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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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