According to research based on an analysis of more than 100,000 e-commerce stores, 50.3% of the traffic in virtual shops are from mobile devices (being 40.3% from mobile phones and 10% from tablets) and only 49.7% from computers. It may sound crazy, but the fact is that mobile commerce is already a reality.
Also according to research, the Google search traffic comprised 18% of traffic from computers and only 12% for mobile phones. These data seem to show that computers are being used for research products, while mobile devices for spontaneous purchases, which are instigated from Social Media, Email Marketing and SMS Marketing.
The increase in purchases on mobile devices also brings another exciting trend, which we are calling “always buying”.
According to research, the traffic on e-commerce sites from computers is traditionally peaked between Monday and Friday and disappeared during the weekend. Since mobile traffic behavior has a somewhat opposite peaks occurring during the weekend.
So when you combine mobile, tablet and computer, traffic to online stores, you will find noticeable peaks. In other words, purchases are something that people are always doing.
It’s no secret that it’s better meet the need of consumer to navigate through touch and let them make a purchase from their tablet or mobile device, than losing a lot of sales. Something that cannot be good especially when you are trying to make the first sale to this client.
Big Data As you know, first of all – the facts. And then they can already turn as you like. Famous saying about “There are Lies, damned lies, and statistics.” is no longer a joke, but it is the daily norm. The thing is that, in itself it is an array of data, no matter how long it was not – it’s just an array of data. In order to extract information from it, it is necessary to perform operations on it. And then the most important step – the analysis of the data obtained. Something that only people can do. And their judgment is subject to inaccuracies and distortions.
Even if the data is based on correct measurement. Now, in many areas of science and businesses undergoing fundamental changes brought about by the introduction of systems of mass data collection and analysis. Thanks to the Internet and other means of mass communication which made this work easier than ever. We live in a time when the data is simple to get, but to understand what they look like – no. Many companies, commercial not just sit on the huge deposits of data but on hundreds of terabytes. Ability to collect new and unprecedented – API, research and other tools at your disposal. But carried away by the pursuit of terabytes of data and gigahertz processors, which will process them, we forget about the purpose of such studies.
In the end, “Big Data” should look for large data sets, depending, detection of which is not under the power of analytics of humans. But there are some important questions that remain unanswered, despite the abundance of news on Big Data in the network.
Here are five questions that, I think would be worth to lift everyone who is going to work in the area of Big Data.
Go through each of these points in more detail.
More – Does Not Mean Better
Despite the «Big», quality is more important than quantity. And in order to understand the quality, it is necessary to understand the limitations imposed by the data. One of these restrictions – the way in which it is sampled. The accuracy of this method is important for all of the social sciences, as well as economic research. Sampling method determines the conclusions that can be drawn on this basis – which methods of analysis and extrapolation may apply. So, to judge the representativeness of the sample, it must be random. If you examine the topological properties of the key role played by diversity.
Through algorithms of «Big Data», it is possible to find statistical regularities in large volumes of data, even if in fact they are not there. In such circumstances, the appearance of false predictions can only be a matter of time. But given the common roots and methods of obtaining the true, highlighting them will be extremely difficult. When it comes to research, the sample is made in accordance with the scientific requirements. Type of sampling is planned in advance and are collected in accordance with specified requirements. It is not easy, but it allows you to make plausible estimates based on incomplete (and they are always incomplete) data. Big Data here is changing the rules of the game, allowing the (theoretical) research for the available data set. It is impossible to interview all the people on earth, but it is possible to collect data on all users on Facebook, and Facebook itself certainly does this.
However, researchers usually do not have access to such samples. For example, when doing research on users of Twitter, researchers usually do not have access to data for all users, they have access to their own tape and believe that based on this, they can make any judgments, but this sample is not complete and is not accidental. Many believe that placing a large amount of data can be carried out on the basis of their research and make judgments simply because the volumes that they have are so great, but it’s wrong. Without an understanding of the structure and nature of the source of these data, it is impossible to choose the right methods of analysis and processing. As a result, the conclusions drawn from the analyzed data will be incorrect.
