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About Aisha Javed

Blogger & Online Community Manager at Xorlogics with an excellent familiarity with various Internet resources, expresses a keen desire to learn/excel on Web Marketing for the benefit of the organization.

Google reveals five security issues concerning Artificial intelligence

In a recent article published by Google, they’ve revealed five major security problems related to Artificial Intelligence. From now on, companies will have to follow a guide on their future Al system to control robots before they can interact with humans.

 

The artificial intelligence is designed to mimic the human brain, or at least its logic when it comes to making decisions. Before worrying about whether an artificial intelligence (AI) could become so powerful that can dominate humans, it would be better to make sure that robots (also called our future colleagues and household companions) are trustworthy. That’s what Google has tried to explain to us. Google’s artificial intelligence specialists have worked with researchers from the Universities of Stanford and Berkeley (California, USA) and with the Association OpenAI on concrete security issues that we must work to resolve.

 

In white paper titled “Concrete Problems in AI Safety” this team describes five “practical problems” of accidents in artificial intelligence-based machine could cause if they aren’t designed properly. Al specialists define accidents as “unexpected and harmful behavior that may emerge from poor design of real world machine learning systems”. In short, these are not potential errors of robots we should be feared but those of their designers.

 

To concretely illustrate their point of view, the authors of this study voluntarily took a random example of a “cleaning robot”. However, it’s quite clear that the issues apply to all forms of AI controlling a robot.

 

 

Pour prévenir ce cas de figure, la solution pourrait consister à créer des « contraintes de bon sens » sous la forme de pénalités infligées à l’IA lorsqu’elle cause une perturbation majeure à l’environnement dans lequel le robot évolue.


  • A robot may disrupt the environment :

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The first two risks identified by the researchers from Google and their acolytes are related to a poor coordination and allocation of the main objective. There is first what they call “Avoiding Negative Side Effects”. Specifically, how to avoid environment related problems caused by a robot while it’s accomplishing its mission. For example, the cleaner could well topple or crush what is on his way because he calculated the fastest route to complete its task. To prevent this scenario, the solution may be to create “constraints of common sense” in the form of penalties imposed on the IA when he causes a major disruption to the environment in which the robot moves.


  • The machine can cheat :  

Second risk of Al based machines is to avoiding reward hacking. For IA, the reward is the success of the goal. Avoid the quest reward from turning into a game and the machine trying to get by all means, even skip steps or cheat. In the case of cleaning robot, it would for example to hide the dirt under the rug in order to say “that’s it, I’m done.”
A difficult problem to solve as an Al can be interpreted in many different ways a task and the environment it meets. One of the ideas in the article is to truncate the information so that the program does not have a perfect knowledge of how to get a reward and thus does not seek to go shorter or easier.


  • How to setup the robot go to the basics?

The third risk is called scalable oversight. More the goal is complex, AI will have to validate his progress with his human referent, which would quickly become tiresome and unproductive. How to proceed so the robot can accomplish itself certain stages of its mission to be effective while knowing seek approval in situations that he will know how to interpret? Example: tidy and clean the kitchen, but ask what to do in the saucepan on the stove. It would simplify to the maximum step of the cooking task so that the robot goes to the point without coming to disturb you during your nap every time.


  • How much independence can you give to an AI?

The next identified problem is the safe exploration of Al. How much independence can you give an AI? The whole point of artificial intelligence is that it can make progress by experimenting different approaches to evaluate the results and decide to keep the most relevant scenarios to achieve its objective. Thus, Google says, if our brave robot would be well advised to learn to perfect its handling of the sponge, we wouldn’t want it to clean an electrical outlet! The suggested solution is to train these Al with simulated environment in which their empirical experiments will not create any risk of accident.


  • Does AI will adapt the change?

Fifth and final problem: robustness to distributional shift or how to adapt to change. “How to be ensured that AI recognizes and behaves properly when it is in a very different environment from the one in which it was being driven? It is clear that we wouldn’t want the robot who was trained to wash the floor of a factory with detergent products does not apply the same technique if asked to clean home.

The article ends by saying that these problems are relatively easy to overcome with the technical means currently available but it’s better to be prudent and develop security policies that can remain effective as the autonomous systems will gain in power. Google is also working on an “emergency stop” button for all menacing AI, if eventually one or several of these risks were not fully mastered,

IoT: Biggest Revolution in Retail

If the IoT represents a huge opportunity for almost every facet of the business, this is particularly true for supply chain specialists, operations and analysis. The leaders of e-commerce and traditional commerce see an opportunity of competitive advantage in IoT.

 

Even though I’ve already wrote about IoT in my previous posts, let me give you again a quick definition of it. In 1999, Kevin Ashton (MIT Auto-ID Center) describes the Internet of Things as a network of interconnected objects that generates data without any human intervention. Today, Gartner describes the IoT as “the network of physical objects containing embedded technology to communicate, detect or interact with their internal states or the external environment.”

