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#Blockchain benefits for Innovation: Disintermediation, Security, Autonomy

The Blockchain brings together three innovative technologies: decentralized architecture, cryptographic protection, crypto-currency issuance.

 

What is the Blockchain?

Literally, a blockchain designates a chain of blocks, digital containers on which are stored information of all kind: transactions, contracts, property titles, works of art … All these blocks form a database resembles to pages of a big accounts book. This book of accounts is decentralized; that means it’s not hosted by a single server but by a part of the users. The information contained on the blocks is protected by several innovative cryptographic methods, so that it is impossible to modify them. Finally, the Blockchain creates a crypto-currency that allows it to remunerate certain nodes of the network that support its infrastructure.

 

1st Innovation – DISINTERMEDIATION: Consensus replaces centralized validation

The first innovation of Blockchain is to generate the confidence necessary for agents (users) to exchange without control of a trusted third party.

Example – The banking system

International transfers are expensive and require several days of processing to be done. In contrary, a transfer with a crypto-currency like Bitcoin is almost instant, secure and free.

Technical explanations

– In order for a transaction to be carried out on the Blockchain, its information (volume of available funds of the issuer, recipient, volume transferred) must be integrated into a block.

– To do this, the transaction must be validated by several nodes of the network (called “miners”) that verify its conformity by solving a complex cryptographic problem (and IT power consumption for Blockchain energy impacts). This result is certifiable thanks to “Proof of Work”. The whole operation, and this is the key word, is called “mining”.

– Once all the minors agree on the validity of the “Proof of Work”, the transaction is integrated into a block. This is added to the “chain of blocks”.

Political Property

The addition of new blocks is the result of an agreement among the actors of the network, which makes the control obsolete by a reference institution. This agreement is the vector of disintermediation and it is incarnated by the collective validation of the “Proof of Work” or “Proof of Stake”.

                                                                                                                        

2nd Innovation – SAFETY: The decentralized architecture and the code block guarantee the inviolability of the information

Two mechanisms guarantee the structural security of the information recorded within a blockchain: a cryptographic process and a decentralized architecture. I’ll explain them separately.

Example 1 – Time-stamping

The proofofexistence.org website demonstrates this inviolability “by design”. It allows to save documents on the blockchain of Bitcoin network to justify its possession at a given moment. Because the document is written on the blockchain, this is enough to prove that the document exists at time T and has not been modified in T + n.

Technical explanations

The code of each new block is built on their previous block in the chain of blocks, so that modifying a block would involve changing all the blocks in the cord, which is impossible.

 

Example 2 – Wikileaks

Before their broadcasts in the press in the summer 2010, Wikileaks has recorded US State Department’s confidential documents on a multitude of worldwide servers. Instead of a single “cookie jar”, the decentralization of hosting makes it virtually impossible to delete all copies of documents. The same logic works for Blockchain.

Technical explanations

Within a blockchain, all the blocks are replicated in the nodes of the network, not in a single server. This decentralized architecture acts as a structural defense against the risks of data theft.

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Political Property

These two mechanisms guarantee the security of the information recorded on a blockchain. The strength of the Blockchain allows its use for sensitive information.

 

The Blockchain and privacy

The list of blockchain transactions can be consulted by all: this transparency is necessary so that the members of the network validate the inscriptions on the blocks and makes it possible to fight against the fraud. However, the identity of each user is concealed behind a pseudonym of 27 to 32 characters. A blockchain is not anonymous but pseudonymous.

 The transparency degree of a blockchain can be adapted to very high confidentiality requirements, which are then referred to as opaque blockchains. Conversely, if necessary, the identification may require several proofs of identity and must be completely transparent: the Blockchain is a modular technology.

 

3rd Innovation – AUTONOMY: The creation of a crypto-currency recompense the infrastructure costs

Today, online services (social networks, payment, hosting, etc.) are backed by platforms that assume the infrastructure needs. In the case of Blockchain, the computing power and the hosting space are provided by the nodes of the network themselves. The physical investment, the computing power and the storage space consumed by the mining are compensated by the emission of bitcoins (or other crypto-currencies).