All The Data Is Not Equally Useful
Because of the volume, many researchers believe that the algorithms of Big Data is the best research tool. Their “purity” often do not attach importance. I was very surprised by the perception in some quarters and the view that the further development of Big Data technologies will make the rest approaches to unnecessary massive research. This view often pops up in connection with research on the social networks. Indeed, why spend on costly public opinion polls and phoning people, process profiles if you can just take a sample of data from social networks? But the opinion data, say, Facebook is more accurate than those which are obtained through surveys by sociologists – is wrong.
In the first place because those who so claims do not see the difference between the sources from which these data are obtained. And I’m not even against of surveys of people and collecting data on their behavior in computer networks. There are many types of social networks, which we unite under the general term. Each of them requires specific methods of research and data collection. Similar structures exist in other areas of statistics and analytics. Moreover, often the data of electronic networks can rely a stretch. Social networks may extend further than noted on Facebook and at the same time does not include many people who have been marked.
Do not forget that today computer networks provide us with a rather primitive way of displaying our relationship. In fact, everything is much more complicated than one would think, looking at the beautiful pictures of social graphs. In many cases it is necessary to take the amendments to the inaccurate or misleading data collected. Universal data do not exist, and the ability to analyze them, to carry them over calculations or model, this does not change. Must be very well understood what information may or may not be removed from any data.
“What?” And “Why?” Are Different Questions
Marketers love Big Data. Mainly because they do not understand how it works or it can give data. For example, they confuse with facts and reasons. For example, the number of “likes” on social network page with brand recognition by people. The analysis of the behavior and interactions of people, financial transactions, and so on are very important task. But this is only the first step to understanding what will happen. In order to predict future behavior, it is not enough to know the answer to the question “what’s happening?”, You also need to understand why this is happening. The answer to this question often follows directly from the first, and to identify these issues is even more dangerous. But to draw conclusions based on the data collected – a difficult task that requires considerable knowledge in their field and a well-developed intuition. In short, even if properly collected and analyzed data, without a qualified expert cannot do if you want to understand that, in fact, do all these figures and graphs, and what the implications of this can be done. And this brings us to the next problem.
Interpretation Of Information
However sophisticated analysis algorithms were not there, they still have to be interpreted by humans. And it is not even important, who is this person – you, the marketer or specially hired analyst. Interpretation as a rationale for the analysis, their integration into any system, is inextricably linked not only with the analysis, but also the personality of the analyst. According to the same data of five different people can make different conclusions. And further steps should be planned precisely in view of these findings. And if the conclusions were wrong, the consequences of these actions can be disastrous.
Example: A Friendster is not a successful predecessor, Facebook, studied the work of sociologists before starting the network. In particular, one study concludes that one person can effectively support only about 150 social relationships with other people. Unfortunately, Friendster interpreted this conclusion as a guide to action and limit the number of “friends” in the ceiling 150. As we can see in the example of Facebook, this is not enough. An error occurred in the interpretation of the concept of “social cohesion”, namely in identifying it with “friends” on social networks. Shows all the same Facebook, these concepts are not equivalent. Also errors in interpretation often occur when the analyst has to combine the data with the theory, which he adheres under which interprets the results should be taken. When the facts are joined with bad theory, there are two options – either to “correction” of the facts (in other words – the rejection of all experimental data except those that fall within the framework of the theory), or the recognition of the theory wrong and the construction of new (what can not all, and a little head to the question “What are the results of the analysis?” wants to hear the answer “I do not know”). Often, such a choice is made unconsciously – we are all susceptible to perceptual distortions that make us automatically dismissed as unimportant and false information that is poorly corresponds with your views.
What Is Good And What Is Bad
Ethical research concerning Big Data is still a “gray zone” in which there is no established rules or patterns of behavior that could be followed. The apparent “impersonality” of the data collected by automatic algorithms, plays into the hands of researchers. We are getting used to manipulate arrays of personal data as if it’s just ones and zeros, not quantified with the lives of hundreds of people. People who usually were not asked whether they want to participate in such studies. Privacy, like many other concepts tied to the context. So far, the dominant view is such that if the data is available to the public, they are available for use. But there is a difference between a shared and available data at any time for any purpose. So far, the use of public domain located in the data is allowed, but this will change soon. Or change the moral norms. Now it is difficult to say with certainty what will happen before.
What To Do?
