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Hybrid Cloud Myths Busted

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

 

Hybrid-cloud-

 

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

benefits of Hybrid cloud

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

 

Private + Public = Hybrid:

 

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

 

A Hybrid Cloud is Complex and difficult to implement:

 

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

 

The public cloud is more cost-efficient:

Cost savings via Hybrid cloud

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

 

On the cloud, the data control escapes you:

 

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

 

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

 

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

 

hybrid Cloud as digital transformation

 

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

 

Sources:

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

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

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

 

the IoT _ Internet of things

 

    • Product-as-a-Service

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

    • Monetizing and securing data

 

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

 

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

 

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

 

In short: The Customer is ALWAYS the King!       

 

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

 

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

 

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

 

Sources:

 

Key strategies of a Big Data project implementation

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

 

Identify and collect important data for your business: 

 

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

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

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

 

Manage data quality: 

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

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

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

 

Analyze and disseminate information: 

 

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

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

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

 

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

Cybersecurity and Digital Businesses Transformation

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

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

 

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

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

 

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

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

 

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

 

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

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

Artificial intelligence

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

 

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

 

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

 

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

 

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

 

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

 

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

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

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

 

Source: Webbmedia Group – 2016 Tech Trends

Cloud computing implementaion and its benefits

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

 

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

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

 

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

 

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

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

 

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

 

Is implementing cloud services worth it?

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

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

 

Advantages of Cloud Computing:

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

 

How the Cloud has convinced organizations?

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

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

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

 

The impact of cloud on the business model of companies

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

 

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

 

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

How to succeed your Big Data project?

big data project

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

 

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

 

  • The initial objective is important

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

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

 

  • The concept of uncertainty

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

 

  • Intelligent Big Data

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

 

  • Impact of Big Data on organizations

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

 

  • Big Data technologies are available

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

 

  • The data is the new oil

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

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

 

  • A Big Data project must be managed differently

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

 

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

Big Data, Big Problem?

In order to measure the progress of companies in the exploitation of their customer data, EY surveyed some known European companies. The purpose was to see the difference between the “buzz” generated by the fuzzy concept of big data and the reality of large companies. The results of this survey, conducted among more than 150 European companies, reveal that despite a largely positive perception, the “Big Data” hasn’t really taken place in reality unlike it has to be the case.
Let’s check the list below to know more about these issues that’s coming in the way of companies in order to integrate big data in their strategy.

 

Data collection via traditional channels:

Every businesses and organizations accumulate various type of data, such as financial information related to revenues and expenses, data about their customers, vendors and also about their employees. We’ve noticed that traditional file systems are still used by companies to gather data in order to increase their knowledge, understanding of customers, products and deploy marketing strategies. (Storing data in paper files, folders is a form of traditional system).

 

Unstructured data:

Companies collects huge volume of data and need valuable knowledge extracted from these data to improve their business results, still the survey reveals that 45% of respondents agrees that the data collected isn’t sufficiently exploited and only 27% of companies are equipped to manage and analyze the gathered data from many sources.

 

Analytical skills:

One of the top adoption challenges of big data is obtaining the skills and capabilities to interpret it. EY survey shows that for 70% of respondents, a team of less the 10 employees is dedicated to analyze the gathered data. Only 6% of companies have a staff of more than 50 dedicated people to decode useful information from the data.

 

A lack of data processing tools:

Good thing is that most of these large companies are aware of the progress of these unstructured data. 59% of respondents claim to anticipate an increase in the volume of data reliability within 18 months. At the same time, only 27% of them affirm that they have established internal processes to operate reliable or unstructured data.

 

Analysis of the data still (too) little predictive and real-time oriented:

Only 10% of companies operate their customer data for predictive purposes and 5% of them do it to optimize the technical process or to increase timeliness and increased storage capacities (key elements to exploit growing volumes and ever faster data and information flows).

 

The lack of mainstreaming in project management (Big) data:

39% of the respondents recognize that internal silos remain a drag on the optimal use of customer data. Each business has a habit of using and transforming data from its databases to meet its own goals or business issues, capital data cannot flow in the company, which explains the lack of a unified vision.

