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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.

IT Challenge : Re-invent business models … or disappear!

Technologies have significantly lowered market entry barriers and the development of free-of-charge models has favored the appearance of business models that have destabilized the positions of historical actors in most sectors.

As a matter of fact, In this digital age, existing approaches to develop, elaborate and describe business models are no longer appropriate for new connected business models. Technologies and services are becoming obsolete faster than in the past, consumers are pleased with innovation and customer experience, the need for agility weighs on production capacities and information systems, therefore cooperation becomes a must.

 

In this changing context, the risk of disappearing, for a company, has never been more present: this is what motivates collaboration and alliances between often competing actors. Cooperation can be seen in a positive competitive way front of the decline phase that threatens businesses of all sizes. This context justifies a reflection on the identification of large organization’s strengths and weaknesses in comparison to their competitors. By 2020, the ability to renew its business model will be critical to the growth and profitability of large firms.

 

Data Capitalizing and Customer Experience

 

Data is the black gold of present and future. But according to Gartner, 85% of the largest organizations in the 500 fortune ranking won’t get a real competitive advantage with the Big Data by 2020. The majority of them will still be in the experimental stage while those who have capitalized on the Big Data will gain a competitive advantage of 25% or more compared to their competitors. Therefore, the development of new products and services, facilitated by the intelligent use of data, creates real changes and new business opportunities.

In a context where the risk of disintermediation is major, control of customer relations, mass customization, co-design with consumers will be fundamental to the success of companies in 2020.

 

Challenges: Differentiating and Innovating

 

  • Understand: it’s essential to keep track of business model and strategies of its competitors of the digital world, the latter being both potential threats and powerful levers of development.
  • Transform: Large groups, especially if they are economically powerful, often find it difficult to transform their organization and integrate innovation, because of their complexity.
  • Listen: anticipating the needs of consumers and focusing on the customer experience means constantly evolving business models in order to develop business agility.
  • Collaborate: creating strategic partnerships with the company’s ecosystem, especially suppliers, accelerates innovation processes and reduces time to market.
  • Adjust: the digital transformation must take into account the context and the business challenges of the company.
  • Innovate: know-how in terms of software development can become more and more strategic for the company.

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#BusinessIntelligence and Decision-Making Strategic Project

Paying attention to how your organization handles decisions rights is the first step to making the effective, timely decisions needed to perform business strategies and realize goals. But decision-making is not a stress-free situation. The uncertainty, complex and chaotic environment, limits the perception of clear signals. On the other hand, one cannot just predict all the possibilities due to the short time limit for certain decisions. In some cases, decision makers must act quickly, to take advantage of all positive breaks without wasting any time. In some scenarios decision-making can be considered as a risk-taking situation. That’s why when implementing the decision-making IT project, technological concerns tend to obscure user’s expectations of decision-making. Thus to provide an effective decision-making IT solution, experts must think about deployment of the strategy.

 

decision-making to Business Intelligence

The formulation of “Business Intelligence” naturally came from the expression of “decision support system” which, although a little dated, was nevertheless much more expressive. A decision-making IT project is equal to build a technological IT architecture to facilitate and support decision makers in any organization. It is clear and concise. Yet, sometimes in practice, the last part of the formulation, the term “decisional”, has all too often been shortened. The “decision-making computer project” is then a “computer project” where only the implementation of technology matters. The designers seem to adopt the hypothesis that it’s enough to work according the rules of qualified technology as “decision-making tools”, without really caring about the purpose of help to the decision.

There’s no doubt that connecting heterogeneous systems, collect and integration of data in multiple formats is a constant headache. Data collection phase is not a fun part. Plus when it’s badly committed, with a minimal budget quickly set, the complexity of this essential phase can soon send the entire project to the trap. In addition to previously said, the exponential expansion of IOT, multiplies the points of access to the system which does not, in any way, solve the problem. Having said that, companies must think about a strong strategy before working on any kind of decision-making IT projects.

 

A strategic project:

 

How to define the assistance to decision-making procedure in company if it’s not in close relation with the deployment of the strategy? Decision-makers do not make decisions all the way, depending on their mood of the moment. They follow a precise direction, each in its own way according to its context, but the direction is common shared based on figures and facts. It’s therefore from the formulation of the strategy that one must start to define the broad lines of an intelligent decision-making system.

 

The dashboard – at the heart of the process:

 

Now a majority of company players are required to make ad-hoc decisions in order to accomplish their daily tasks. To ensure that all the necessary assistance is available, the designers need to focus on the needs of decision-makers:

 

  • What types of decisions are needed to achieve the strategic objectives?
  • How do they measure the risks?
  • What information should be available as soon as possible so that they can make advantageous decisions?
  • Finally, more generally, what are the needs of each decision-maker for presentation and analysis tools?