I tried to identify the main ways to overcome the problems described above. End of specialized analysts. Analytical skills has too few experts who make important decisions. As a result, the personal opinion of one person can be critical for the course of the entire company. Instead of hiring experts and pay them, you must cultivate your inner analytical staff. Ideally, even from people who have the skills and analytical thinking, but not consisting mainly staff of analysts – they can express an independent opinion without fear of making weight and can often look at the problem from an unexpected quarter. IT – more and less than T. Technological side of IT in the enterprise is important, but should not obscure the information. Until now, IT depends and rests on specific people and personalities. Understanding (and often – guessing) the needs of other departments are also served by the IT department of the company is not an easy task, and it should be handled by experts to be clear about the entire IT enterprise structure. Systems thinking and teamwork skills – these are the traits that are often not enough. In order to analyze the information, it should be well structured. However, many organizations are collecting the data are not engaged in their structuring. It’s like if the library books lying side by side, not cataloged. Data structures allow efficient data analysis and quickly find the desired information.
The analysis should be used in conjunction with the simulation. As practice shows, often opportunities purely analytical algorithms do not suffice. It’s easy to explain – analyzing, we inevitably return to the past, and then try to extrapolate the results obtained in the future. Practice shows that it is not an effective method in any case, little effective separately from others. Systems theory allow us to understand the general laws of behavior of the system at any time, but on the basis of these laws, there are based models that are corrected using extrapolated data. This combined analysis method is much more effective than each of them individually.
It has always been common sense in the IT market that managers of large companies have conservative characteristics, and no wonder, after all for a good part of companies to the area of technology impacts directly on your bill.
Despite not knowing how to quantify, I have observed in recent years that one of the greatest fears of CIOs today is to be the head of managing a data center. I believe that many sleep praying for that day shall not come. And they’re right. Is there anything more risky for the IT manager than having to put in place a project of this responsibility? The core business of the whole company is present in that data center. Any error not anticipated, and mean the loss of revenue can also compromise the CIO career.
Even so, investments are needed with the growing number of fans consumers to the digital world. The companies have set up channels of communication with them, invested in audience data storage and essential information for business development. Cloud computing, time theme in the technology segment represents a large portion of the need for investment in Data Center.
To get an idea, lets take a look at research done for understanding the changes. According to the survey, these high investments of building data centers are leading many companies to outsource their data center. Further, in turn, perform a movement to an area that includes a new infrastructure.
The important thing is to remember that the service moves depends exclusively on planning and risk analysis. The times vary according to the company’s current situation, however, to make a successful drive in 48 hours for a large company, you need about two months of planning.
The planning process involves conducting a survey of all technology requirements and infrastructure in the customer base, developing an inventory and analysis of environmental characteristics. With this documentation in hand, the team can analyze all the risks of the procedure and establish what will be the necessary approach. This is the most complex part because of which it takes long period of two months, if something is wrongly planned, the action can be compromised and cause a long downtime in the company.
The project moves in data center is one of the riskiest in the IT industry and two important tips for the CIO overcome fear are:
1) Talk to those who have already made moves to know which path to tread;
2) Make sure that the supplier is sufficient and has the necessary experience and can perform this action successfully.
After that, when planning and risk analysis are well made, even a child can perform successfully action.
The same process that leads to business scanning depth also helps to speed up the automation of IT management processes.
Thus, in the digital environment, employees, customers and suppliers interact in increasingly standardized way through developed business applications exactly to ensure this uniformity of processes which is the basis and the springboard for automation.
But if on the one hand, scan means “automate something”, on the other, mass computerization promotes the multiplication of users and applications in information networks, which leads to a large decentralization of processes.
With decentralization, comes the hard need to meet the increasing masses of users, applications and multiple modes of devices such as desktop terminals, smartphones, POS or even code readers in the warehouse or factory floor.
Not to slow the flow of services in this new multifaceted demand environment, it has become necessary to free users to access countless applications and other download facilities, which are always sources of clutter and safety hazard, but without which the end user often reduces its efficiency.
In other words, the new environment of diversity that hit data networks has increasingly pressed the administrator to relativize the process of centralization levels. The question that arise is something like: How not to allow a desktop that install a free application, such that there are thousands of applications on the web, as this application is essential to the efficiency of specific user in everyday business life. As an example, we can mention numerous essential applications for tasks such as VoIP, CAD, virtual meetings, graphics editors etc.