 

estimates for IoT revenue by region in 2020

For some IoT is only a new name of an old concept, the only thing which has recently changed in this existing concept, is the evolution of Cloud technology. According to a recent survey by Gartner, IoT is one of the fastest-growing technological trend. Estimation says that by 2020, the number of connected objects will be multiplied by 26 to $ 30 billion. Main reason behind IoT success is the development of solutions based in clouds; which allows to actually have access to the data generated by the connected objects.

 

The growth of IoT relies on three levers: reduction in integrated chips costs, technologies supported by a cloud platform and powered by analyzing Big Data and finally the Machine Learning. A case study of IBM named “The smarter supply chain of the future” revels that in near future the entire supply chain will be connected – not just customers, suppliers and IT systems in general, but also parts, products and other smart objects used to monitor the supply chain. Extensive connectivity will enable worldwide networks of supply chains to plan and make decisions together.

 

The main objective of such connective supply chain is to gain better visibility and to reduce the impact of volatility in all stages of the chain and get better returns by being more agile product flow. Several developments are already underway in the IoT and are revolutionizing the retail supply chain at various levels:

 

– At the client side: integration of end consumer in the IoT. The main objective of this step is to collect customer data to create customized product, personalized offers while simplifying the purchasing process. Devices such as health trackers, connected watches etc. continuously collect the data from consumers, prescribers. The collected data represents a great opportunity of positioning product/services. For example, from a person’s browsing history, its culinary tastes and influences on social networks, information on a nutrition bar can be offered to him. Recommendations may also be appropriate if the person enrolled in a sports club or acquired a fitness tracker and so on.

 
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– As for retailers: Beyond the preparation of the assortment by merchants, there are smart shelves and organization of sales outlet. Moreover, we are witnessing a rapidly changing purchasing behavior so with smart shelves a retailer’s system can analyze inventory, capacity and shipment information sent by suppliers. Via the predicted system retailers and suppliers can avoid costly out-of-stocks or missed sales.

To take the example of nutrition bar, time spent in front of a specific category of products (yogurt lightened for example) can be an early indicator to change suggestions or promotions. In addition, the integration of the retail IoT can allow the line to automatically trigger orders. The whole environment can be configured to access a library of planograms, to store inventory data and related warehouses to automatically run restocking. As the elements of this environment are already used independently, we can predict that we are at the dawn of IoT in retail.

 

If the store are at a less advanced stage in the application of IoT, transportation and warehousing are well connected. The integration of RFID shows a first generation data-oriented machine. Integrated tracking systems have long been used in transport and warehouse systems. RFID tagging of pallets has to have better visibility on the status of stocks and the location. The convergence of demand signals and increased visibility on the state of stocks and their location results in scenarios such as the anticipated shipment for which Amazon has filed a patent. Increasing integration of IoT can lead to efficient use of robots for material handling and delivery by drones. These innovations are challenging the effectiveness of existing systems by optimizing the machine learning an effective alternative.

 

Even with all the benefits it promises to offer companies, IoT is still a gamble, with big risks and unsolved problems. For any organization that decided to embark on the IoT, a number of questions remain open whether in technology, integration with file distribution systems to traditional ERP API to communicate with sensors and application languages ​​(Python, ShinyR, et AL.)

 

There are several interfaces that work well in specific areas, but it needs more standardized platforms. Industry experts have launched PaaS (Platform as a Service) to integrate this growing IoT technology. Despite these challenges, the technology seems a surmountable obstacle. Only the legislation on collected data is a real problem so far. Even the customer acceptance remains a challenge. In 2013, Nordstorm had to backtrack on his program which was to track customer movements by the Wi-Fi use on smartphones and via video analysis due to customers demand.

 

Finally, the important thing to remember is that the IoT is a revolutionary technology. A lot of expert retailers, e-commerce players and technology solutions providers will rethink and adapt the model and evolve in processes designed for organizations wishing to adopt the IoT. Retailers that take the lead in this space stand to gain an important advantage in an already competitive environment. Early adopters will be positioned to more quickly deliver IoT-enabled capabilities that can increase revenue, reduce costs and drive a differentiated brand experience. The IoT will be a disruptive force in retail operations.

 

 

Sources:

The Smarter Supply Chain Of The Future

The CEO Perspective: IOT for Retail Top Priorities to build a Successful Strategy

Machine Learning: An Artificial Intelligence approach

I’ve heard a lot of people saying that Machine Learning is nothing else than a synonymous of Artificial Intelligence but that’s not true at all. The reality is that Machine Learning is just one approach to AI (in fact it’s called the statistic approach).

 

Let me first give a definition of Machine Learning. It’s a type of artificial intelligence that gives computers the ability to learn to do stuff via different algorithms. On the other hand Artificial Intelligence is used to develop computer programs that perform tasks that are normally performed by human by giving machines (robots) the ability to seem like they have human intelligence.

 

If you are wondering what it means for a machine to be intelligent, it’s clear that “learning” is the KEY issue. Stuffing a lot of knowledge into a machine is simply not enough to make it intelligent. So before going far in the article, you must know that in the field of Artificial Intelligence, there are 2 main approaches about how to program a machine so it can perform human tasks. We’ve a Statistical Approach (also known as probabilistic) and Deterministic Approach. None of these two approach are superior to the other, they are just used in different cases.