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This is finally the purpose of the decision-making IT project in full light. Therefore it shouldn’t be a catalog of tools, stacks of report, but a personalized dashboard system in its own way. The design of the dashboard of each decision-maker must be at the heart of the decision-making project.

 

From decision-making to Business Intelligence: 

 

We can now safely adopt the term “Business Intelligence”, whose role is to ensure the fair flow of consistent and consolidated information flows between decision-making nodes. Business Intelligence is still only in the beginnings of its dawn. The predicted evolution towards the generalization of the storage and processing of very large masses of data risks to shift once more the focus on the technical aspects at the expense of the decision-making process. The designer must not lose sight of the demands of decision-making process, in a complex and uncertain universe, in order to better value the role and importance of tools.

 

#BusinessIntelligence: for a better Control of Data

Business intelligence (BI) is a subject in full evolution, addressing the general management as well as the trades. BI helps decision-makers to get an overview of the different activities of the company and its environment. This cross-sectional view requires knowledge of the various business lines and involves certain organizational and managerial specificities. From the exploitation of business data to IT governance, the Business Intelligence point of view, and its decision-making tools such as reporting, dashboard and predictive analysis are so important for the success of a business.

The organization of BI in the company is highly dependent on the organization of the company itself. However, BI can have a structuring impact for the company, notably through the formalization of data repositories and the setting up of a competence center.

What is the purpose of Business Intelligence?

 

Business Intelligence (BI) encompasses IT solutions that provide decision support to professionals with end-to-end reports and dashboards to track analytical and forward-looking business activities of the company.

 

This notion was appeared in the end of the 1970s with the first infocentres. In the 1980s, the arrival of relational databases and the client / server made it possible to isolate production computing from decision-making devices. At the same time, different actors embarked as specialist of “business” layers analysist, in order to mask the complexity of the data structures. Beginning in the 1990s and 2000s, BI platforms were built around a data warehouse to integrate and organize information from enterprise applications (extraction, transfer and Consolidation). The only objective was to respond optimally to queries from reporting tools and dashboards of indicators and made it available to operational managers.

 

How does decision-making tools work today?

 

Over the past few years, BI platforms have benefited from NoSQL databases, enabling them to directly process unstructured data. Today Business Intelligence applications benefit from a more powerful hardware architecture, with the emergence of 64-bit, multi-core, and in-memory (RAM) architectures. In this way, they can execute more complex processes, such as data mining and multidimensional analyzes, which consist in modeling data according to several axes (turnover / geographical area, customer, product category, etc.). ..).

 

Which fields are covered by the BI?

 

Traditionally focused on accounting issues (consolidation and budget planning), BI has gradually expanded to cover all major areas of the company, from customer relationship management to supply chain management and human resources.

 

  • Finance, with financial and budgetary reports, for example;
  • Sale, with analysis of sales outlets, analysis of the profitability and impact of promotions for example;
  • Marketing, with customer segmentation, behavioral analysis for example;
  • Logistics, with optimization of inventory management, tracking of deliveries for example;
  • Human resources, with the optimization of the allocation of resources for example;

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Specialized publishers have developed ready-to-use indicator libraries to monitor these different activities. Finally, with the emergence of new web technologies (including HTML5 and the JavaScript and AJAX graphical interfaces) we’ve seen the appearance of new players proposing a BI approach in the cloud or SaaS mode.   

 

Today, information is omnipresent; the difficulty is not to collect it, but to make it available in the right form, at the right time and to the right person, who will know how to exploit it and drive added value. So the BI market offers fairly comprehensive and complete solutions for the data reporting and consolidation aspects of both proprietary and open source domains. Possible developments in the short to medium term would include proactive and simulation analysis tools as well as the interactivity and user-friendliness of data access and the combination of structured and unstructured data from Internal and external data.

How #DeepLearning is revolutionizing #ArtificialIntelligence

This learning technology, based on artificial neural networks, have completely turned upside down the field of artificial intelligence in less than five years. “It’s such a rapid revolution that we have gone from a somewhat obscure system to a system used by millions of people in just two years” confirms Yann Lecun, one of deep learning and artificial intelligence’s creator.

All major tech companies, such as Google, IBM, Microsoft, Facebook, Amazon, Adobe, Yandex and even Baidu, are using. This system of learning and classification, based on digital “artificial neural networks”, is used concurrently by Siri, Cortana and Google Now to understand the voice, to be able to learn to recognize faces.

 

What is “Deep Learning”?

 

In concrete terms, deep learning is a learning process of applying deep neural network technologies enabling a program to solve problems, for example, to recognize the content of an image or to understand spoken language – complex challenges on which the artificial intelligence community has profoundly worked on.