And denying a senior executive free entry of the company in areas of the system that only qualified personnel, passwords holders of “admin” type should have right of access?
This need for release functions associated with the speed at which the data center grows, led to an unusual situation. According to studies, the internal user copy of businesses (and not necessarily the malicious user) is now the point of greatest concern and sense of vulnerability is by the CIO or the security manager.
The data from this survey, around 91% of data center managers believe that these internal users – especially those endowed with some higher access privileges, such as “admin” passwords or similar, are the most vulnerable point of their environments informational.
On the other hand, according to a study by Gartner, 92% of critical vulnerabilities are mitigated as much as possible simply by removing the “admin rights” from the users.
According to the survey, today, about 50% of employees hold some privilege password that allows certain basic actions (however compromising), such as re-configuring the safety profile of a desktop. In addition, 38% – a really significant number – are professional or non-technical executives, third the first step, which have sufficient access privileges so that they can play in highly critical system areas.
Compounding the picture, no less than 44% of community members are outsourced people (the official operator, technician software company, the installer of a switch or technical outsourcing company) receiving temporary privileges and for a almost always justifiable reason, can get to know the most intimate parts of the data center.
The IT industry is currently more evolving, this congenital disease of granting privileges is paradoxically today, the most obvious reason is the need for more automation, since the proliferation of “admins” happens exactly by the granting of tasks to individuals in its sole discretion, define actions that do not necessarily take into account the safety and vulnerability of systems.
So much so that, for 40% of data center managers, the lack of internal users mainly weakens compliance policies, which demonstrates the incompatibility between the standardization of processes and excess privileges over data networks and applications business.
But what are the feasible models for automation on the high complexity of the transaction environments, with its multiplicity of entities, codes, people, processes and applications?
Just as an example, in large companies, there are desktop application for each employee, and many are not cataloged. How to manage this overwhelming amount of applications, not thinking of automation and without beacon productivity with security?
One of the bases of such a process is the strict control of the investments (classifying them as “authorized applications”, “not allowed”, or simply “not classified” – the GreyLists) and allying such control to admin rights management.
The assumption is that, through corporate policies, you can set and define which applications can be installed and executed, without locking the user access profile, but ensuring that their actions are handled (what he can or cannot do) as well as audited and transparent, in order to gauge the productivity and safety.
This approach also suggests that investments and measures aimed at achieving and maintaining compliance start to converge immediately with automation policies, which will generate double beneficial.
First, investments in automation will find immediate justification on a budget matrix which are already active and assimilated, which are requirements of regulatory frameworks of industry, governments and markets.
Second, because the creation and operation of strict controls are required by compliance, will no longer result in rising labor costs (in addition to speed up the process as a whole), replacing this variant of control spending by automation. With this, there will be a containment of accumulation of new assets and a lower future investment warranty and ensure continued compliance.
This gradual elimination of the human factor in information processes, which are becoming faster and more complex is, ultimately, the starting point and at the same time, the finish line for the model of “compliance on automation”.
According to experts, in just a few years, Big Data technologies have gone from loud promises to one of the main driving forces behind the new digital era. In 2014, we increasingly saw how companies implemented projects more in big data from the test phase and moved their manufacturing operation.
This year, the organization will move from batch processing of big data to use them in real time. The current industry leaders and people who claim to be leaders in the near future, have made significant progress in the integration of new big data platforms in analytical systems that can process the information on the fly and respond quickly to changes in the business.
According to the research, 2015 will be dominated by five major trends.
1. Quick Update Of Data
The need for rapid updating of data is one of the main sources of Big Data technologies. Processes in legacy databases and data warehouses is slow, they are not flexible enough and are not satisfied with the business. In 2015, the speed of updating the data in organizations will be given more attention, but the emphasis will shift from data collection and data management to more active use of them.
Legacy databases and data warehouses are expensive, and solve the problems of generalization and structuring of data is necessary in attracting skilled database administrators. The need to involve database administrators leads to delays in access to new sources of data and the establishment of rigid structures that are difficult to change. Legacy databases are not flexible enough to meet the needs of most organizations today. Initial drafts of Big Data focused on the creation of storage for specific data sources.