 

The Machine Learning (=Statistic AI) is based on, yes you’ve guessed right, statistics. It’s a process where the AI system gather, organize, analyze and interpret numerical information from data. More and more industries are applying AL to process improvement in the design and manufacture of their products.

 

There’ll be around 5 to 20 billion connected devices within 3 years and so many capture points will be used to make live decisions, to recommend, provide real-time information and detect weak signals or plan of predictive maintenance. Whether it’s at the level of business uses, the sectors of industry and services (health, distribution, automotive, public sector …) or even the use of Business Intelligence, everything is changing! With the Machine Learning and voice recognition technology based on AI, even the Big Data technology might be quickly overtaken by real-time information.

 

In a preview of an upcoming e-book, “AI & Machine Learning”, UMANIS talks about The Data, machinery and men. In the e-book they have elaborated problems and expectations that different companies are facing in the technological era.

 

Based on the responses of 58 participants who responded to the survey “AI & Machine Learning”, here below you’ll find identified trends and indicators.

 

  • 44% of companies believes that AI and Machine Learning have become essential and latest trend in various fields including education, healthcare, the environment and business sector,
  • One company on two is curious about the technological innovations in order to understand the collection of data (via machine)
  • 1/3 of companies are currently on standby on AL & Machine Learning topics,
  • 21% of IT decision makers were informed about Cortana suites (Microsoft) and Watson (IBM)
  • 36% want to go further on this type of technology,
  • 88% are planning to launch an AL project within more than 6 months,
  • 50% of respondents are unaware of the purpose of these technologies in the company.

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TOP 5 issues:

  • Detect abnormalities
  • Using machine learning to optimize the automation
  • Integrating a Learning Machine module into an existing SI
  • Remodeling of the real-time Data architecture to gather big volumes with high computing power
  • Finding a permanent solution of storage and backup of the collected data

 

There’s no doubt that machine learning area is booming. It can be applied to high volumes of data to obtain a more detailed understanding of the implementation process and to improve decision making in manufacturing, retail, healthcare, life sciences, tourism, hospitality, financial services and energy. The machine learning systems can make precise result predictions based on data from training or past experiences. By gathering relevant information for making more accurate decisions, machine learning systems can help manufacturers improve their operations and competitiveness.

Hybrid Cloud Myths Busted

Often presented as a third path between the private cloud and public cloud, companies, consulting firms, suppliers and hosts have started to take their interest hybrid cloud. What is the hybrid cloud? There is no strict and standardized definition of what the hybrid cloud is, each player on the market (consulting firms, hosting companies…) have their own definition.

 

Hybrid-cloud-

 

For Forrester, the hybrid cloud presents itself as “an IT infrastructure model in which at least one external cloud service is integrated with an application, data source, or internalized infrastructure element”. For Gartner, “Hybrid cloud refers to policy-based and coordinated service provisioning, use and management across a mixture of internal and external cloud services”. Basically, it’s a mix use of public and private cloud which are used together to create value.

benefits of Hybrid cloud

Combining the benefits of public clouds such as agility and low cost, with the strengths of private clouds (control, performance and safety) the hybrid cloud brings the best of both technologies. These assets play an essential role in the success of companies, even if few of them still wonders what really a „hybrid cloud“ is. Two options are offered to businesses: get on the board or remain docked. To see more clearly, I propose to demystify the five major myths that usually surround the hybrid cloud.

 

Private + Public = Hybrid:

 

It is not enough to place together these two infrastructures to create a hybrid cloud. In fact, you may not gain any of the respective advantages of both types of clouds but end up multiplying the risk by both of them! First, by moving secure data to the public cloud, security breach can result in brand damage and loss of customers’ trust, and requires significant time and effort to remediate; secondly, migration of apps from a public cloud to a private cloud can lead to unexpected costs. By having a hybrid cloud, you control your workload, your network and storage resources while minimizing risk and increasing productivity.

 

A Hybrid Cloud is Complex and difficult to implement:

 

The use of complete IT solutions allows you to reduce complexity and to choose standard technology – (Microsoft, OpenStack, Vmware) on which your hybrid cloud is standardized, but also the type of public cloud with which the private cloud deployed on site by customer can interact. A complete solution accelerates three essential elements of development: – Integration of end-to-end testing to verify that all components work together; – Use of a converged infrastructure that simplifies the implementation and deployment; – Predefined plans for services, with workflows that must automate provisioning through a self-service portal.

 

The public cloud is more cost-efficient:

Cost savings via Hybrid cloud

A New study by IDG Research Services shows that if we take into account the governance issues, risk and compliance, the hybrid cloud displays in fact a lower total cost of ownership. When data or workloads migrate to the public cloud, it is easy to override local or international regulations on data protection. The local laws and requirements vary from market to market, and some are so complex that companies simply prefer to avoid public clouds. May be that’s why you may want to choose private cloud for sensitive workloads. In Germany for example, the rules on how data is stored and processed are especially strict. The solution lies in how to mix public cloud and private cloud offering each workload the advantages of one or the other, depending on their requirements.