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The absence of ROI of Big Data projects:

Only 29% of respondents consider that the Big data is a major milestone and has the potential of big impact on business. As for the establishment of a “Big data Action Plan with concrete actions,” they are only 18% who actually did. Regarding the return on investment in particular, 58% of companies surveyed did not seek to quantify the contribution of solutions to their business performance. Again, the gap is huge between the most mature (77%) and “not mature” (3%).

 

Lack of sponsorship from the managing director:

The lack of ROI measurement, coupled with unfavorable economic conditions explain the caution of most CEO on the subject. The majority of small-medium size companies considered perception of top management as a brake on the optimal use of data within their business, 57% of them, against only 11% for the big size companies.

 

The reluctance to share personal data:

The issue related to data security, in which we can add the protection of privacy is the key to the future of the big data. The study of EY highlights that 70% of consumers are reluctant to share their personal data with companies and 49% say they are less likely to do so in the next five years.

 

Low awareness of safety issues and protection of data:

Among the companies EY surveyed, 30% believe they are not concerned with the protection of privacy issues during the operation of their customer data. 92.3% Companies, identified as the most mature in the Index EY Maturity Data, consider that the issue of protection of privacy is a priority. While for 58.6% of those who have been identified as less mature, don’t care about protection issue.

 

To resume:

Two-thirds of European companies (63%) consider that the big data is a valuable and interesting concept but still too vague, difficult to integrate within companies, in terms of organizational transformation, ROI strategy, management and training skills.

 

Even though big data is the petrol of this century, currently, half of the companies did not even studied any opportunities related to big data. Only 9% of companies surveyed have launched both the big data opportunity to study and put in place a comprehensive strategy to manage their customer data. Half of the respondents acknowledged that the absence of “a clear plan of action that constitutes a road map for the entire company” is an impediment to the optimal use of customer data. 57% of companies consider perception of top management as a brake against 11%.

 

The big data approach can be useful and beneficial for every businesses, but without a solid plan aligned with your business objectives you may miss out an elegant solution with a guaranteed return on investment.

Survey : Big Data and the IoT

Connected objects confront the Big Data to new needs, including the quick processing of multiple data sources from the Internet of Things. These levers of innovations are still at stages of maturity, but they represent a real potential for organizations.

 

The rapid development of the Internet of objects has made the data analytics more challenging due to processing and collecting data via different sensors contained in our connected objects. According to the report from IDC (International Data Corporation), the Big Data market will amount mora than $ 125 billion in between 2016-2019.

 

Future of IT: Big Data and Internet of Things are two sides of same coin and a recent study (January 2016) by Tech Pro search shows that Big data and the connected objects are an important economic growth driver via their collaboration in collection of valuable data. Even if they open the possibility of connecting people or objects more relevant, to provide the right information to the right person at the right time, or to highlight useful information for decision making, survey result demonstrate that large companies are more than twice as likely to integrate Big Data solutions than small businesses.

 

The promise of big data depends on the ability of a company to use a connected device to compile data, both internally and externally. Since Big data is a new source of economic value and innovation, the value of data evolves from initial use to future potential uses with higher added value, (all data are thus considered valuable by definition). Study shows that he cost of the analytical processing of big data is expected to decrease this year, allowing companies to collect valuable data more easily.

 

Topics studied in the survey :

  • Data collection via the Internet of Things related to the market place;
  • Data collection via the Internet of operations related data objects;
  • the budget and the number of employees dedicated to the Internet of Things;
  • data security;
  • the advantages and disadvantages of data collection for the Internet of Things.

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Who uses big data?

The first objective of survey was to assess where big data is in use by determining where implementations took place. The study shows that 29% of companies have deployed big data solution, against 61% who did not.

Implementation of Big Data

 

The size of the business is important, because large firms are much more likely to implement big data than small business. In fact, respondents in companies with 1,000 or more employees have implemented big data almost 1.5 times more than those who did not (49% against 35%), main while only a fifth of smaller companies have implemented big data. Three quarters of these companies haven’t any big data solution.

Big Data by company size

 

The region is also important. People whose business is established in the Asia Pacific region are more likely to have responded that their company had achieved an implementation of big data, 19% more than in Europe, which comes in second place. Conversely, the big data is rare in Central and South America.

Big Data by region

Who uses the Internet of Things?

As big data is associated with the Internet of Things, Pro Tech Research also sought to know who uses IoT to collect data relating to the operations or the market place. Only slightly more than a fifth (21%) of respondents say they use the internet of things and 35% plan to put it near future.

