data center


The formulas to turn enormous amounts of data into information with economic value become the great asset of the multinationals.

Algorithms are a set of programming instructions that, logically introduced in software, allow to analyze a set of previously selected data and establish an “output” or solution. These algorithms are being used by companies mainly to detect patterns or trends, and based on this, generate useful data to adapt their products or services better.

It is not a novelty for companies to obtain data from advanced analytics to study the characteristics of the product they plan to put on the market; the price to which it wants to place it or even private decisions as sensitive as the remuneration policy for its employees. The surprising thing is the dimension.

It is not only that the number of data in circulation has recently multiplied to volumes that are difficult to imagine – it is estimated that humanity has generated 90% of the information of the whole history in the last five years. The possibilities of interconnecting them have also grown dramatically.

Algorithm revolution

This revolution has contributed to each of the millions of people who give their data every day for free and continuously, either uploading a photo to Facebook, buying with a credit card or going through the metro turnstiles with a magnetic card.

In the heat of giants like Facebook and Google, who base their enormous power on the combination of data and algorithms, more and more companies are investing increasing amounts of money in everything related to big data. It is the case of BBVA, whose bet is aimed both at invisible projects for customers -as the engines that allow processing more information to analyze the needs of its users- and at other easily identifiable initiatives, such as the one that enables bank customers to. Forecast the situation of your finances at the end of the month.

Dangers and Risks

The vast possibilities offered by the algorithms are not without risks. The dangers are many: they range from cybersecurity – to deal with hacking or theft of formulas – to the privacy of the users, going through the possible biases of the machines.

Thus, a recent study by the University Carlos III concluded that Facebook uses advertising for sensitive data of 25% of European citizens, who get tagged in the social network according to matters as private as their political ideology, sexual orientation, religion, ethnicity or health.
Cybersecurity, for its part, has become the primary concern of investors around the world: 41% said they were “apprehensive” about this issue, according to the Global Investors Survey of 2018.

What is the future of the algorithms?

This technology is fully functional to meet the objectives of almost any organization today, and although we do not know, is present in many well-known firms in the market. Its capabilities of analysis, prediction and report generation for decision making make it a powerful strategic tool.

Algorithms, either through specific applications or with the help of Business Intelligence or Big Data solutions open the way to take advantage of the information available in our company and turn it into business opportunities.

Thanks to the algorithms we know better how our clients and prospects behave, what they need, what they expect from us. And they also allow us to anticipate the actions of our competitors and market trends.

Like any technological innovation that has revolutionized our way of understanding the world since man is a man, it will take us some time to become aware of this new reality and learn to make the most of it. As citizens and as communicators we can turn algorithms into valuable allies.

The algorithm is at the heart of technologies potentially as powerful as artificial intelligence. Nowadays, algorithms are the basis of machine learning technologies, which surprise us every day with new skills. And it is behind techniques of the setting of virtual assistants or autonomous vehicles.

A programming language is an artificial language designed to express computations that can be carried out by machines such as computers. They can be used to create programs that control the physical and logical behavior of a device, to express algorithms with precision, or as a mode of human communication.

Is formed of a set of symbols and syntactic and semantic rules that define its structure and the meaning of its elements and expressions. The process by which you write, test, debug, compile and maintain the source code of a computer program is called programming.

Also, the word programming gets defined as the process of creating a computer program, through the application of logical procedures, through the following steps:

  • The logical development of the program to solve a particular problem.
  • Writing the logic of the program using a specific programming language (program coding).
  • Assembly or compilation of the program until it becomes a machine language.
  • Testing and debugging the program.
  • Development of documentation.

There is a common error that treats the terms ‘programming language’ and ‘computer language’ by synonyms. Computer languages encompass programming languages and others, such as HTML. (language for the marking of web pages that is not properly a programming language but a set of instructions that allow designing the content and text of the documents)

It allows you to specify precisely what data a computer should operate, how it should be stored or transmitted, and what actions to take under a variety of circumstances. All this, through a language that tries to be relatively close to human or natural language, as is the case with the Lexicon language. A relevant characteristic of programming languages is precisely that more than one programmer can use a common set of instructions that are understood among them to carry out the construction of the program collaboratively.

The implementation of a language is what provides a way to run a program for a certain combination of software and hardware. There are basically two ways to implement a language: Compilation and interpretation. Compilation is the translation into a code that the machine can use. The translators that can perform this operation are called compilers. These, like advanced assembly programs, can generate many lines of machine code for each proposal of the source program.

Imperative and functional languages

The programming languages ​​are generally divided into two main groups based on the processing of their commands:

  • Imperative languages
  • Functional languages.