Instead of increasing the volume of data at their disposal, the company will no longer engage in an assessment of their relevance and increasing efficiency of obtaining the required information. What capabilities of data analysis and operations are associated with their treatment, that organization has? How quickly information is updated when changing customer preferences, market conditions, competitive moves and features of operations? Answers to these questions will depend on the volume of investment and scale of projects in Big Data in 2015.
2. The Transition From The Lakes To The Data Processing Platforms
To a certain extent in 2014 the year of hubs, lakes or data (data lake) – repositories were considered, where the raw data was stored in the original format: structured, unstructured or semi-structured, ready for use. Value is determined by the presence of these lakes scalable infrastructure, highly efficient from an economic point of view due to the low cost of storing terabytes of data, and the ability to respond quickly to changing situations.
In 2015, the lake of data will continue to evolve. There will be new technologies that will accelerate the processing of data stored and perform various operations on them. This will not only improve the efficiency but also creates a single point of control, and a single point of security.
In 2015, the lake of data will be improved as we move from batch processing to real-time integration and file resources, Hadoop and database platform in large-scale processing. In other words, we are not talking about creating a large-scale data storage lakes that support complex queries and large reports, and on ensuring continuous access to the event processing and real-time data in order to promptly obtain the latest information and instantly take the necessary measures.
3. Self Service
Continuous improvement tools and Big Data services means that in 2015 the technical means will no longer be a bottleneck when accessing business users and analysts to the information they need.
In 2015, the technology will be introduced to enable business users to access the data of interest to them. Self-service systems to help developers and analysts to study the data directly. Before the creation of centralized data structures thought possible only with the participation of IT services. It was a very long and expensive process. Hadoop platform has allowed businesses in some scenarios to get easy access to the data structures in the performance of read operations. Leading organizations will implement data binding in the normal course of business and be able to receive information from a centralized structure. Self-service of this kind will help them use the new data sources and respond to emerging opportunities and emerging threats.
4. Consolidation of Hadoop Suppliers And New Business Models
In early 2013, Intel introduced its own distribution Hadoop, different from all others in that it is supported directly in Intel hardware. But a year later the corporation abandoned this idea and began to support the distribution in Cloud.
At the same time, Intel said that customers prefer to sit on the fence, watching how the market will develop Hadoop. Many different options offered by the suppliers, users resulted in confusion. In 2015 the consolidation of suppliers will continue Hadoop. Many will refuse to own distributions and try to focus on something else.
For 20 years, we use free software with open source software, which today represents great value for the market. Technology is improving gradually. Technological life cycle begins with the appearance of innovative ideas and create products, fundamentally different from the others, and ends when these products have completely lose their individuality. Edgar F. Codd invented the concept of relational databases in 1969. In 1986, the development of this innovative idea has turned into a public company Oracle, and conversion to mass production can be considered as the first release of MySQL version in 1995.
For database technology path from an innovative idea to mass product took 26 years. Hadoop was just entering a period of technological maturity. Since the publication of the first materials to Google MapReduce ten years ago. With the global spread of Hadoop we encountered in 10 years after the original concept. But Hadoop is still in the innovation phase, and offers vendors rashly adopting Red Hat for Hadoop, gradually withdraw from the market. This has already happened with Intel, and soon followed the example, Pivotal.
In 2015 we will see the evolution of a new, augmented by new nuances model software open source, in which deep innovations will be combined with the development of the forces of the community.
It is the community of software developers, open source play a key role in shaping the standards and seeking consensus. Competition has accelerated the transformation of the Hadoop processor batch analysis in a fully functional data processing platform.
5. From The More Advertising To Big With This
In 2015, the architects of corporate systems from a better understanding of Hadoop technology stack will move on to a more clear and specific definition of requirements for Big Data applications, including requirements for preparedness and business continuity.
If an organization is going as quickly as possible to move from experimentation to a serious introduction to the data center, it is necessary that the architects of corporate systems are in the forefront of the movement of Big Data. IT leaders have an essential role in determining the basic architectures, taking into account the requirements that apply to service-level agreements, the need to ensure high availability, business continuity and meet the critical needs of the enterprise. In 2014, the boom of Hadoop ecosystem marked by the proliferation of new applications, tools and components. In 2015, the market will focus on the differences between the platforms and architecture, which is necessary for the integration of Hadoop in the data center and achieve the desired results for the business.