 

On the cloud, the data control escapes you:

 

While extraction or data migration can be difficult via some particularly cloud service providers, a well-orchestrated hybrid cloud environment enable you to keep hand on your work. A well-managed hybrid cloud can provide quickly required public and private resources, provide IT departments a high level of visibility and control, as well as self-service and on demand access for developers and applications users.

 

It is difficult to know which applications are suitable for cloud:                

 

Companies are often hampered by the critical interdependencies of IT infrastructure, ignorance of their IT assets and their relationships to business applications. With a simple spreadsheet and without rigorous methodology, it’s impossible to know precisely if an application is suitable or not for cloud – and even less to know the position in a cloud architecture. Experts who use automated platforms for collecting and analyzing data can provide a complete comprehensive view of the application portfolio and tell whether you need to migrate, consolidate, modernize or simply stop the use of an application.          

 

hybrid Cloud as digital transformation

 

Even if the definition of hybrid cloud is still unclear, its undeniable benefits in terms of agility and cost reduction are now the essential model of tomorrow. Companies that are preparing or starting their digital transformation have every reason to anticipate a future adoption and now choose technologies that will integrate public and private worlds, i.e. converged infrastructure and proven software solutions.

 

Sources:

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Connected and intelligent products: what strategy you should adapt to monetize the Internet of Things?

If you own a small/ medium or a global industry, you’ve certainly entered a level in the Internet of Things (IoT) and have probably invested in infrastructure and strategies. Why? I think it’ll be enough to say that this technology opens up a whole new source of product opportunities, development of existing products or of “business models”.

A recent study done by McKinsey entitled “Unlocking the potential of the Internet of Things” revealed that the business prospects of the IoT are astronomical as it evaluates between $3.9 and $11.1 trillion per year by 2025! In this new economy, it seems that three critical strategies must be addressed.

 

the IoT _ Internet of things

 

    • Product-as-a-Service

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Products of IoT are smart and connected. But to take full advantage, it’s essential to move towards a model based on subscriptions, and more specifically, to realize that for a user (i.e. a potential subscriber), quality of customer experience is greater than the product itself. Thus, the McKinsey study points out that the ability to monitor devices on the same customer site allows the manufacturer to market them as “services” with the new possibility offered by the IoT by introducing a pay per use business model. No doubt that this business model has many other benefits: to guide the development of new products, cross selling or additional sales, etc. As noted by the study: “This approach allows service providers to build relationships more “intimate” with their customers in which competitors will have great difficulty to interfere.”

 

Many questions about this transition from products to services remain. Now, it is indeed a trend in the market, as evidenced by numerous creations of companies whose success and valuation have not denied. Recall, for example, Apple recently began marketing its hardware through outright membership. Many saw it as a simple competitive response, reproducing the model already offered by major market participants. However, as evidenced by a recent market study by Goldman Sachs, the growth prospects are well beyond this perception simply a reactive attitude to competition. In its report “Market Conviction,” Goldman Sachs strongly recommends buying Apple shares and specify their target listing at $160 per share – a gain of some 50% from its current closing price! Why? Monetization services. The merger of its services and materials in a comprehensive subscription system is indeed now the most promising form of development because of its high value.

 

A recent interview with Andy Mattes, CEO of Diebold (leading manufacturer and distributor of ATM) focused on how to maximize the creation of shareholder value in the context of a recent buyout and change the competitive landscape. The discussion was organized around two key issues: first, the ability of Diebold to optimize production and to integrate biometric technologies to its intelligent connected devices and the other, his propensity to create value by marketing production as a service, which’ll have a direct impact on his company’s shares value. How? By increasing margins, providing cost-effective and customer-centric software solutions. Such examples are now commonplace in the extent that the pay per use billing model are more favorable to the customer through monitoring tools and measuring product use.

 

Whether in B2B or B2C field, the Internet of Things has the ability to transform virtually any product in use, and therefore create ideal foundation to build closer and lasting relationships with customers. The operational challenge for companies is therefore to build a transitional infrastructure allowing them to adopt a mode of development centered on the new subscription model.

 

    • Be aware of the behavior of your product/service subscribers

 

Connectivity is not something that is confined to humans. In fact, the number of connected devices has long surpassed the number of humans on the planet. We note today that consumers will own more and more connected devices of all kinds – their cars, their home automation systems and other various connected devices. However, to succeed in this area, it’s certainly not enough to “stick” sensors on a given object to turn it into a “smart product”. It’s a challenge to think in terms of “value creation”; a trend recognized by many companies. The new connected health assistant launched by Visiomed is a good example. With the critical data collected from the patient, this wizard allows a new experience of health through the power of artificial intelligence that has demonstrated 87% consistent diagnoses and complementarity of e-consultancy platform.