 

To understand deep learning, we must return to supervised learning, a common technique in AI, allowing the machines to learn. Basically, for a program to learn to recognize a car, for example, it is “fed” with tens of thousands of car images, labeled etc. A “training”, which may require hours or even days of work. Once trained, the program can recognize cars on new images. In addition to its implementation in the field of voice recognition with Siri, Cortana and Google Now, deep learning is primarily used to recognize the content of images. Google Maps uses it to decrypt text present in landscapes, such as street numbers. Facebook uses it to detect images that violate its terms of use, and to recognize and tag users in published photos – a feature not available in Europe. Researchers use it to classify galaxies.

 

Deep learning also uses supervised learning, but the internal architecture of the machine is different: it is a “network of neurons”, a virtual machine composed of thousands of units (Neurons) that perform simple small calculations. The particularity is that the results of the first layer of neurons will serve as input to the calculation of others. This functioning by “layers” is what makes this type of learning “profound”.

 

One of the deepest and most spectacular achievements of deep learning took place in 2012, when Google Brain, the deep learning project of the American firm, was able to “discover” the cat concept by itself. This time, learning was not supervised: in fact, the machine analyzed, for three days, ten million screen shots from YouTube, chosen randomly and, above all, unlabeled. And at the end of this training, the program had learned to detect heads of cats and human bodies – frequent forms in the analyzed images. “What is remarkable is that the system has discovered the concept of cat itself. Nobody ever told him it was a cat. This marked a turning point in machine learning, “said Andrew Ng, founder of the Google Brain project, in the Forbes magazine columns.

 

Why are we talking so much today?

 

The basic ideas of deep learning go back to the late 80s, with the birth of the first networks of neurons. Yet this method only comes to know its hour of glory since past few years. Why? For if the theory were already in place, the practice appeared only very recently. The power of today’s computers, combined with the mass of data now accessible, has multiplied the effectiveness of deep learning.

 

“By taking software that had written in the 1980s and running them on a modern computer, results are more interesting” says Andrew Ng. Forbes.

 

This field of technology is so advanced that experts now are capable of building more complex neural networks, and the development of unsupervised learning which gives a new dimension to deep learning. Experts confirms that the more they increase the number of layers, the more the networks of neurons learn complicated and abstract things that correspond more to the way of a human reasoning. For Yann Ollivier, deep learning will, in a timeframe of 5 to 10 years, become widespread in all decision-making electronics, as in cars or aircraft. He also thinks of the aid to diagnosis in medicine will be more powerful via some special networks of neurons. The robots will also soon, according to him, endowed with this artificial intelligence. “A robot could learn to do housework on its own, and that would be much better than robot vacuums, which are not so extraordinaire for him!

 

At Facebook, Yann LeCun wants to use deep learning “more systematically for the representation of information”, in short, to develop an AI capable of understanding the content of texts, photos and videos published by the surfers. He also dreams of being able to create a personal digital assistant with whom it would be possible to dialogue by voice.

 

The future of deep learning seems very bright, but Yann LeCun remains suspicious: “We are in a very enthusiastic phase, it is very exciting. But there are also many nonsense told, there are exaggerations. We hear that we will create intelligent machines in five years, that Terminator will eliminate the human race in ten years … There are also great hopes that some put in these methods, which may not be concretized”.

 

In recent months, several personalities, including Microsoft founder Bill Gates, British astrophysicist Stephen Hawking and Tesla CEO Elon Musk, expressed their concerns about the progress of artificial intelligence, potentially harmful. Yann LeCun is pragmatic, and recalls that the field of AI has often suffered from disproportionate expectations of it. He hopes that, this time, discipline will not be the victim of this “inflation of promises”.

 

Sources:

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Secure #IOT: and if #BigData was the key?

By 2020, the planet will have more than 30 billion connected objects according to IDC. The security of these objects is a major discussion topic. Ensuring the security, reliability, resilience, and stability of these devices and services should be a critical concern not only for manufacturer, companies using them but also for the end user. Security solutions abound on the market, but has anyone just thought of Big Data?

 

The Internet of objects is third industrial technological revolution, enabling companies to work smarter, faster and of course in a more profitable way. IOT represents endless and challenging opportunities, and above all, it shows that a full-fledged ecosystem is being created. This is very different from big data, because most companies consider big data to be static; the data is generated in logs that have utility only where they are, because there is no connectivity. With the Internet of objects, the data is mobile.

 

A good example of the potential created by the Internet of objects is the work done by Deloitte and a medical device manufacturer in order to optimize the management of chronic diseases in patients with implanted devices. They have established remote data transmissions from patient pacemakers. Pacemakers communicate via Bluetooth at low frequency and contact the healthcare provider using a handset. With this connected object, the physician can obtain real time information to better determine the treatment protocols.