Computing Power Example: BitShares.org

BitShares is an exchange and service platform backed by the Bitcoin blockchain. In June 2015, BitShares managed to execute more than 100,000 transactions in a second, a performance at global stock markets.

Technical explanations

– “miners” allocate a part of the computing power of their personal machine to solve the cryptographic problems necessary for the validation of transactions (“mining”). This is a remunerated activity.

– The first “miner” to validate a block (set of transactions) wins “tokens” whose nature differs according to the concerned blockchain.

– This opportunity for financial gains leads to a competition between “miners” to be the first to solve the problem and to suggest a “Proof of work”. This competitive situation is pushing the “miners” to invest in powerful machines and thus increase the computing power of the blockchain.

Moreover, as seen in the previous point, the blockchains are housed by the nodes of the network: some of them have a local, exhaustive and identical copy of the whole concerned blockchain. Hence the analogy with an account book shared with the actors of the network.

Political property

Within a blockchain, the infrastructure is no longer concentrated in the hands of an organization but is, on the contrary, broken out in all the points of the network. De facto, a blockchain is self-supporting and independent from third-party services.

 

Understanding the debate between public blockchain and private blockchain

Explanations

Blockchain technology is adaptable: the degree of blockchain opening can be limited to create a so-called “private” or “association” blockchain. This model is different to so-called “public” blockchains, such as the one behind Bitcoin, which anyone can consult and use. Within a “private” blockchain, block validation is performed by a more limited number of nodes on the network. As a reminder, only these nodes have access to all the information.

Example

Financial institutions are preparing for the deployment of a “private” blockchain where the validation of blocks requires only the approval of 10 institutions, as opposed to validation by the entire network. If the majority consensus idea is undermined, this model has advantages in terms of timeliness and infrastructure costs.

Issues

The situation is similar to that of the Internet network where private intranets coexist with the public Internet: the public / private blockchain debate is ideological and technical issues. Some members of the original Bitcoin community have a negative view of the privatization of a technology thought and designed to be open.

Understanding the #Blockchain Economic Revolution

The Blockchain is a revolution that is undoubtedly leading to a complete overhaul of economic activity. It’s not a simple geek trend but still most people have absolutely no idea what the blockchain stands for. It’s essential to distinguish clearly the differences between bitcoin, crypto-currency and the breakthrough of technology underlying below the nameà the #Blockchain. You must know that there are several types of blockchains on the market, and bitcoin is another version of it which got huge success in recent years.

 

To be short, #Blockchain is an information storage and transmission technology that is transparent, secure and operates without a central control unit. Transactions between network users are grouped in blocks. Each block is validated by the nodes of the network called “minors”, according to the techniques that depend on the type of block. This process puts everything on trust between the market players without going through a central authority. It’s an open source system where each link in the chain offers autonomous legitimacy. The decentralized nature of the chain, coupled with its security and transparency, suggests a revolution of an unimaginable enigma. The fields of opportunity open far beyond those who have access to the monetary sector.

 

In fact, it is a revolution, as has been in human history, the advent of commerce. When individuals bought and sold their products face to face, with the handshake, the trust was established. Second, globalization has created new needs. Entities have been set up to protect sellers and buyers. Laws and legal services have developed around financial exchanges. Each market had to have intermediaries at the grass-roots level, without it being possible to assess or quantify a degree of trust between people. What changes with the blockchain is not only its decentralized aspect, but also absence of intermediates. Blockchains could replace most “trusted third parties” centralized by distributed computing systems. More than that, many observers highlight the blockchain as an alternative to any back-office systems in the banking sector. It would also help eradicate corruption in global supply chains.

 

The boom of the Internet offers some good indications on how the blockchain could develop. The Internet has reduced communication and distribution costs. For ex, the cost of a WhatsApp message is much cheaper than an SMS. Just as the cost of a software or an online platform is cheaper than having to sell its products through a physical store. The marginal operating costs, thanks to the Web, have been reduced to almost zero. This has caused profound changes in the telecommunication, media and software markets. The Blockchains result allowed to limit all marginal transaction costs close to 0.