 

This notion of the expected end result is also crucial in the world of the Internet of Things for the general public. Nest is not just a thermostat. Instead it shall be considered a “brain Automation”, capable of storing heating habits, to issue warnings of the presence of certain particles in the air. Similarly, vehicles benefiting from Autonet mobile connections have permanent diagnostic system allowing for example to generate alerts to plan the control of vehicle emissions, control the speed of a young driver, sent notifications to chosen contacts when airbags are deployed etc.

 

To meet the expectations of discerning consumers and to better inform them, it is therefore essential to develop services that can “learn” and adapt their behavior to improve themselves fully autonomously.

 

    • Monetizing and securing data

 

The Internet of objects leads to a radical change in the safety concept. The recent “piracy” of Cherokee Jeeps showed that a connected car is likely to be diverted away, even if the driver is at the wheel! Always keep in mind that this type of threat is expected to grow in the same proportion as the explosion of the number of connected devices.     Thus you must NOT underestimate the brain power of a hacker!

 

The safety of the Internet of Things requires indeed implementation of two basic security principles: a robust authentication system and highly secure communication system. A leading solution that supports these two features exists since decades through public key infrastructure (PKI). This system establishes indeed a platform of “trust” with strong authentication/identification services and encryption mechanism based on digital certificates.

 

It’s not certain that the PKI environment is ideal for long term; however, so far it’s the best option for all companies active in the IoT objects wishing to protect their customers – and by the same- their own reputation. There is no doubt that now data security presents same priority like physical security for all companies involved in the IoT.

 

In short: The Customer is ALWAYS the King!       

 

New business models constantly change over the progress of the Internet of things to offer increasingly demanding customers new consumption patterns. In this new context, the winners will be undoubtedly those who will have evolved rather quickly towards centered subscriptions approaches and commercialization of products as a service (PaaS). It’s a challenge that can seem overwhelming to some as far as it’s actually considered as a true organizational reconstruction and completely rethinking its existing management systems. As always, the precursors will be tomorrow’s leaders in their respective markets.

 

The Internet of Things certainly raises many issues but no doubt, those who’ll dive in and take part of this adventure will be the winners of this connected world. This is why you must focus on customer needs for services rather than thinking in terms of creating new generations of products.

 

What your customer needs? What results do they expect? And finally, what security measures you can implement to protect them? If you have answers of these questions you have enough power to be a leader in your sector.

 

Sources:

 

Key strategies of a Big Data project implementation

Focusing on Big Data and analytics to create a real competitive advantage requires a structured and effective approach to collect, clean, correlate and analyze all of the gathered data. To facilitate the task, we’ve gathered three key strategies of this process to ensure the success of your Big Data project implementation.

 

Identify and collect important data for your business: 

 

Above everything in Big Data project, it’s essential to target information to be collected in order to ensure a significant return on investment. Avoid investing in information process which cost can be higher than their potential value.

While benefits are being experienced and are expected from Big Data projects, there are complexities that organizations need to consider. The amount of data organizations have access to is expected to continue to increase. The more data there is in different forms (both structured and unstructured); the more complex processes involved to ensure that analysis of the data is comprehensive, meaningful, and useful.

An innovative approach exists and allows, from a graphical interface, easy to use, integrate data of any kind, transform and manage quality, natively in Hadoop clusters. By overcoming the complexities of programming under Hadoop, IT can respond more quickly and less costly to business demands.

 

Manage data quality: 

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Cleaning data while using or storing it in huge repositories is hardly effective. It is best to complete this step “live”: when transactions are in progress within the systems, for example when a user clicks “OK” on your web site, or an RSS feed notifies you a new message on a blog.

Correlate information from different sources is also essential. For example, you can greatly optimize your marketing actions knowing that “tcook 1968” which visited your website is the same person as “Thomas Cook” that made a credit card payment by phone last month, and visited your physical store by presenting himself as “Thomas Cook”.

Create a data quality Firewall can help you ensure the quality of data, before the gathered data spread to other departments of your company. This firewall will improve business processes, reporting and will optimize the correlation and management analysis of Big Data.

 

Analyze and disseminate information: 

 

Business users are now seeking to free themselves from complex solutions, often imposed by the IT department and poorly adapted to their daily needs. The self-service analysis tools today allow many employees to explore their data, to produce content with high added value, and share it across the organization or beyond it.

This agility, access and manipulation of data, however, must not go against the company’s governance rules. For this reason, it is essential to have a platform with security and a unique repository to handle all of the valuable data chain.

The self-service analysis, through a portal or an application, should not overcome information delivery capabilities to a large number of recipients, regardless of the medium (smartphone, tablet) a schedule or updated in real time.

 

Organizations that do not create Big Data projects in an organized, future-looking way may find that their solutions become more challenging and less appropriate to use over time.

Cybersecurity and Digital Businesses Transformation

In a special report on digital business transformation and cybersecurity, Gartner affirms that as organizations migrate their business to digital, a lack of directly-held infrastructure and services outside of IT’s management can ought to be self-addressed by cybersecurity. “Gartner predicts by 2020, 60% of Digital Businesses will suffer major service failures due to the inability of IT Security Teams to Manage DigitalRisk”

As Digital Transformation initiatives gain pace across the world, the threat of cyber-attack grows in tandem. Further risks stem from the evolving business and regulatory requirements and technology trends that are posing new cybersecurity challenges and endangering the success of digital programs.