 

However, there’s one critical issue that still need to be addressed to facilitate the Internet of objects adoption by every organization, and this issue concerns the IOT security as well as all the elements that makes it up. With billions of objects and terminals connected to the Internet, including cars, homes, toasters, webcams, parking meters, portable objects, factories, oil platforms, energy networks and Heavy equipment, the Internet of objects abruptly multiplies the surface of threats, increasing the number of vulnerabilities and creating millions of opportunities for threats and attacks.

IOT Risk Management

The recent DDoS attack illustrates the alarming dangers and risks associated with unsecured devices and components of Internet of objects. This should certainly have the effect of raising awareness for businesses and individuals, and should lead them to take actions for the security of Internet of objects. According to a recent study released by computer security firm ESET and the NCSA (cyber security alliance), about 40% of respondents in the US have no confidence in the security and privacy of connected objects. So these security issues will remain at the forefront as long as manufacturers will not seriously removed security vulnerabilities, and companies won’t increase their internal cybersecurity measures to effectively detect and counter future security threats. Although it is necessary to take into account many parameters to secure the Internet of the objects (security of the terminals, network security, etc.), one of the key pieces of the puzzle is to determine how to take advantage of massive quantities of data continuously generated by the devices.

 

A data-driven approach to prevent IOT cyber attacks

 

Big data plays a crucial role in protecting a company and its assets against cyber threats. The future of the fight against IOT cybercrime will be based on the use of data for cybersecurity. According to a recent Forrester report, “Internet object security means monitoring at least 10 times, if not more than 100 times more physical devices, connections, authentications and data transfer events as today. Having a better ability to collect event data and intelligently analyze them through huge data sets will be crucial to the security of connected systems. “

Given all this, companies need to think about these two following things to prepare for this new era …

 

The first is that companies need to rethink the security perimeter. Recent attacks that have targeted connected objects have made clear that the “security perimeter” is now more conceptual than physical. The constantly evolving nature of our new hyperconnected world also leads to the constant evolution of threats. As the technical community continues to connect the world and contribute to innovations that improve home security, improve medical care and transform transport, it is clear that the hackers will seek to exploit these same innovations for harmful purposes. We need to rethink the perimeter of security as the corporate edge continues to expand beyond the traditional borders to which we were used to.

 

Then, the detection of the threats must adapt to the magnitude of the connected objects. The world continues to hyper-connect, the number of security events that any enterprise must store, consult and analyze are also increasing significantly. Having a cybersecurity platform capable of supporting billions of events is essential to ensure total supervision of all devices connecting to and accessing a company’s network. The use of technologies such as #MachineLearning for the detection of anomalies will allow companies to continue to detect suspicious behavior on the workstations without any human intervention. The ML scalability coupled with the Internet of the objects will be the key to the anticipated detection of the threats specific to IOT.

 

As we know, by 2020, the planet will have more than 30 billion connected objects. To get the most out of these revolutionary innovations and prevent them from becoming a nightmare in terms of IT security, organizations will have to learn how to manage, process, store, analyze and redistribute a vertiginous volume of data in real time and all of this by respecting security norms.. We increasingly depend on these devices for essential services, and their behavior may have global reach and impact.

 

Sources:

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#MachineLearning: How #PredictiveAnalytics reinvents Customer’s Satisfaction

Billions and trillions of data is collected on customer behavior from the huge platform called internet. To these are added valuable information gathered by every organization from different sectors. In this mine of information, Machine learning, pursues an ultimate goal: to better understand customers in order to offer them the best experience possible by offering them the product or service most likely to their need. It’s analytical power and the advance in artificial intelligence allows companies to take advantage of the wealth of data they collect.

At this point we all know that #bigdata is worth nothing, nada, without proper decryption. This is where machine learning or “automatic learning” comes into action. With its power of analysis, this field of artificial intelligence extracts the valuable information from the mass data. In other words: it enables to turn the lead into gold by simplifying the life of the customer and improving its satisfaction thanks to the precise analysis of its way of purchase.

 

Artificial Intelligence: algorithms and insights

Since its first use in the general public in the late 1990s, the machine learning have never stopped to make talk about it. Its recent victory was in March 2016 via AlphaGo, the software of Google, against the legendary Lee Sedol. We’ve witnessed AlphaGo’s most notable examples of deep learning, which was, the ability of a machine to independently analyze sums of data with an extremely high level of performance.

If such technological power remains exceptional, all of us daily experience the small machine learning without knowing it. How? Well, just surf on Amazon, LinkedIn, Spotify or even Netflix to see these platforms automatically offer suggestions according to their precise taste. These associations of ideas remain pertinent on subjects as fine as the interest for a film, a song, a service or a cross purchase. It is a much less superficial intelligence than it seems but with concrete results.