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Blockchains are a low-cost market disruptor for any business that acts as an intermediate in market. They allow things that have never been possible by using existing infrastructure and financial resources. We can exchange things that were not previously considered assets. It can be data, our reputation or unused power. The possibilities are as vast as they are unimaginable, but that does not mean that each type of element will be profitable for a company.

 

It is preferable not to dwell first on the technological aspect. It is much better to focus on the root of your customer’s problem. Successful businesses know how to identify, what is missing or a concern to their prospects, and know how to solve it. Blockchain technology is valuable in a setting where data has to be shared and edited by many unapproved parties. That is the infrastructure. The added value comes from the services that are built around it, with applications or modules.

Currently we are in the infrastructure market phase, there are still standards or platforms to democratize blockchain technology. In the near future, thanks to the crazy pace of development of this system, it will be easier for developers and entrepreneurs to use the blockchain on a daily basis. As easily as the MySQL or MongoDB databases we use today. Once the infrastructure stage is over, the evolution of blockchains will really become exciting. The infrastructure will be a huge database on which companies will be able to operate all kinds of connected objects or devices. The connected devices will collect data, blockchains will ensure, shear and process data; Artificial intelligence applications will automate activities.

 

Just imagine these farms where the product is grown and picked up by robots, delivered at home via drones, with a connected refrigerator that alerts us when we need something from there. An artificial intelligence system manages presets objectives to perfectly match the supply and demand. Blockchains are much more than just a bitcoin. They are the real building blocks of our future world.

Managing Data Traceability: Impact and Benefits

Data is at the heart of digital transformation. A company can’t support data lacking integrity if they aim to advance in their digital transformations initiatives.  The integrity of the data lies primarily in the confidence that users can have in the latter. Most of this trust rests on the traceability of data. In the absence of traceability, it is not possible to know if these data are trustworthy.

 

Data traceability is a concept that companies have been trying to understand for some time.  You might be asking for which reasons do today’s companies need more traceability? Well with a large amount of data from unmanaged external sources (sensors, data streams, Internet of objects), it is essential, for companies, to monitor these data when they are collected, processed and moved to be able to use them effectively. Digital transformation requires higher levels of data integrity. Indeed, companies need to have better data, which can be a basis they can trust.

 

Previously, data traceability was based on two dimensions: “where” and “how”. The need for better analysis and exploitation of the data leads to new demands and extends the definition of data, adding the following dimensions: “what”, “when”, “why” and “who”. Faced with these new requirements, it is necessary to master the bases of the primary components of the type: “where” and “how, especially as regards the impact and the value to be realized.

 

The “where” component of traceability focus on the origin of the data. The “how” component focus on how the data source was manipulated to produce its result. It is also possible to refine these two types of dimension via their level of granularity: “low” granularity and “high” granularity. The “where” component at the “low” granularity level focus on defining an upstream output dataset at the point of consumption to understand which dataset have been used to produce a result. The “how” component of the “low” granularity level focus on the transformations applied to the set of data source to produce the output dataset. On the other hand, “high” granularity level of traceability is concerned with the values ​​of data in the “low” level granularity: instead of where they were created and how they were modified to produce the result.

 

An example will better illustrate the types and granularities of traceability. Let’s take an accounting report, showing the total amount paid to suppliers over a given period. The “where” component of the “low” granularity level would trace all output data from the source invoice to the supplier tables from the accounting application. The traceability component “how” of “low” granularity level would look at how the supplier and billing tables were linked together with the calculation functions that were performed on the billing table to produce the total amount paid to each supplier. Traceability “where” at the “high” granularity level could (to search for the amount paid to a vendor) trace back to the invoices provided by the supplier. In order to take interest in the entire process, traceability at the “high” granularity level could also link to the original request: the purchase order, the receipt operations, in addition to the payment approvals.