Imperative programming language

Through a series of commands, grouped into blocks and composed of conditional orders, it allows the program to return to a block of commands All this if the conditions get met. These were the first programming languages ​​in use, and even today many modern languages ​​use this principle.

However, structured imperative languages ​​lack flexibility due to the sequentiality of instructions.

Functional programming language

A functional programming language (often called procedural language) is a language that creates programs employing functions, returns a new result state and receives as input the result of other purposes. When a task invokes itself, we talk about recursion.

The programming languages ​​can, in general, get divided into two categories:

  • Interpreted languages
  • Compiled languages

Interpreted language

A programming language is, by definition, different from the machine language. Therefore, it must get translated so that the processor can understand it. A program written in an interpreted language requires an auxiliary program (the interpreter), which converts the commands of the programs as necessary.

Compiled language

A program written in a “compiled” language gets translated through an attached program called a compiler that, in turn, creates a new independent file that does not need any other program to run itself. This file is called executable.

Also, it has the advantage of not needing an attached program to be executed once it has compiled. Also, since only one translation is necessary, the execution becomes faster.

The interpreted language, being directly a readable language, makes that any person can know the manufacturing secrets of a program and, in this way, copy its code or even modify it.


The implementation of a language is what provides a way to run a program for a certain combination of software and hardware. There are basically two ways to implement a language: Compilation and interpretation. Compilation is the translation into a code that the machine can use. The translators that can perform this operation are called compilers. These, like advanced assembly programs, can generate many lines of machine code for each proposal of the source program.


To write programs that provide the best results, a series of details must be taken into account.

  • Correction.  Programs are correct if they do what they should do as they got established in the phases before their development.
  • Clarity. It is essential that the program be as clear and legible as possible, to facilitate its development and subsequent maintenance. When developing a program, you should try to make its structure coherent and straightforward, as well as take care of the style in the edition; In this way, the work of the programmer is facilitated, both in the creation phase and in the subsequent steps of error correction, extensions, modifications, etc. Stages that can be carried out even by another programmer, with which clarity is even more necessary so that other programmers can continue the work efficiently.
  • Efficiency. The point is that the program does so by managing the resources it uses in the best possible way. Usually, when talking about the efficiency of a program, it is generally referred to the time it takes to perform the task for which it got created. And the amount of memory it needs, but other resources can also get considered when obtaining the efficiency of a program. It all depends on its nature (disk space it uses, network traffic it generates, etc.).
  • Portability. A program is portable when it can run on a platform, be it hardware or software, different from the one on which it got developed. Portability is a very desirable feature for a program, since it allows, for example, a program that has been designed for GNU / Linux systems to also run on the family of Windows operating systems. It will enable the program to reach more users more efficiently.

When they are not administered, the data can become overwhelming, which makes it difficult to obtain the information that is needed at the time. Fortunately, we have software tools that, although designed to address data storage effectively, discovery, compliance, etc., have as a general objective to make the management and maintenance of data easy.

What is structured data?

When we talk about structured data, we refer to the information usually found in most databases. They are text files usually displayed in rows and columns with titles. They are data that can be easily ordered and processed by all data mining tools. We could see it as if it were a perfectly organized filing cabinet where everything can get identified, labeled and easily accessible.

It is likely that most organizations are familiar with this type of data and are already using it effectively, so let’s move on to see the unstructured data.

What is unstructured data?

Although it seems incredible, the database with structured information of a company does not even contain half of the information that is available in the company ready to be used. 80% of the information relevant to a business originates in an unstructured form, mainly in text format.

Unstructured data is usually binary data that has no identifiable internal structure. It is a massive and disorganized conglomerate of several objects that have no value until identified and stored in an organized manner.

Once organized, the elements that make up their content can be searched and categorized (at least to some extent) to obtain information.

For example, although most data mining tools are not capable of analyzing the information contained in email messages (however organized they may be), it is possible that collecting and classifying the data contained in them can show us relevant information for our organization. It is an example that illustrates the importance and scope of unstructured data.

But e-mail has no structure?

The unstructured term faces different opinions for various reasons. Some people say that although a formal structure cannot get identified in them, it is possible that it could be implicit and, in that case, it should not get categorized as unstructured. However, on the other hand, if the data have some form of structure, but this is not useful and can not be used to process them, they should be categorized as unstructured.

Although e-mail messages may contain information with some implicit structure, it is logical to think of them as unstructured information, since common data mining tools are not prepared to process and analyze them.