 

From big data to automatic learning

Well-resourced with quality data, the algorithm analyze deeply in the vast meadows of digital world. They cross distant data from each other to reveal information never brought to light. These algorithms bring us the astonishing results which a human mind would have swept away. For example, in a customer journey, deep learning allows to discover that the intention of purchase can be correlated with an action at precise moment of purchasing action. With automatic learning, one can therefore target with precision every important thing that human understanding can escape.

 

Machine learning: better tracking of customer routes

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According to Salesforce’s state-of-the-art survey published in 2016, customer engagement is a top priority for organizations. Customer satisfaction is the main reason for success, even surpassing revenue growth and the acquisition of new customers. In this context, Machine learning is thus a major ally.

From an operational point of view, most of the machine learning applications used today are subject to a pre-learning phase. A large amount of data is thus processed, during algorithm design, to better guide the search and automate more easily the answers that will be offered to online surfers. It comes to deal with a combination between human intelligence and artificial intelligence. The goal still to be reached, for each organization, is a user experience that is as simple and fluid as possible. The machine learning has already made it possible to take a major step forward thanks to the ultra-segmentation of the profiles for a refined follow-up of the customer routes.

 

Sharing Data: the essence of war

In order to function at full capacity, machine learning must benefit from first-class information. How it’s possible? By adapting an omnivorous diet. Depending on the project, companies use the information they collect through cookies, geolocation, social networks, loyalty programs (which typically collect data on age, location, purchase history …).

Contrary to popular belief, consumers are rather inclined to share their data, but not at any price. This is evidenced by the Columbia Business School’s “What is the future of data sharing” study conducted by the Columbia Business School Center for Global Brand Leadership in 2015 with 8,000 Internet users in the United Kingdom, the United States, Canada, France and India. “Consumers are much more knowledgeable about the issue of data sharing than we originally suspected. According to our study, one of the determining factors in the decision to share them is trust in the brand, “says Matthew Quint, director of the Center for Global Brand Leadership. Researchers at Columbia Business School have come to the conclusion that more than 75% of Internet users more readily share their data with a brand they trust.

 

Customer data: Give and Take

Beyond trust, the sharing of information is based on a give-and-take approach. According to the same Columbia Business School study, 80% of consumers agree to share confidential information in exchange for a reward. It must be a “valuable offer, but this value must be clearly defined and easy to understand to hope for the best possible return on investment,” says Matthew Quint. Young consumers would be more favorable than their elders to concede their personal information. What promises beautiful days to machine learning.

 

All the above points ends on the same conclusion that organizations can get a better understanding and add a new layer of intelligence on their customers behavior by using predictive analysis.

Critical challenges of #DataProtection and #CyberSecurity within your Organization

#DataProtection and #CyberSecurityData breaches are a constant threat to all organizations. And the risk keeps growing: By 2016, the total number of exposed identities by data violations has increased by 23%, with a record of 100,000 incidents, of which 3,141 were confirmed data breaches.  The data now is corrupted/compromised in a few minutes and their exfiltration takes only some days.

 

The worst part is that detecting a violation can take months, with an average discovery of 201 days. Unable to respond quickly, organizations face the risk of exposing valuable data and confidential information. The recovery process can be incredibly costly, and the damage in terms of reputation is incalculable.

 

Why companies must stay alert?

Why companies must stay alert?

The increasingly digital revolution requires companies to constantly be on their guard in order to detect attacks and respond to potential incidents. However, after several years of constant vigilance, many companies are wondering if their investments will one day be sufficient. Some of them even think that they’ve solved the problem with devices to counter conventional attacks (such as phishing, for ex) or to fill in the most important flaws (the identity and access management system, for ex). In reality, that’s not the only thing they must do in order to protect their valuable data.

 

While most companies have laid the foundations for proper cybersecurity, most of them haven’t realized that these measures are only the beginnings of a much wider and proactive policy, and the digital world needs continuous investments on security matters. An enterprise may consider that it has implemented sufficient cybersecurity measures when it will be able to remain permanently within the limits of its risk appetite.

 

Demonstrating the contribution of cybersecurity investments can be challenging. Nevertheless, when a company reaches a high level of maturity in this area, it becomes easier to justify ongoing vigilance by demonstrating the contribution and value of investments: whenever the Security Operations Center identifies a potential attack, the evaluation of the costs generated by the different attack scenarios (particularly the least favorable one) justifies the made investments.

 

How organizations can unfold threats and vulnerabilities?

  • All vulnerability and incident data are retrieved in a single system. By the automation of simple security tasks and correlating intelligence data against threats with security incidents, analysts have all the information they need to protect your business.
  • Through the integration with the CMDB, analysts can quickly identify affected systems, their locations, and their vulnerability to multiple attacks.
  • Workflows are essential to ensure compliance with your security runbook. Predefined processes allow 1st level personnel to perform real security work, while more experienced security professionals can focus on tracking complex threats.
  • By managing an overload alert via applying priorities based on their potential impact on your organization. Analysts need to know precisely which systems are affected, as well as any subsequent consequences for related systems.
  • By improving controls and processes to identify, protect, detect, respond and recover data
  • By creating cyber security awareness within your employees

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How organizations can improve their CyberSecurity?