 

We all know today’s business depends on technology and digital systems are now lifeblood of an organization. With that being said, Paul Proctor, vice president and distinguished analyst at Gartner, affirms that “Cybersecurity is an important part of digital-business with its new challenges in an open digital-world“.

Organizations leaders must realize that it’s time for a fundamental rethink of how cybersecurity is positioned and understood within their organizations and which innovation they must do to survive digital risks.

 

Let’s have a look on 5 key areas of focus, identified by Gartner, to address successfully cybersecurity in digital business:

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Hard to admit, but the perfect protection is not feasible. Gartner states that 60% of cyber security budgets in the companies will be dedicated to the early detection of threats in 2020. This is less than 30% in 2016. “Organizations need to detect and respond to malicious behavior and incidents, because even the best preventive controls may not prevent all incidents, “says Gartner.

 

  1. Improve the leadership and governance : Improving leadership, getting governance right, making security a board-level priority, measuring leading indicators to catch problems while they are still small, is more important than the development of tools and skills when it comes to cybersecurity and technology risk in digital business. Governance, data infrastructure, decision making, prioritization, budget allocation, awareness, budget allocation, reporting, transparency and accountability are key features to protect and run any business.
  2. Adapt to evolving threats: In today’s connected, information-heavy world, organizations across all sectors are facing not simply escalating risk, but the near-certainty that they will suffer an information security breach. IT department must overcome the challenge to prevent all threats and risk management. Gartner predicts that companies will spent 60% of cyber security budgets to early detect threats in 2020 instead of waiting for the last minute. Only a sharp focus on business structure, culture and risks will let you better safeguard the data essential to your organization’s survival and success and prevent all nightmares.
  3. Alignment of cybersecurity at the speed of digital business: Organizations that adopt digital go at a faster pace than traditional companies. So the same logic must go for security systems, with that being said, the traditional security system will stop working in the new era of digital innovation. Organizations must invest in their security system in order to innovate them to avoid all obstacles. Companies are increasingly reliant on digital data to drive their growth. The “bad guys” have recognized this, have fortified their techniques and are enjoying significant success; while the “good guys” are still trying to fight a modern war! Having a well-defined security program can help management make an informed choice about how to invest in security. All organizations able to successfully establish an ecosystem that protect and grow business will remain competitive.
  4. Explore the new era of cybersecurity: Before it was perhaps easier to protect data because they resided exclusively in the data center. New technologies are now overflowing data outside via the cloud and mobile etc. For example, in 2018, 25% of company’s data will come directly from IOT (=from mobile to the cloud), and by circumventing enterprise security controls. Organizations must now respond to the issues of cybersecurity and risks of technologies and assets that they no longer own or control.
  5. People and Process – Cultural Change: As companies support productivity through the rapid integration of bring your own device (BYOD), cloud computing and other aspects of total mobility, there is a corresponding increase in the risk to which the information located on or accessed via these channels is exposed. With the acceleration of digital business and the power technology gives individuals, it is now critical to address behavior change and engagement from employees to customers. Internal threats to information security run from the inadvertent (simple user error, loss of mobile devices) to the malicious (internal fraud, data theft). Cybersecurity must meet the needs of people through the process of change. People-centric security gives each person in an organization increasing autonomy in how he or she uses information and devices — and what level of security adopted when he or she uses it. The individual then has a certain set of rights in using technology and is linked to the group in the entire enterprise. The individual must also recognize that if things go wrong, it will have an impact on the team, group and business.

 

Sources: Special report – Cybersecurity at the Speed of Digital Business

Artificial intelligence and connected objects, trends of the upcoming years?

Artificial intelligence

Artificial intelligence, great topic of the moment? Yet the term dates back to the 1950s! AI is the term used most commonly for Artificial Intelligence. The concept is to develop computer programs that perform tasks that are normally performed by human. The goal is to give machines (robots) ability to seem like they have human intelligence. I’m pretty sure that at this point we all have seen robots doing the grunt work in factories, intelligence driverless cars, and companies are using AL to improve their product and increase sales.

 

Since 2007, Gartner has been predicting key strategic technology trends for the coming years – not an easy task considering the rapid change in the IT market. According to a classification made by Gartner, Artificial Intelligence, Big Data, Cloud Computing, sensors, connected objects, smart machines and modern 3D printing are the key trends of the years 2016-2020. Automation and artificial intelligence figure prominently in the top 10 technology trends of the future presented by Gartner at its conference Symposium / ITxpo 2015.

 

Multiple devices, mobile to electronic devices via the connected devices and sensors are the first big trend mentioned by the research company. More and more devices are becoming connected and “resulting in smarter homes, smarter cars, smarter everything. IoT is leading to a point where “no object will just be an object—it will all be wirelessly connected to something else.