Unstructured data types

Unstructured data is raw and unorganized data. Ideally, all this information could be converted into structured data. However, it would be somewhat expensive and would require a lot of time. In addition, not all types of unstructured data can easily be converted into a structured model. For example, following the e-mail example, an e-mail contains information such as the time of sending, the person to whom it is sent, the sender, etc. However, the content of the message is not easily divided or categorized and this can be a problem of compatibility with the structure of a relational database system.

This is a limited list of unstructured data types:

  • Emails.
  • Text processor files.
  • PDF files.
  • Spreadsheets.
  • Digital images
  • Video.
  • Audio.
  • Publications in social media.

Looking at that list, you could ask what these files have in common. These are files that can be stored and managed without the system having to understand the format of the data. Since the content of these files does not get organized, they can get stored in an unstructured way.

Precisely many qualified voices in the sector suggest that it is unstructured information that offers greater knowledge. In any case, the analysis of data of different types is essential to improve both productivity and decision making in any company.

The Big Data industry continues to grow, but there is a problem with unstructured data that do not get used yet. However, the companies have already identified the problem and technologies and services are already being developed to help solve it.

A database performance monitoring and management tools can be used to mitigate problems and help organizations to be more proactive so that they can avoid performance problems and interruptions.

Even the best-designed database experiences degradation of performance. No matter how well the database structures are defined or the SQL code gets written, things can and will go wrong. And if the performance problems are not corrected quickly, that can be detrimental to the profitability of a company.

Performance of a Database

When the performance of the database suffers, business processes within organizations slow down and end users complain. But that is not the worst of all. If the performance of the systems they see abroad is bad enough, companies can lose business, as customers who are tired of waiting for the applications to respond will go elsewhere.

Because the performance of database systems and applications can be affected by a variety of factors, the tools that can find and correct the causes of database performance problems are vital for organizations that rely on them in database management systems (DBMS) to run your mission-critical systems. And in today’s IT world, focused on databases, that applies to most companies.

Types of performance problems you should look for

Many types of database performance problems can make it difficult to locate the cause of individual problems. It is possible, for example, that the database structures or the application code are flawed from the beginning. Bad database design decisions and incorrectly encoded SQL statements can result in poor performance.

It may be that a system was well designed initially, but over time the changes caused the performance to begin to degrade. More data, more users or different patterns of data access can slow down even the best database applications. Even the maintenance of a DBMS – or the lack of regular maintenance of databases – can cause performance to plummet.

The following are three important indicators that could indicate database performance issues in your IT department:

1. Applications that go slower. The most important indication of potential performance problems in the database is when things that used to run fast start running at a slower pace. Including online transaction processing systems that are used by employees or customers, or batch jobs that process data in large quantities for tasks such as payroll processing and end-of-month reports.

Monitoring a processing workload without database performance management tools can become difficult. In that case, database administrators (DBAs) and performance analysts have to resort to other methods to detect problems, in particular, complaints from end users about issues such as application screens taking too much time to upload or nothing to happen for a long time after the information is entered into an application.

2. System interruptions. When a system is turned off, the performance of the database is obviously at its worst. Interruptions can be caused by database problems, such as running out of storage space due to increased volumes of data or by a resource that is not available, such as a data set, partition or package.

3. The need for frequent hardware updates. The constantly upgrading of servers to larger models with more memory and storage are often candidates for database performance optimization. Optimizing database parameters, tuning SQL statements and reorganizing database objects can be much less expensive than frequently updating expensive hardware and equipment.

On the other hand, sometimes hardware updates are needed to solve database performance problems. However, with the proper tools for monitoring and managing databases, it is possible to mitigate the costs of updating by locating the cause of the problem and identifying the appropriate measures to remedy it. For example, it may be cost-effective to add more memory or implement faster storage devices to resolve I / O bottlenecks that affect the performance of a database. And doing so will probably be cheaper than replacing an entire server.

Problems that tools can help you manage

When the performance problems of the database arise, it is unlikely that its exact cause will be immediately evident. A DBA should translate vague complaints about end-user issues into specific issues, related to performance, that can cause the problems described. It can be a difficult and error-prone process, especially without automated tools to guide the DBA.

The ability to collect the metrics on database usage and identify the specific problems of the database – how and when they occur – is perhaps the most compelling capability of the database performance tools. When faced with a performance complaint, the DBA can use a tool to highlight current and past critical conditions. Instead of having to look for the root cause of the problem manually, the software can quickly examine the database and diagnose possible problems.

Some, database performance tools can be used to set performance that, once triggered, alert the DBA of a problem or trigger an indicator on the screen. Also, DBAs can schedule reports on database performance to be executed at regular intervals, in an effort to identify the problems that need to be addressed. Advanced tools can both identify, and help solve any situations.