IOT

33% of respondents in companies with 1000 or more employees indicate having no current or planned implementation system of IoT, against 59%, 47% and 46% for other companies (ranked in descending sizes). Like the big data, large companies are more likely to have implemented or plan to establish an IoT system to collect internal and external data.

IoT and Big Data

However, it is interesting to note that unlike the big data, internet objects is less likely to be used not in small companies, but in the medium-sized companies (50-249 employees) and midcap companies (250 to 999 employees). This demonstrates that the Internet of Things is more rooted in small businesses as big data, while the reverse is true in both categories of intermediate size.

 

There’s no doubt that IoT is marked by the development of social networks, partnerships and complex interrelations, enabling the development of industrial processes, improvement in services quality and performance available to individuals and consumers. IoT feeds data and increases its value and volume.

 

To learn more about big data and the Internet of Things, download the full report TechProResearch, “Big Data and IoT: Benefits, Drawbacks, usage trends”.

 

Sources:

Research : Big Data and IoT – Benefits, drawbacks, usage trends

3 things to know about Big Data and Predictive Analytics

Big Data Growth

If you’re like most business owners, you’ve probably tried or already trying to accomplish a lot of things with a reduced team. You’ve already tried or you want to give a try to build a sales or marketing team with few resources and few staff members. At the same time, you have to juggle growing revenue, rising funds and expanding your customer/user database. But, in all this rush, there is one thing you mustn’t forget, which is your data.

 

You use them every day by giving them more or less important, (to be honest I think still now most companies doesn’t care enough about data). Yet, they can have a considerable impact on the success of their business. So what can “Data” help you with?

 

Here are three things you must know about the data and its potential to taking your existing business to the very next level.

 

1. Everyone holds the information (data)

Some leaders thinks that their gathered data isn’t revealing or valuable because their business isn’t big enough to gather valuable data. But they’ve to realize that’s NOT true. Even in small companies, data is EVERYWHERE and you must take them into consideration to survive in this competitive world.

 

Everyone who turns on an electronic device and any activity that can be connected on-line, whether a click, online, downloading a document or white paper, generates a “digital footprint.” In 2010, Google Chairman Eric Schmidt said that five exabytes of data were created every other day! To give you an idea, this is the number of data that has been created between the dawn of civilization and 2003. In 2014, IDC (International Data Corporation) announced that by 2020, the universe digital will double approximately every two years.

 

2. Data can interpret your past

Companies that gives importance on their data typically use to interpret the past so they can better plan their future. For example marketers can see the number of visitors who viewed their website over a month, or the percentage of visitors who clicked on pages before leaving the website. A sales manager can gain visibility and detailed reporting on sales opportunities and sales pipelines via sales CRM app.

 

A hand on your data can be useful to review your past activities, make diagnoses and help to better organize future activities. However, you simply get a view of your past performance. The data can only predict but can’t tell you the guarantee about what will happen in the future.

 

3. Data and predictive analytics

The most advanced organizations are, of course, also concerned by “data” by using it as a predictive technology to anticipate and better plan than their competitor. For them, it’s THE tools that allows them to optimize their actions and future decisions. So basically, predictive analysis can predict future trends and behaviors from the existing data.

 

Take for example these below operations. Predictive analytics can help you determine:

 

    • Who is your top priority customer and with whom you must continue your cooperation,
    • The contacts you need to target for a specific action;
    • How and when prospects are likely to buy from you.

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Prediction tools provide clarity to sales and marketing teams to help them to make decisions. Their activities and actions will, for sure, generate the best results for your business thanks to the help of “predictive intelligence”. Predictive analytics actually deals with extracting information from billions of rows of historical data and use it to predict trends and behavior.

 

The different prediction models are designed to determine the changes or similarities in past purchasing patterns and highlight the most important ideas. For example, you can find:

 

  • Which customers your sales team must contact first;
  • What arguments you must highlight for a specific client;
  • What type of content (white paper or video, for example) and what form of communication (email, phone …) will get the best (potential) customer engagement.

 

I’m sure you’ve understood the importance of, even the tiny data, in this huge big data, as most of our activities leave a digital footprint. The analysis of these data and will help you better understand your past actions (strengths, weaknesses and areas for improvement) and then act on your future actions through predictive analytics.

 

Sources:

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