A company must establish a solid foundation of cybersecurity to protect its present environment. For example by carrying out a safety assessment and building a roadmap; review and update security policies, procedures and standards; establishing a security operations center; testing business continuity plans and incident response procedures; designing and implementing cybersecurity mechanisms.

 

As a business holder, you must consider that your basic safety measures will become less effective over time, so don’t forget to focuses on the changing nature of business environment. At certain point you must highlight the actions needed to enable your company to keep up with the demands and developments of the market. It can be by designing a transformation program to improve cybersecurity maturity, using external assistance, in order to accelerate its implementation. You can decide what will be maintained internally and what will be outsourced and define a RACI matrix for Cybersecurity.

 

Last but not the least, the company must proactively develop tactics to detect and neutralize potential cyber-attacks. It must focus on the future environment and have more confidence in its ability to manage predictable and unexpected threats/attacks. Few companies are at this level, and today it is necessary for them to design and implement a cyber threat strategy (Cyber Threat Intelligence), define and integrate a global cybersecurity ecosystem, a cyber-economic approach, Usage of data analysis techniques for investigations, as well as monitoring cyber threats and preparation for the worst by developing a comprehensive intrusion response strategy.

 

Sources :

Verizon’s 2016 Data Breach Investigations Report

Whitepaper: Insights on governance, risk and compliance

Big Data: 2017 Major Trends

big data trends 2017

Over the past year, we’ve seen more and more organizations store, process and exploit their data. By 2017, systems that support a large amount of structured and unstructured data will continue to grow. The devices should enable data managers to ensure the governance and security of Big Data while giving end-users the possibility to self-analyze these data.

Here below the hot predictions for 2017.

 

The year of the Data Analyst – According to forecasts, the Data Analyst role is expected to grow by 20% this year. Job offers for this occupation have never been more numerous before. Similarly, the number of people qualified for these jobs is also higher than ever. In addition, more and more universities and other training organizations offer specialized courses and deliver diplomas and certifications.

 

Big Data becomes transparent and fast – It is obviously possible to implement machine learning and perform sentiment analysis on Hadoop, but what will be the performance of interactive SQL? After all SQL is one of powerful approach to access, analyze, and manipulate data in Hadoop. In 2017, the possibilities to accelerate Hadoop will multiply. This change has already begun, as evidenced by the adoption of high performance databases such as Exasol or MemSQL, storage technology such as Kudu, or other products enabling faster query execution.

 

The Big Data is no longer confined to Hadoop – In recent years, we have seen several technologies developing with the arrival of Big Data to cover the need to do analysis on Hadoop. But for companies with complex and heterogeneous environments, the answers to their questions are distributed across multiple sources ranging from simple file to data warehouses in the cloud, structured data stored in Hadoop or other systems. In 2017, customers will ask to analyze all their data. Platforms for data analytics will develop, while those specifically designed for Hadoop will not be deployable for all use cases and will be soon forgotten.

 

An asset for companies: The exploitation of data lakes – A data lake is similar to a huge tank, it means one needs to build a cluster to fill up the tank with data in order to use it for different purpose such as predictive analysis, machine learning, cyber security, etc. Until now only the filling of the lake mattered for organizations but in 2017 companies will be finding ways to use data gathered in their reservoirs to be more productive.

 

Internet of Objects + Cloud = the ideal application of Big Data – The magic of the Internet of Objects relies on Big Data cloud services. The expansion of these cloud services will allow to collect all the data from sensors but also to feed the analyzes and the algorithms that will exploit them. The highly secure IOT’s cloud services will also help manufacturers create new products that can safely act on the gathered data without human intervention.

 

The concentration of IoT, Cloud and Big Data generates new opportunities for self-service analysis – It seems that by 2017 all objects will be equipped with sensors that will send information back to the “mother server”. Data gathered from IoT is often heterogeneous and stored in multiple relational or non-relational systems, from Hadoop cluster to NoSQL databases. While innovations in storage and integrated services have accelerated the process of capturing information, accessing and understanding the data itself remains the final challenge. We’ll see a huge demand for analytical tools that connect natively and combine large varieties of data sources hosted in the cloud.

 

Data Variety is more important than Velocity or Volume – For Gartner Big Data is made of 3 V: Large Volume, Large Velocity, Large Variety of Data. Although these three Vs evolve, the Variety is the main driver of investment in Big Data. In 2017, analysis platforms will be evaluated based on their ability to provide a direct connection to the most valuable data from the data lake.