 

Benefits of using data traceability

 

Declined in many forms, traceability can provide many benefits in terms of impact and added value to the companies that implement it. Such as:

  • Governance: Ensure the traceability of upstream data to provide owners and data sources with quality and access control results. This will allow data owners to manage their procedure in addition to downstream traceability (integrated with a corporate glossary, data traceability can allow data managers to control current definitions and understanding of terms and fields of data).
  • Conformity: Provide regulatory authorities with information to govern data sources, users and their behavior.
  • Change Management: Enabling users and developers to understand the impact of modifying certain data on downstream systems and reports.
  • Development of Solutions: Improve design, testing and deliverables of better quality. This is achieved through the sharing of traceability metadata, glossaries, and relationship among distributed development teams.
  • Storage Optimization: Provide as an input to archive decisions and provisions, an overview of the data being accessed and indicate where, how often and by whom access is permitted.
  • Data Quality: Improvement of the quality scores defined by the application of business rules and standardization on data, added to the metadata population as input of algorithms and decision making.
  • Problems Resolution: Helps in the analysis of root causes in repair-type processes.

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Traceability also brings a deeper advantage such as focus on the changed values ​​of the basic data entities that are shared between processes, services, and applications. For example, the impact that a change in, position, service, address or even the employer of a contact might have on the marketing, business or maintenance service of a business. According to the “U.S. Bureau of Labor Statistics,” an employee has on average 11 different employers throughout his career. Taking into account the speed at which US residents move and change their professions each year, the potential change in baseline data may be important in light of the adequacy of these statistics to the population of basic data within a company. The ability to collect, validate, distribute and track these changes in a timely manner could lead to better protection of existing revenue streams and the ability to capitalize on new revenue in B2B or B2C business relationships.

So, the companies which take advantage of traceability, are able to find data faster and are better able to support security and privacy requirements.

The Impact and Challenges of Artificial Intelligence for Next-Gen Enterprises

Artificial Intelligence (AI) is not a new phenomenon. It continues to develop and its applications are already very present in our personal daily life (gaming, robotics, connected objects …), arousing as much enthusiasm as fear. This complex concept gained its success in the science fiction world. Although AI is still calling for a more or less fantasized imagination, it is an integral part of reality, and it can be found in many services, systems and applications.

 

What can be the role of artificial intelligence in the enterprise of the future? Will AI make organizations smarter? These are the main questions that have motivated big companies, with the objective of analyzing and anticipating the impacts of this revolution in progress. In this post, I’ll be discussing organizational, legal and ethical issues related to the governance of artificial intelligence in large enterprises.

 

A critical factor in adapting the company to the evolutions and challenges of AI environment is to rethink relationship with the company’s stakeholders, and in particular with the customer. It doesn’t mean that one must highlight that “the customer is important” but to emphasize the interaction with the customer. With that being said, client exists only through the interest and interactions developed with them.

So, the question companies should ask is how can they develop a successful interaction with the help of artificial intelligence? What does this mean concretely in terms of channels, content, customer knowledge and, above all, commitment to the customer?

 

Some companies have “Innovation and prospective” unit to carry out an analysis and a reflection of AI impact within the company. This is to take the step without neglecting the employees who are at the center of the subject. These cells allow the sharing of ideas. As the applications of artificial intelligence within the company are diverse, such as increase of human expertise through virtual assistants; optimization of certain products and services; new perspectives in research and development through the evolution of self-learning programs. The objective of this unit is to exchange in order to make the prospective, in a participatory way, through conferences, roundtables, written reports or scenarios, depending on the choice of the structures.

 

The Impact of Artificial Intelligence for Enterprises

 

Artificial intelligence technologies are already anchored in our daily lives. These technological advances intensely question the managerial and organizational practices around innovation in large companies. Many conducted surveys demonstrate that in general, companies do not have a dedicated budget for artificial intelligence. Nevertheless, there are either investment projects or resources that can be allocated to artificial intelligence teams integrated into the wider data teams. Be that as it may, the subject of artificial intelligence is present in large enterprises; It may remain theoretical but may also be the subject of initial experiments, notably concerning the predictive algorithms. Artificial intelligence does not fundamentally change everything in the company, it will rather “increase” performance, automating or perfecting certain processes and / or operations.