Big data does not generate value for you. The generation of value is when we create insights that generate tangible results for the business. However, creating big data projects do not constitute simple tasks. There are many technologies, but the challenge of integrating a very diverse collection of structured and unstructured data is not trivial. The complexity of the work is directly proportional to the variety and volume of data that must be accessed and analyzed.
A possible alternative to this challenge is the creation of data lakes, which is a repository where it stores a large and varied amount of structured and unstructured data. The massive, easily accessible repository built on date (Relatively) inexpensive computer hardware is storing “big data”. Unlike data marts, Which are optimized for data analysis by storing only some attributes and dropping below the level aggregation date, the data lake is designed to retain all attributes, so especially When You do not know what is the scope of data or its use will be.
It is a new terminology, so there is no consensus as to its name. Some call data hub. We adopt the date lake which is most used term.
With a data lake, different data is accessed and stored in its original form and there we can directly seek correlations and insights, as well as generate the traditional data warehouse (DW) to handle structured data. Data Lake data models (or schemas) are not up-front, but emerge as we work with the data itself. Recalling that in the relational DW, the data model or schema must be previously defined. Data lake, the concept is one of “late binding” or “read schema” when the schema is built on the query time. Comes at a good time because the traditional data warehouse model has existed for some 30 years, almost unchanged. It has always been based on modeling called third normal form and that implies a single view of the truth. It worked and works well in many cases, but with the concept of big data and with increasing volumes and varieties (often unstructured) and the need to be flexible to do unplanned questions, the DW model clearly shows its limitations. It was not designed for today’s world.
For simplicity, a data lake can be imagined as a huge grid, with billions of rows and columns. But unlike a structured sheet, each cell of the grid may contain a different data. Thus, a cell can contain a document, another photograph and other cell can contain a paragraph or a single word of a text. Another contains a tweet or a post on Facebook… No matter where the data came from. It will just be stored in a cell. In other words, data lake is unstructured data warehousing where data from multiple sources are stored.
An innovative aspect of the concept is that, not having the need to define models previously eliminated much of the time spent on data preparation, as required in the current model of data warehouse or data center. Some estimates we spend on average about 80% of the time preparing data and only 20% analyzing. Significantly reduce the preparation time, we will focus on the analysis, which is what, in fact, creates value. How data is stored in its original form without going through previous formatting can be analyzed under different contexts. They are no longer limited to a single data model. In practice, is the model that companies like Google, Bing and Yahoo use to store and search huge and varied amounts of data. And before you ask, the technology that supports the data lake concept is Hadoop. The data lake architecture is simple: one HDFS (Hadoop File System) with a lot of directories and files.
The concept of a new data lake is not only the technology of a large repository but it is a model that proposes a new data ecosystem. We can think of no more restrictions in data warehouses and data minings where data models are already pre-defined, limited in the scope of possible questions. As all the data is available in date lake, we can make innovative intersections between data that may at first glance, not make sense. But an insight leads to a new question, which brings us to another insight and thus create new knowledge and generate value. Another advantage over traditional data warehouses is the ability to work in a much more simplified way with unstructured data.
Secret of the data lake is the concept of metadata (data about data). Each data entered, or as some say, ingested, the lake has a metadata to identify you and facilitate its location and further analysis. How to do this? Placing multiple tags on each die, so that we can locate all data from a given set of tags. A tagging concept advantage is that new data, new sources, can be inserted and once “tagged” shall be connected to the already stored data. No need of restructuring and redesigning of data models.
A resulting date lake enables users to make their searches directly without the need of IT sector interventions. This remains responsible for the security of data stored, but can leave business users, who understand the business itself, the task of generating insights and new thinking questions. Again, an analogy with Google. You make your own searches, no need to ask anyone to support or write them for you.
How to put data lake in a place? The first step is to build a repository where the data are stored without modification of tags. The second stage is the one that creates value and is what is called distillation of the data, where information is extracted and analyzed. But it needs to be done with some care…
At first glance, the data lake may looks like a pile of data which is out of control. An effective governance process, involving security, access control and adherence to compliance standards is required. Also, because it is still a new concept (although tags and Hadoop are not as new as well), is surrounded by hypes. The best practice is to put all the data in one place, and let the users on their account, make searches and correlations, generating insights. But we all know that between theory and practice there is a huge gap. So, before plunging into the lake, study the matter further and clearly define strategy for data lake and if it makes sense for your company, go for it.