 

 Gartner expects more interaction between these connected devices via different networks in the coming years and beyond (via 4G + 5G technologies). The user experience and virtual environments comes in second position. According to Gartner, this presents a big opportunity and competitive advantage to IT developers and enterprises. 3D printing are third in this ranking, which isn’t yet a mature market, but getting stronger. Thus, global shipments of 3D printers for businesses should show 64% of an average annual growth rate until 2019.

 

Information on the massive data processing era (Big Data), followed by advanced machine learning and deep learning are also on the top 10.

 

“The explosion of data sources and complexity of data classification makes traditional (manual) analysis almost impossible and unprofitable for organizations. With artificial intelligence, the chances of error are almost zero in addition to that greater precision and accuracy is achieved. Plus according to Gartner, in 2018, 20% of all business content and documents will be produced by machines.

 

Gartner have published results of a survey on the topic Big Data, artificial intelligence and the relationship between the two domains. Without further ado, here are the main results:

  • 69% of respondents says that artificial intelligence will improve with the massive use of data
  • 68% think that Big Data will be used very long term by public authorities and businesses
  • 67% believe that the Big Data presents long-term benefits for the health and well-being
  • Finally, 65% approves that they use avatars that are digital assistants who interact with the users in order to save the need of human resources.

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Artificial intelligence can provide unexpected business intelligence for organizations, enhance knowledge on their customers and improve customer interaction with the company, and in some case even replace entire departments as intelligent, learning machines perform tasks until now strictly reserved for humans. Not surprisingly, demand for solutions made possible by artificial intelligence is increasing in the private sector as well as in the public sector. “In every organization, IT experts should explore how to use intelligent agents and these autonomous connected objects to improve the activity” said David Cearley, vice -President and associated Gartner.

 

Source: Webbmedia Group – 2016 Tech Trends

Cloud computing implementaion and its benefits

This week I’ve decided to talk over cloud computing because I’ve noticed even though it’s an existing technology, talking about it is still Chinese for some people. So to better understand this IT evolution, I’ve decided to provide some explanations, based on several studies, of what’s good to know about #cloudcomputing.

 

Let’s first dive in some history because it’s important to understand where did it came from. Basically Internet exists since 1960s, but it’s only in 1990s that it showed any importance for businesses. The famous WWW “World Wide Web” was born in 1991, and in 1993 a web browser was released that allowed users to view web pages.

Since then the arrival of cloud computing has revolutionized the use of computer services in organizations. Before companies need to manage their IT assets in order to get the more out of it. Now, with cloud computing, companies can use on-demand IT services, without worrying about the infrastructure issues, security and maintenance.

 

The uses of computing in cloud are endless and there are only good reason: it’s the same as with traditional IT, except that the servers / machines that store and process information are not in the same building and are high-performance, reliable servers designed to help you grow and scale any business quickly and easily. In short, below are some uses of cloud computing:

 

  • Use of services / softwares online
  • File storages
  • Disaster recovery
  • Backup
  • Collaboration via shared workspaces and synchronous communication tools

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When I think about it, life before cloud computing was quite pain in the ass because the company’s employees used to access to IT resources (servers, applications, storage spaces …) via the internal corporate network. (Means no home working). There’s no doubt that cloud computing is enhancing out ability to work anytime anywhere.

 

If we compare, traditional enterprise applications are still too complicated and expensive. The number and variety of software and hardware required for their execution is overwhelming. A team of experts is needed to ensure the installation, configuration, testing, implementation, security and update of these software’s.
Cloud computing is the new way to work. The company’s employees are able to access to computer facilities provided by one or more cloud providers via the Internet. These infrastructures are pooled among several companies to be proposed at the lowest cost. Organizations don’t manage any hardware or software. With this kind of infrastructure you only pay for what you need, upgrades are automatic and the evolution of the system is easy. Many businesses are moving to cloud because cloud computing increases efficiency and helps improve cash flow and offers many more benefits.

 

Is implementing cloud services worth it?

“Anywhere, any device, anytime!” For cloud computing vendors the answer is obvious: yes! Companies have every incentive to put their data in clouds. For various reasons: they do not buy the product license and doesn’t need to care of updates of the software and equipment maintenance.

Other arguments: the lack of investment in major infrastructure (most of the time, the computer room one of most important part of the building to which was added an air conditioning system), a very fast service (a server can be activated in minutes) and flexibility (for IaaS and PaaS).

 

Advantages of Cloud Computing:

  • If you’ve ever implement a new applications / service, I’m pretty sure that you are aware of the time it can take. But with a cloud based application, you can cut through this complexity and use the application within no time because you don’t have to worry about maintenance or management.
  • There’s no initial investment if IT tools and no hardware maintenance
  • Costs reduction because users only pay for what they consume. Also strong economy of energy costs and cost of licenses. Also with cloud based applications organizations can increase and decrease the number of users based on your needs.
  • Reduced risk of hardware failure. Data is secured and IT is truly mobile.
  • Not to forget mobility aspect, the user can at any time and from any device connect to applications and workflow. All you need is a terminal with an Internet connection. It’s more productivity and presents a gain of time.
  • It has no size limits, so you have no worries of its capacity.
  • Data is stored securely in clouds, if in case you’ll lose your computer, you’ll never use your important information

 

How the Cloud has convinced organizations?