 

Benefits for organizations:

 

Today, artificial intelligence already generates many benefits for organizations, notably by:

  • Responding to Big Data issues; Artificial intelligence relies in large part of the search and mass analysis of data from which it can learn;
  • Increasing human decision-making expertise, online help assistant: a Hong Kong-based company, Deep Knowledge Venture (DKV), possesses, for example, artificial intelligence at its board of directors. Vital (Validating Investment Tool for Advancing Life Sciences) who makes investment recommendations and is also entitled to vote;
  • Optimizing services and products: improving customer knowledge, decision-making and operational processes;
  • Strengthening systems security: in the area of ​​cybersecurity, artificial intelligence becomes a structuring element of IT infrastructures, in order to secure networks. Automatic recognition is well established for the detection of fraud, and experts are under way to create algorithms that will identify threats that human brains and traditional security mechanisms fail to recognize.
  • Helping to make discoveries: some companies in the field of health analyze all the scientific publications related to a particular area of ​​research, which allows them to look for new properties, new molecules.

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Challenges:

 

The challenges for large companies are numerous. Starting with cultural and organizational changes. As noted in the Telecom Foundation’s Watchbook No. 8: “The craze for artificial intelligences has been accelerated by the availability of AI capabilities in the form of APIs (on the one hand, Vision or predictive), and the source code of the platforms of Machine Learning released by major Internet operators, on the other hand “.

These technology facilitators will keep pushing companies to become APIs (APIs) in order to optimize their resources. It is therefore necessary to understand the world of APIs in this transversal, cross-enterprise approach, which is not without posing a number of challenges for large companies. To succeed one must develop the fallowing roadmap strategy:

  • Build stronger relationships with clients;
  • Optimize internal processes;
  • Accelerate the development of new developments.

 

To conclude I’ll say that we live a golden age of artificial intelligence, boosted by the increasing interest of web giants for the stakes of Big Data. The first AI investors are indeed the pure players of the internet and the main players of the software. The movement is launched, and it is our responsibility to anticipate the effects of this revolution on large companies.

Artificial Intelligence vs Human Intelligence

Whatever the encouraging results and the progress of #ArtificialIntelligence (#AI) the world can see, we are far from the development of an intelligence such as human intelligence. More and more studies show the major importance of our sensory relation to our environment.

In his book, Descartes’ Error, neuroscientist Antonio Damasio writes that “Nature appears to have built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it “. In other words, the human thinks with all his body, not just with his brain.

This need of physical survival in an uncertain world can be at the root of the suppleness and power of human intelligence. But few AI researchers have really embraced the implications of these ideas.

Artificial Intelligence vs Human Intelligence

The motivation of most artificial intelligence algorithms is to conclude patterns from vast data sets – so it could require millions or even billions of individual cat’s photos to gain a high degree precision in the recognition of cats in general, for example.

But when a human confronts a new problem, most of the hardest work has already been done. In a way that we are just beginning to understand, our body and brain have already built a model of the world that we can apply almost instantly to a wide range of challenges. But for an AI algorithm, the process starts at zero each time. There is an active and important line of research, known as “inductive transfer”, based on the use of knowledge previously learned by machines to illuminate new solutions. However, as is, it is doubtful that this approach is capable of imitate something like the richness of our own body models.

 

If Stephen Hawking’s caveat that smart machines could put an end to humanity is relevant, technology is still far from proposing something that is more or less approaching human intelligence. And it will be impossible to achieve this goal if experts won’t think carefully about how to give the algorithms a kind of long-term relationship and embodied with their environment.

 

Currently #AI has beaten humans in poker, but not to forget that a computer can’t win at poker if great set of algorithms aren’t behind it. The victory of AI proves the ability of a computer program to learn from human to surpass him but doesn’t mean that human intelligence has been left behind by the artificial intelligence.

