrastructure at exactly the level of traceability and cost, adapted to their capacity and need for specific workloads. The principle is simple: The low Total Cost of Ownership (TCO) comes from the fact that the data centers are usually equipped with standard and thus inexpensive standard components and second, that due to virtual infrastructures for larger data volume no more space, air conditioning or electricity is needed.

 

However, companies must not only work with one a single vendor. Using cloud services from more than one hyperscaler avoids dependency on a single vendor. Connecting with different Hyperscaler companies can also choose, depending on the requirements, the individually matching cloud service. This keeps them flexible in order to respond quickly and cost-effectively to new business challenges. This approach requires well-thought-out architectures, as in addition to disk space and computing capacity, data traffic also costs money in the cloud. Unnecessary and duplicate data exchange with Second Source can also increase costs.

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But where exactly are the differences in the hyperscalers? Well, the pitfalls lie in the different cloudstack. The most important distinguishing features can be divided into three categories: product diversity, performance classes and workload-specific target groups.

As no company is the same, sometimes performance, sometimes security, sometimes compliance, then again the availability or the costs or criteria such as scalability, connectivity and other workloads have priority. This question must respond to each companie’s need individually in the selection of hyperscaler.

 

Both in strategy and in purchasing, new thinking is required. Because the potential of the Hyperscaler can only be raised if the companies say goodbye to their single sourcing strategy. Multi-cloud sourcing strategies imply that businesses can move from one provider to another at any time and even distribute the same workloads among multiple resources.

 

Public cloud services are now demanded not only by large but also by medium-sized companies as a managed service. Hybrid and multi-cloud models, which constantly analyze new services and integrate them into managed service offerings, dominate.

For the path of the public cloud transformation, the supporting service providers have to offer a wide range of services and technical implementation. They act as partners of the large public cloud providers and have to know their advantages and disadvantages and advise their customers accordingly. Service Providers need to know all the offerings of the cloud providers in detail and to harmonize them with the requirements and business processes of the user companies.

 

Hyperscale computing is highly decisive choice for organizations dealing with large data volume, so the usage of the new computing system will be seen more in the area where companies demand big analytical needs.Since hyperscale computing is attracting more users, legacy methods of data management are no longer enough to meet the data management needs due to explosive data growth. HyperScale Technology provides a modern data management that is scalable, highly resilient, and simple. It allows organizations to manage data seamlessly on-premises and in the cloud. With Hyperscale Technology solutions companies can removes the burden of day-to-day operations with a simplified installation, automated and self-service operations, and update process.

GDPR 1 Year Anniversary – What have you learned so far?

GDPR 1 Year Anniversary – What have you learned so far?

On 25 May 2019, we’ve celebrated the first anniversary of General Data Protection Regulation, aka, GDPR. Preparing for the GDPR was a superhuman effort for many. Now here we are, almost a year later, and the buzz of GDPR is faded, but not completely gone. And that’s to be expected — establishing and reinforcing a strong culture of compliance is not a “one-and-done” effort, but an ongoing and organization-wide push. What have companies learned in this past year? Are they taking GDPR in consideration and adapting new strategies in order to fulfill its requirements?

 

GDPR compliance requires a constant attention, and it’s full on challenges. Even after one year, a few companies still do not always see the meaning of the GDPR, but only a bigger workload. For few companies, the biggest challenge so far is resourcing, both financial and personal. GDPR job must be done with the skill set of right people, and as the demand of professionals is increasing, it’s hard to find the right profile to get the job done. In 2017 an IAPP study had estimatedthe need of 75000 DPOs worldwide. The IAPP new GDPR One Year Anniversary – Infographicindicates that 375000 organizations are documented to have DPOs and 500000 organizations are estimates to have registered DPOs across Europe.

A lot of organizations already have most of the basic structure for compliance with GDPR in place and they are able to respond to data subject access requests, the extensive mapping and tracking of data that is processed. The IAPP study showed that 200000+ cases have been received by DPAs during past year and 94000 individual complaints such as access requests, right to erase their data, unfair processing of data, unwanted marketing, disclosure and employee privacy.

 

The aim of the GDPR is the protection of personal data. Not only names, but also data such as telephone numbers, license plates or IP addresses are considered to be personal. For companies that have more than ten employees, the GDPR obliges them to have an internal or external privacy advocates. He is the contact person of employees, management and other affected persons. Many companies have already implemented this requirement. But even if the monitoring of compliance with data protection laws and the EU GDPR is one of the duties of a data protection officer, employees have to pay attention in everyday life even to the consideration.

 
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For this purpose, it is not enough to provide them with GDPR-compliant tools only. Instead, it is important to clarify the importance of data protection, even if much seems obvious at first glance. For instance, it is advisable to avoid making loud phone calls about sensitive company data in public as much as possible and to use privacy filters when using service laptops while on the move. Likewise, employees should not use public WLAN networks, as they are in most cases not safe but instead virtual private networks, so-called VPNs. They must also have the sense to detect an abnormal email with a lot of spam links.

 

The IAPP report also shows that the European data protection agencies have issued fines resulted in €56,000,000+ for GDPR breaches since it was enforced last May, from more than 94000 individual complaints, 64000+ data breach notifications and 280+ cross-border cases, and it’s just a warm up for the data protection authorities.

 

Often companies still lack the awareness that the GDPR not only concerns the handling of customer data, but also employee and supplier data. For business to go forward positively, it’s highly important to stay tunewith regulator guidance and enforcement decisions from their country’s respective DPA in order to know when internal processes may need and update. When it comes to GDPR, organizations should monitor the European Data Protection Board website, which also has started reposting information from national DPAs, as well as ongoing guidance. The penalties for non-compliance and the potential reputational risk are severe.

 

Source : GDPR One Year Anniversary – Infographic

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