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Spark and Machine Learning makes Big Data undeniable – In a survey for Data Architect, IT managers and analysts, almost 70% of respondents favored Apache Spark compared to MapReduce, which is batch-oriented and does not lend itself to interactive applications or real time processing. These large processing capabilities on Big Data environments have evolved these platforms to intensive computational uses for Machine Learning, AI, and graph algorithms. Self-service software vendor’s capabilities will be judged on the way they will enable the data accessible to users, since opening the ML to the largest number will lead to the creation of more models and applications that will generate petabytes of data.

 

Self-service data preparation is becoming increasingly widespread as the end user begins to work in a Big Data framework – The rise of self-service analytical platforms has improved the accessibility of Hadoop to business users. But they still want to reduce the time and complexity of data preparation for analysis. Agile self-service data preparation tools not only enable Hadoop data to be prepared at source, but also make it accessible for faster and easier exploration. Companies specialized in data preparation tool for Big Data end-user, such as, Alteryx, Trifacta and Paxata are innovating and consistently reducing entry barriers for those who have not yet adopted Hadoop and will continue to gain ground in 2017.

 

Data management policies in hybrid cloud’s favor – Knowing where the data come from (not just which sensor or system, but from which country) will enable governments to implement more easily national data management policies. Multinationals using the cloud will face divergent interests. Increasingly, international companies will deploy hybrid clouds with servers located in regional datacenters as the local component of a wider cloud service to meet both cost reduction objectives and regulatory constraints.

 

New safety classification systems ensures a balance between protection and ease of access- Consumers are increasingly sensitive to the way data is collected, shared, stored – and sometimes stolen. An evolution that will push to more regulatory protection of personal information. Organizations will increasingly use classification systems that organize documents and data in different groups, each with predefined rules for access, drafting and masking. The constant threat posed by increasingly offensive hackers will encourage companies to increase security but also to monitor access and use of data.

 

With Big Data, artificial intelligence finds a new field of application – 2017 will be the year in which Artificial Intelligence (AI) technologies such as automatic learning, natural language recognition and property graphs will be used routinely to process data. If they were already accessible for Big Data via API libraries, we will gradually see the multiplication of these technologies in the IT tools that support applications, real-time analyzes and the scientific exploitation of data.

 

Big Data and big privacy – The Big Data will have to face immense challenges in the private sphere, in particular with the new regulations introduced by the European Union. Companies will be required to strengthen their confidentiality control procedures. Gartner predicts for 2018 that 50% of violations of a company’s ethical rules will be data-related.

 

Sources:

Top 10 Big Data Trends 2017 – Tableau

Big Data Industry Predictions for 2017 – Inside Bigdata

#InternetOfObjects and the Emerging Era of #CloudComputing

Big data and connected objects represent an important source of economic growth according to numerous studies. They open the possibility to connect people or objects in a more relevant way, to provide the right information to the right person at the right time, or to highlight information that is useful for decision-making. Allied to Big Data, connected objects give professionals new opportunities to better understand customer needs and better satisfy them.

 

According to McKinsey, the overall economic potential of the IoT universe could be between $ 3,900 billion (US ‘trillion’) and 11,100 billion per year by 2025! So with 30 billion connected objects by 2020 it’s now necessary, more than ever, to rethink the use of Cloud.

 

The explanation of this boom?
Connected objects are already very widespread and are gradually taking over all sectors. The general public sees it as a way to improve everyday life, while companies are already using it to control and improve industrial processes and propose new services. Cities and vehicles are becoming smart by using different types of sensors.

 

Nearly all manufactured goods entering the market – vehicles, equipment for energy or water supply, health sector equipment, scientific and technical research facilities, machine tools and robots, etc. – all are bound to be connected and, for a good part, to be interconnected.

 

We are only on the premises but very well equipped with advanced technologies, the only thing to do is to imagine their great usage that will respond to every real expectations and will bring real added value. This ability to make our environment much smarter is linked to sensors, to the data collected by these sensors and to the speed of processing of this data. The triangle of Connected Objects, Big Data and Cloud will become essential to transform this universe of connected objects into intelligent systems.

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Future of IOT Data 
The continuous flow of data generated by IOT is challenging the networks. All of these billions of objects that can be interconnected via the Internet are accelerating the real tsunami of announced data. The cloud is a simple and flexible way to deal economically with this mass of data that will continue to grow with time and new uses. And to cope with this huge data, the computing power will have to be adjusted. With the successful adoption of IoT, manufacturers will work on new systems architectures, especially those that are “hyper-integrated”, “hyper-convergent”, and can bring very high performances.