After all, new duck does not dives deep into the lake…
Few technologies have had throughout history such a rapid acceptance among enterprises, including small and medium-sized (SMEs), as cloud computing. This model, which allows the use of different kinds of software as a service, is increasingly adopting facilitating innovation in companies and at the same time, enabling them to be more agile.
Cloud computing drives innovative attitudes within a company and, in some cases, can be considered as a first step to be a beneficial organizational culture of innovation. Companies of all sizes are increasingly taking business processes to the cloud.
This is because the new generation of business applications that run on cloud brings with it not only the flexibility needed by business lines (purchasing, HR, marketing, sales, etc.) to operate and turn, but also the integration that is necessary to automate and connect all the organization’s processes with each other. The cloud solutions boost efficiency, reduce costs and accelerate the adoption of technology trends such as mobility, data analysis (including real time) and big data.
Investing in a cloud solution adds new values to companies that can operate in real time, sharing information efficiently and securely between company employees. The agility and the ability to access real-time information are not just that cloud computing can provide.
Working in conjunction with other strategies, the use of cloud software brings the short and long term benefits such as greater analytical potential and agility in business application databases to add, compare and evaluate several possible scenarios and information within the company; access via mobile devices to detect changes, and share information with employees without thinking about their location.
The cost is also an important benefit that the cloud offers to the companies. Cloud computing allows companies to incorporate the latest innovations in the field of information technology without having to bear the extremely high software license acquisition costs and not to the implementation of an infrastructure (systems and hardware) specific to rotate applications. In this software acquisition system, are only subject to payment of a monthly fee for the specific applications being used and that are hosted on the cloud server.
Other Advantages: The solutions running in the cloud tend to be flexible enough to be configured according to the demands of each company in a very reasonable time. In addition, the cloud software interfaces are often similar to applications that people use every day on their smartphones or tablets. Therefore, no need to train staff to use the tool.
Finally, I highlight the cloud breaks a paradigm related to consumption by SMEs technology. The technology allows these companies to really focus on the conduct of its business and not worry about the acquisition and management of IT. It is also a pioneer in giving access to the latest innovations without having to fiercely consume budgets. So now that the market is more competitive than ever, SMBs can better compete with large companies.
You just have to add cloud in your business. What was once trend now shows a key tool for the growth of any organization.
What you’ll do? Wait a little longer and run the risk of leaving your competitors in front or anticipate them to be more agile, innovative and competitive?
After using the services like shared or VPS Hosting, when you recognize that requirements of your website are increasing, you start thinking about going for a dedicated server. But, when you think about few of the important factors like Cost, Server Management and Maintenance concerns that restricts you, you do not choose dedicated server hosting.
Handling a web server by your own needs a lot of technical knowledge and skills, hence before you decide to opt for a dedicated server ensure that you have enough technical knowledge or at least a staff to handle the server issues.
Outsourcing the server management tasks may cost too much to you. As an alternative, you can choose a managed dedicated server hosting package. The website hosting provider will ensure that few of the server management services are properly managed and monitored by them.
Even if you think that going for a managed hosting is still not affordable for you, then why not collocate your server in a world class data center and forget worrying about it?
Nowadays, to get rid of server management tasks organizations are using the colocation services for the ease. It is just like keeping your own server in the premises of a data center service provider and allow them to monitor it for you at a low cost. This service is known as Colocation Services.
It is much similar to the managed hosting, where some of the aspects of your web hosting is managed and monitored hourly. Here, you are the one who determine the level of the server management you need. By choosing the management levels you can reduce the setup and maintenance costs.
There are a lot of data center service providers that offers a different colocation service where a server is provided you on rent. Selecting the colocation services for your business is a great way to reduce the downtimes and gain maximum availability of your website. According to the management level, the provider takes care of monitoring and trouble shooting of your server.
The web hosting company providing the colocation service has a well-equipped data center that ensures you to get a high level security for your server. All the factors mentioned above makes colocation services a better sell for organizations, freeing them from the burden of managing their own servers.