There are multiple variations of performance issues, and advanced performance management tools require a set of functionalities.

The critical capabilities provided by the database performance tools include

  • Performance review and SQL optimization.
  • Analysis of the effectiveness of existing indexes for SQL.
  • Display of storage space and disk defragmentation when necessary.
  • Observation and administration of the use of system resources.
  • Simulation of production in a test environment.
  • Analysis of the root cause of the performance problems of the databases.

The tools that monitor and manage the performance of databases are crucial components of an infrastructure that allows organizations to effectively deliver the service to their customers and end users.

When we talk about measurement, we must understand how knowledge differs from data and information.

In an informal conversation, the three terms get often used interchangeably, and this can lead to a free interpretation of the concept of knowledge. Perhaps the simplest way to differentiate the words is to think that the data get located in the world and experience is located in agents of any type, while the information adopts a mediating role between them.

An agent does not equal a human being. It could be an animal, a machine or an organization constituted by other agents in turn.


A data is a discrete set of objective factors about a real event. Within a business context, the concept of data gets defined as a transaction log. A datum does not say anything about the way of things, and by itself has little or no relevance or purpose. Current organizations usually store data through the use of technologies.

From a quantitative point of view, companies evaluate the management of data regarding cost, speed, and capacity. All organizations need data, and some sectors are dependent on them. Banks, insurance companies, government agencies, and Social Security are obvious examples. In this type of organizations, good data management is essential for their operation, since they operate with millions of daily transactions. But in general, for most companies having a lot of data is not always right.

Organizations store nonsense data. This attitude does not make sense for two reasons. The first is that too much data makes it more complicated to identify those that are relevant. Second, is that the data have no meaning in themselves. The data describe only a part of what happens in reality and do not provide value judgments or interpretations, and therefore are not indicative of the action. The decision making will get based on data, but they will never say what to do. The data does not say anything about what is essential or not. In spite of everything, the info is vital for the organizations, since they are the base for the creation of information.


As many researchers who have studied the concept of information have, we will describe it as a message, usually in the form of a document or some audible or visible communication. Like any message, it has an emitter and a receiver. The information can change the way in which the receiver perceives something, can impact their value judgments and behaviors. It has to inform; they are data that make the difference. The word “inform” means originally “shape” and the information can train the person who gets it, providing specific differences in its interior or exterior. Therefore, strictly speaking, it is the receiver, and not the sender, who decides whether the message he has received is information, that is if he informs him.

A report full of disconnected tables can get considered information by the one who writes it, but in turn, can be judged as “noise” by the one who receives it. Information moves around organizations through formal and informal networks. Formal networks have a visible and defined infrastructure: cables, e-mail boxes, addresses, and more. The messages that these networks provide include e-mail, package delivery service, and transmissions over the Internet. Informal networks are invisible.

They are made to measure. An example of this type of network is when someone sends you a note or a copy of an article with the acronym “FYI” (For Your Information). Unlike data, information has meaning. Not only can it potentially shape the recipient, but it is organized for some purpose. The data becomes information when its creator adds sense to it.

We transform data into information by adding value in several ways. There are several methods:

• Contextualizing: we know for what purpose the data were generated.

• Categorizing: we know the units of analysis of the main components of the data.

• Calculating: the data may have been analyzed mathematically or statistically.

• Correcting: errors have been removed from the data.

• Condensing: the data could be summarized more concisely. Computers can help us add value and transform data into information, but it is tough for us to help analyze the context of this information.

The widespread problem is to confuse information (or knowledge) with the technology that supports it. From television to the Internet, it is essential to keep in mind that the medium is not the message. What gets exchanged is more important than the means used to do it. Many times it is commented that having a phone does not guarantee to have brilliant conversations. In short, that we currently have access to more information technologies does not mean that we have improved our level of information.


Most people have the intuitive feeling that knowledge is something broader, deeper and more productive than data and information. We will try to make the first definition of knowledge that allows us to communicate what we mean when we talk about knowledge within organizations. For Davenport and Prusak (1999) education is a mixture of experience, values, information and “know-how” that serves as a framework for the incorporation of new skills and knowledge, and is useful for action. It originates and applies in the minds of connoisseurs. In organizations, it is often not only found in documents or data warehouses, but also organizational routines, processes, practices, and standards. What immediately makes the definition clear is that this knowledge is not pure. It is a mixture of several elements; it is a flow at the same time that it has a formalized structure; It is intuitive and challenging to grasp in words or to understand logically fully.