 

At the same time that the American Gafa (Google, Amazon, Facebook, Apple), the Chinese BATX (Baidu, Alibaba, Tencent and Xiaomi), car manufacturers and all industries and services make the bet of artificial intelligence, Would it be necessary to conclude that future sounds the death of human intelligence?

 

Will we be, in near future, led by robots that have become, thanks to humans, more intelligent and more effective in managing our lives? Or, conversely, is not the victory of a computer program ultimately only the success of computing and calculating power … and nothing else than that. This question will be asked repetitively and it’s up to each individual to decide the answer we want to give.

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We must not forget that #ArtificialIntelligence only helps us in our daily lives, by assisting us in various activities that we are moreover willing to entrust to more efficient and technical than we do to realize them. For example, to order a lunch/dinner, to chat with an after-sales service or to drive a car is in no way the demonstration of talent, intelligence or the human mind, nor of the domination of artificial intelligence. Is it not, moreover, the characteristic of human intelligence to know how to invent machines to replace it and make it better in such activities?

 

The recent success of “chatbots”, these small programs able to make the machine interact with the man thanks to the artificial intelligence, testifies in the same way. Thanks to them, it is now easier to manage your gas bill or know the terms of voting at an election. And tomorrow, they will allow us to dialogue with our car, our house and all connected objects of our world …

 

For it is ultimately the primary meaning that must be restored to artificial intelligence: to be at the service of human. Whether it deals with the recognition of voice, images, movements or their interpretation, artificial intelligence is only a tool, designed by humans, to render a service more efficient and to free man from the constraint of Activities entrusted to him.

 

Emotion, the shielded hunt of the human

True, technological breakthroughs frighten us as much as they fascinate us, but we have the certainty that human intelligence will remain dominant as long as it knows the only thing a machine can never transmit: emotion. This means that artificial intelligence can only assist us with functions and tasks that do not involve emotion and feelings. The man will then take over the artificial intelligence.

 

It is also in this case that technology may well make us much more human by reminding us of our irrevocable comparative advantage: our ability to experience feelings, to understand others, to anticipate their expectations, to guess their Fears … No machine is capable of feeling emotions with the finesse, precision, delicacy but also the weakness and sometimes the charm of the emotional intelligence of man.

 

Faced with the increasing irruption of artificial intelligence into everyday life, the man-machine relationship “remains to be defined”, but leave impression that the “upheaval will be profound”.

IT challenge N°3: Encouraging Innovation by Rethinking the Organizational Working Model

The role of digital technology is rapidly shifting, from being a driver of marginal efficiency to an enabler of fundamental innovation and distraction. Creating value out of knowledge and ideas is the key to success for companies in this existing and upcoming connected digital era. Companies certainly need to change the way they identify, develop and launch innovative ideas/products.

Encouraging the Innovation by Rethinking the Organization’s working models

Mastering the uses: 

 

The most innovative digital procedures are no longer only created within the companies. Experts must seek innovation on the “street” usage, and open themselves to the collective intelligence of their ecosystem by no longer reducing innovation to a “inside of the box” population.  This heavy trend, which everybody can observe on a daily basis, upsets the organizational models of companies.

 

Digital transformation does not happen overnight, but it certainly promotes a matrix and cross-functional working option within the company, where collaboration is inescapable. But, in some cases, internal collaboration can come up against individual resistance, organizational restrictions and cultural habits.

 

The challenge is to succeed in mixing cultures and habits within the organization in order to achieve a multidisciplinary collaboration that generates innovation. However, one should not be mistaken about the main objective of collaboration which is only a medium to serve a purpose such as innovation, performance, value creation…

 

Collaboration Endorsement: 

 

Appropriating digital uses where they appear, requires know-how to mix top-down and bottom-up innovation approaches, or crowdsourcing and R & D … But the hierarchical culture in large organizations goes against these innovative approaches that require the development of collaborative approaches: the risk / benefit ratio remains difficult to evaluate in the collaborative approach when there is a loss of control for the manager and a redistribution of power. It is therefore necessary to accompany the evolution of behaviors and practices by working with multidisciplinary teams.