The question that arises in the light of detailed analysis of DCIM-making – how such systems can bring the most benefit to companies? DCIM first to the West and India have expressed interest in operators of commercial data centers. And it continues to rise, especially in view of the changing needs of the market. Large companies are gradually increasing confidence in the colocation-providers, they are increasingly willing to place their servers, business applications, critical data, and etc on leased sites.
The customers and their requirements are different, sometimes conflicting. The task of the operator of the data center – to comply with the technical conditions of use of heterogeneous hardware, the composition of which also may vary. DCIM sharpens its focus on asset management, documentation, audits. On the one hand, cloud virtualization allows flexibility to reallocate the load on computing resources. On the other – need DCIM operative management system for engineering infrastructure.
If we talk about the corporate segment, the most interested in the DCIM are IT-oriented companies, such as telecom operators and large banks. Usually they are geographically dispersed organizations that have large-scale IT infrastructure, combines several or even dozens of data centers. Adding to this, the accumulated or had inherited after the merger / acquisitions problem. In such a situation, DCIM allows you to control the cost of ownership and significantly reduce the risks when making changes to the IT infrastructure, and this applies to both, the daily operation and planning of potential investments.
Today, our industry is moving towards cloud data centers, implying that the IT resources are provided as a service and infrastructure load change very rapidly. While the number of such data centers in India is small, but this trend will soon become universal. Creating a DCIM-system, takes you to the dynamic load balancing.
So, geographically dispersed companies need to conserve energy on priority. It can control the load in the network data center. There are regions where electricity is cheaper, there are day and night rates, and taking into account the time zone difference, could carry the most energy-intensive load to other data centers. These savings can be quite substantial.
It is necessary to say a few words about the cost of the DCIM-making and its payback period. If the company is planning to purchase a license for the core modules and resource management changes, implement basic integration with external systems of DCIM, the same Configuration Management Database. To fill the database, it is necessary to examine the data center in detail, create and implement regulations, to train staff, develop reports, etc.
Note that the tools employed depends on the objectives and conditions of a specific project: For someone, to have the cable magazines could be more important, the other – the power supply circuit; some have a plurality of primary systems that can serve as sources of data for DCIM, others almost enter all the information manually. No single recipe, but we have a great instrumental and technical base and can choose the most appropriate means.
Assessing the situation in the data center, we are working with the company that defines its segment, where the use of DCIM give the most benefits. For a realistic assessment of this effect, as well as the convenience of the proposed tools we can create a pilot zone. Its deployment goes from 1 to 3 months, 3 to 6 months and still need to assess all the possibilities of integrated management of the data center.
Summarizing, we note another trend is gaining popularity in the corporate world. Virtually all, regardless of which sector of market they belong to, have already switched to the cloud computing architecture, started (or are about to start) to implement virtual environments. Therefore, companies today should consider the use of management tools for engineering as a part of the data center as they relate to the management of virtual computing environment.
In this article, we are going to see some specific areas, where the server market can grow rapidly.
According to Gartner, there are three particular areas, where the server market can grow hugely by 2015. They are:
Data Centers in Hyperscale
According to a study done by an IT Research Company “Gartner”, data centers in each hyperscale will be one of the most promising areas for the server market to grow. Gartner states that, the sales of vendors will boost in the coming years. According to him, this area accounts for about 11% of the server sales. It also says that the x86 server market will reach up to 17% by the year 2015. Server providers are also offering support, installation, plan the manufacturing date of the items and provide customer designs. The main reason behind providing all these things is to highlight the company in this competitive market.
The model of desktop virtualization is the hottest trend and another area that have shown significant growth in the market. The operational efficiency and business continuity plan are among the companies interests attracting them towards the desktop virtualization. According to the Gartner’s recommendation, server vendors should not only look for partnerships with small and medium sized corporations, but also with large companies and government organizations.
Companies are now taking initiatives in utilizing the server with low power consumption. This need for companies will encourage and increase the opportunities between IT vendors. Gartner also states that, the 2.5% of the x86 server market will yield space to data centers equipped with the best technologies for power management. The IT Research company noted that the web hosting servers with low power consumption still depends on the recent technology and research and demand investment.