The number one reason is the facility of connecting their employees using a multitude of different devices. Whether it’s a computer, smartphone, laptop or tablet.

According to the McKinsey Global Survey, more than 80% of respondents indicated they are using or experimenting with cloud technology. And 27% of users affirms that cloud computing has increased the performance of their business by saving time to its employees.

Finally, 93% of users declared that they have improved the efficiency and utilization of their “data center” through the cloud.

 

The impact of cloud on the business model of companies

According to IBM, on average, the cloud helps companies to halve their operating costs. “A unified computing is a 30% cheaper service. The automation of load distribution represents 11% savings in addition to cost governance” says Ian Brooks, in charge of promoting the cloud at HP.

 

With all these information, we can conclude that cloud computing presents the boom and bust in information technology. End of 2020, 840 million people worldwide will be using “Cloud Computing” solutions and companies will be saving at least 210 billion annually.

 

Contact us if you have any question about cloud computing implementation. Our dedicated team can help you save A LOT than you can imagine.

How to succeed your Big Data project?

big data project

Big Data is sweeping the business world, there’s no doubt that data-driven decisions and applications create immense value by utilizing data sources to discover, present and operationalize important business insights.

 

Let’s see the list below how you can implement, manage and succeed your Big Data project.

 

  • The initial objective is important

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If your goal is not clear from the very beginning, you may not only be wasting your time but also money on wrong tools so you can easily penalize your project in terms of time and consumed resources. Keep in mind that your goal isn’t to develop a BIG, FAT database but to be able to collect useful information and analyze it in order to take good decisions for your organization.

Many companies focus on collecting as much data as possible from as many sources as possible. While gathering data is important, the second half of the equation — the “science” part — is too often forgotten. You need to approach your big data efforts from a scientific perspective to gain the most benefit from them. If not, you’re at risk of basing your decisions off of bad models, poor data quality, and erroneous assumptions.

 

  • The concept of uncertainty

One of the most significant developments of Big Data compared to more traditional data is the management of the uncertainty. This does not mean that nothing is planned or the Big Data project is launched without preparation. This means, however – and this is particularly true in marketing – that the Big Data project must take into account this uncertainty from the beginning of its design, and operate on a self-learning model. Again, you must, from the start, create goals that allow you to measure your progress along the way. You’ll also need to take into account what data you need, what existing data you have, and how it all applies to your business objectives.

 

  • Intelligent Big Data

Big Data is not a matter of robots. It’s primarily the result of crossing human intelligence, technology and automation. We all know that collecting information into a data lake is one thing, but finding the business value hidden in heaps of structured and unstructured data is quite another. To have big impact of big data and to deliver phenomenal results to meet expectations, they require new profiles at the intersection of different disciplines: computer science, databases, statistics, artificial intelligence, and last but not least, business knowledge (marketing, finance, logistics, etc.).

 

  • Impact of Big Data on organizations

Big data is becoming an effective basis of competition in pretty much every industry. Not only because of new professions emerged, for which the training is still largely to be create. But also because organizations “craft” of business are strongly rethought. One of the more significant impacts of big data is the organizational change or transformation necessary to support and exploit the big data opportunity. Old roles must be redefined and new roles must be introduced, creating both opportunities and anxiety for individuals and organizations alike.

 

  • Big Data technologies are available

Big Data is not only a buzzword but already available here and now. Many of the technologies used in the Big Data have indeed been invented and popularized by Web giants (Google and Yahoo! are among the pioneers) and are now made available to all who are able to implement them.

 

  • The data is the new oil

The distinction between information system (all processes and organizations between data, their process and archiving) and computer system (hardware and especially software used to process the data) is a classic.

The data is still a largely unknown area by the management of who still consider computer systems like magic formulas capable of transforming the business effortlessly. However, the data is capricious, and it requires a lot of work. It’s growing importance in a society where computerization is presence in all sectors, strength to change the perception of this data by the user. Much remains to be done for this change to be fully realized.

 

  • A Big Data project must be managed differently

Big Data is not only a marketing buzz word to describe existing and new technologies but they have their vocabulary, their professionals, their methods, algorithms, and specific projects approaches. Each Big Data project has its specificities. Beyond the technical approach, it induces specific methodology, an appropriate legal framework and a good measure of social impacts.  Learning will be necessary because Big Data are in constant reconfiguration.

 

What can we learn from Big Data? Certainly first of all we should understand what it is and what its value is because working with data is nothing like it was before. The reality is quite different. Above all, we must get rid of some myths like wishing to analyze everything in Big Data. Similarly, the idea of storing everything in order to “do something someday” is just a waste of time. Companies have never been in a better position to leverage the mountains of data available today to quickly gain insights for real business results.

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