 

Organizational issues are linked to the geographical dispersion and multiculturalism of the teams, even if digital tools seek to mitigate this dispersion, localization, travel time, different languages ​​and cultures are points of attention. To succeed, one must established the right behavioral KPIs to measure the traction of their business collaboration.

 

 

Challenges: Capturing innovation and skills transformation

 

  • Globalize: create global digital platforms to facilitate the work of teams (multinationals, in a mobility situation) to capture both innovation and customer experience. These digital platforms are essential in transforming organizations.
  • Capitalize on social networks and fan communities.
  • Experiment: organize events to innovate
  • Reconfigure: transform skills and adapt human resources to recognize collaborative approaches (valorization, recognition, retribution, identification of leaders), but also to frame practices and accompany change (right to disconnection, stress management, charters of use …).
  • Convince your investors about your digital vision and the long-term value-creation impact (top and bottom line) of your digital transformation journey.

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Leaders across all industries are writing the next chapter of the digital economy. It is time to either become part of the story or just another footnote in the history of disruption

IT Challenge n°2: Rise of new Partnership Models

 

 

2020 companies will be totally interlinked organizations within an ecosystem in which new strategic partnerships and associations will be formed, as well with customers, suppliers and competitors!

 

A profound transformation of the ecosystem

The growth of value creation is a major trend in digital era. We witness more and more companies opening up, thanks to the multiplication of the interactions allowed by cloud, data repositories, connected objects … This condition requires companies to rethink their business partnership strategies within their ecosystem in order to succeed in the age of digitization.

This ecosystem is very extensive, with an interesting diversity of actors, such as, GAFA (Google, Apple, Facebook, Amazon), start-ups, innovative SMEs, communities, customers, employees, self-entrepreneurs, suppliers, public and local authorities and political institutions… In the era of “co-something”, a company can no longer succeed alone in its market, particularly because of the rapid emergence of new business models, competitors “out of nowhere” and an accelerated renewal technology.

 

The challenges: anticipate and ally

Controlling the ecosystem depends on anticipating the evolution of it’s different actors, to be noted that actors in the traditional IT are not necessarily those of the current ecosystem, nor, of tomorrow. Some will disappear or merge, others will emerge, many will become partners.

Establishing a good relationship with the right partner, which can be a supplier, requires joint sharing of opportunities and risks, commitment to common goals, and shearing value. And this sharing of value aims to bring something new and positive to the partners, and ultimately to help them grow. Strategic partnerships can be established when there are common objectives for value creation. With this perspective, the partnership is strategic and is quite different from the traditional customer-supplier relationship (even major), in which the parties are bound by a contract for the providing services.

The objectives of each party must be the same and the balance of the relationship arises precisely because of that different but valuable opinions and ideas.

 

Challenges: Negotiation and Confidence

  • Collaborate: one of the typical challenges of partnerships will be to manage the paradox between internal resources (including CIOs) that are experiencing difficulties and struggles, collaborating and, on the other hand, the market, which requires close collaboration to better innovate.
  • Dialogue: companies are confronted with a cultural interoperability challenge in order to engage all the actors involved, even if they do not share a common language.
  • Establishing trust: a partnership relation is always based on trust. Thus, it is not a question of “collaborating to collaborate”, but of collaborating to win together, in order to create communities that engage clients and collaborators.

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R-Link is the result of a long-term partnership between Renault and Tom-Tom. R-Link is an integrated multimedia tablet, driven by a tactile control or an advanced voice command. It combines, the various functions related to multimedia in the car such as, navigation, radio, telephony, messaging, well-being, eco-driving. Renault’s interest in combining with Tom-Tom was to increase the value for its customers, to know them better and to improve the level of service.
This example illustrates the idea of a service platform: Renault added services to its products by developing the customer experience.

 

To conclude, I’ll say that the success of these new partnership model depends only on the business taking much greater role in designing, building, and exploiting the technology, platforms, and data it needs to succeed. Overcoming challenges of traditional IT management is a step forward of bringing IT closer to its true mission and succeeding in all IT collaborations.

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.

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