 

Cloud, indispensable for the development of the internet of objects
Connected objects are synonymous with capturing very large masses of valuable data. The gathered information via IoT will have to be stored, transmitted and analyzed for which the choice of Cloud infrastructure is the most appropriate method. Firstly because of the flexibility afforded by this type of offer only a Cloud solution allows real-time adaptation of the infrastructure capacity according to the level of demand. A flexibility for the management of all the connected objects devoted to knowing peaks of load and allows connected devices to interact with powerful back-end analytic and control capabilities. 

Furthermore, this flexibility can play more decisive role for commercial success, a situation in which it is essential to adapt its infrastructure quickly to meet demand. A necessity that affects the companies of moderate sizes seeking to contain their investments in technical infrastructures.

A flexible cloud service for connected devices can facilitate critical business decisions and strategies process by allowing you to connect your devices to the cloud, analyze data from those devices in real time, and integrate your data with enterprise applications, web services etc.

 

New skills and infrastructure needed
Applications linked to IOT are limited only by the human imagination. From automotive to home automation, to medical and healthcare industry, entertainment and education, IOT is pervasive and growing rapidly and transforming all economic sectors. To operate these innovative devices, it will be necessary to develop applications capable of collecting and processing the data that they will generate. The manufacturers of connected objects and the service providers responsible for the management of these applications must therefore provide themselves with appropriate skills and infrastructures.

Value Creation with #BigData and #ConnectedObjects

The Internet of Things and the Big Data have extended the digital revolution to all parts of the economy. With the Internet of objects (IoT) and gathered data we are at the dawn of a new digital revolution. If #BigData helps companies to understand the behavior and expectations of their customers, the connected objects are contributing to the process.

 

Three aspects of the digital revolution in particular are shaking up technology, industry and the economy with profound social consequences: “the decrease of computing and telecommunication costs, which are gradually becoming cheap resources and easily accessible to everyone, IOT evolutions leading into an era of continuous and never-ended innovation and the desire to create something outside the box, a new economic mechanisms which in particular enables the development of activities with increasing returns that redefine the competitive rules of the game”.

IOT

 

One by one, all economic sectors are switching to the digital age by threatening disappearance of businesses that won’t evolve. Companies must consider their positioning in this new paradigm, rethink their business model, to develop new competitive advantages – those of the previous era becoming partially obsolete – and then to transform to implement the new vision.

 

Positioning and competitive advantages: Companies must first understand the potential value creation of connected objects and Big Data in their markets. Here are four key capabilities of connected objects combined with Big Data:

 

  • Monitoring: The sensors placed on the connected objects will provide with more information and control in order to identify and fix these problems. The data can also be used indirectly to better contemplate the design of future objects, to better segment the market and prices, or to provide a more efficient after-sales service;
  • Control: use of the gathered data by algorithms placed in the product or in the cloud makes it possible to remotely control the objects if they are equipped with actuators;
  • Optimization: the analysis of the current and past operating data of an object, crossed with all the other environmental data and the possibility of controlling them, makes it possible to optimize the efficiency of the object;
  • Autonomy: the combination of all previous capabilities and the latest developments in artificial intelligence allows to achieve a high level of autonomy of individual objects (such as household vacuum robots) or complete systems (such as smartgrid).

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In addition, connected objects require companies to re-evaluate their environment, as the data produced and the services and platforms that accompany them allow for system optimization on a large scale. For example, public transport is already being considered in the context of a wider mobility market, in which the aim is no longer to operate a bus or subway network, but to help a Customer to go from point A to point B.

The ecosystem then expands to include transportation facilities in and around the city (bus, metros, individual car, taxis, car-sharing, etc.). .), GPS and mobile applications, social networks of users and infrastructures of the city (road, car parks, etc.).

CONNECTED OBJECTS

Transformation of the business model: Once measured the appearance of connected objects and their impact on a defined market, companies must think of their transformation to excel in this new paradigm. First, the company must evolve most of its functions and their expertise, in terms of:

 

  • Design: connected objects are more scalable, more efficient and less energy-consuming. Greater collaboration is needed between software teams and hardware teams to design new products and services that integrate more intelligence, sensors and remote capabilities in the cloud using Big Data;
  • Marketing: the new data created by the connected objects make it possible to better segment the market and individualize the customer relationship. This individualized marketing also makes it possible to design services more easily adaptable while preserving economies of scale;
  • Customer services: the role of customer services is gradually evolving towards the prevention of breakdowns, sometimes at a distance. The analysis of the data also allows these services to understand the causes of breakdown, in particular to improve the design.

 

We are witnessing a new era of the Internet of Things that, along with Big Data and cloud computing, is one of the key foundations for companies of the future. To do their best, companies will have to acquire much more robust technological infrastructure as these objects should be created within a safe environment where we trust digital technology. More fundamentally, companies need to evolve their structure and governance to gain agility and adaptability.

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