Knowledge exists within people, as part of human complexity and our unpredictability. Although we usually think of definite and concrete assets, knowledge assets are much harder to manage. Knowledge can be seen as a problem or as stock. Knowledge is derived from information, just as information gets derived from data. For information to become knowledge, people must do practically all the work.
This transformation occurs thanks to

• Comparison.

• Consequences.

• Connections.

• Conversation.

These knowledge creation activities take place within and between people. Just as we find data in registers, and information in messages, we can obtain knowledge from individuals, knowledge groups, or even in organizational routines.

Information and data are fundamental concepts in computer science. A data is nothing more than a symbolic representation of some situation or knowledge, without any semantic sense, describing circumstances and facts without transmitting any message.

While the information is a set of data, which are processed adequately so that in this way, they can provide a message that contributes to the decision making when solving a problem. Also to increasing knowledge, in the users who have access to this information.

The terms information and data may seem to mean the same; however, it is not. The main difference between this concept is that the data are symbols of different nature and the information is the set of these data that have gotten treated and organized.

Information and data are two different things, although related to each other.

The differences between both are the following:


  • They are symbolic representations.
  • By themselves, they have no meaning.
  • They can not transmit a message.
  • They are derived from the description of certain facts.
  • The data is usually used to compress information to facilitate the storage of data, and its transmission to other devices on the contrary that the report, which tends to be very extensive.


  • It is the union of data that has been processed and organized.
  • They have meaning.
  • You can transmit a message.
  • Increase knowledge of a situation.
  • The information or message is much higher than the data since the data gets integrated by a set of data of different types.
  • Another remarkable feature of the information is that it is a message that has communicational meaning and a social function. While the data does not reflect any word and usually is difficult to understand by itself for any human being, lacking utility if it is isolated or without other groups of data that create a consistent message.

The main difference gets centered on the message that the information can transmit, and that a data on its own cannot perform. A lot of info is needed to create a news or information. There is a difference between data and information, and that this difference is quite significant. Therefore, these terms should not be confused, especially within the computing and computer field, as well as, within the area of ​​communications.

For this to be information as such, you must meet these 3 requirements:

  • Be useful– What is the use of knowing that “The price of X share will rise by 10% in the next 24 hours” if I want to see the definition of Globalization?
  • Be reliable– What good is a piece of information, if we do not know if it is true, accurate or at least reliable? Not every part of the data will be correct, but at least it must be reliable. It could be making a decision based on the wrong information.

  • Be timely– What is the use of knowing that it rains in the United States if I live in Argentina? I am looking to see if it will rain in the afternoon in my country to know if I should go out with an umbrella or not.

What is data?

Data are symbolic representations of some entity, can be alphabetic letters, points, numbers, drawings, etc. The data unitarily have no meaning or semantic value, that is, they have no impact. But when correctly processed, they become meaningful information that helps make decisions. The data can be grouped and associated in a specific context and produce the data.

Classification of data

  • Qualitative– Are those that indicate qualities such as texture, color, experience, etc.
  • Continuous– These are data that are expressed in whole or complete numerical form.
  • Discrete– These data are expressed in fractions or using decimals.
  • Quantitative– Data that refers to the numerical characteristic, can be numbers, sizes, quantities.
  • Nominal– They includes data such as sex, academic career, qualifications. They can be assigned a number to process them statistically.
  • Hierarchized– They are those that throw subjective evaluations and are organized according to achievement or preference.

What is information?

Information is the grouping of data whose organization allows to convey a meaning. It will enable the uncertainty to decrease and the knowledge to increase. The info is elementary to solve problems because it provides everything necessary to make appropriate decisions.

In an organization, information is one of its most vital resources so that it lasts over time. For data to become information must be processed and organized, always fulfilling some characteristics, some exclusionary, others only important but may not be.

Characteristics of the information

  • Relevance– Must be relevant or important to generate and increase knowledge. The incorrect decision making is often due to the grouping of too many data, therefore the most important ones must be collected and grouped.
  • Accuracy– must have sufficient accuracy, taking into account the purpose for which it is needed.
  • Complete– All the information needed to solve a problem must be complete and available.
  • Reliable source– The information will be reliable as long as the source is reliable.
  • Deliver to the right person– The information must be given to whoever is entitled to receive it, only then can it fulfill its true objective.
  • Punctuality– The best information is the one that is communicated at the precise moment when it is needed and will be used.
  • Detail– You must have specific details so that this is effective.
  • Comprehension– If the information is not understood, it can be used and will not have any value for the recipient.

The process of transformation of data into information and knowledge

There are many instances from which one receives data until that data is a factual knowledge that we will enjoy benefits, and even one of those intermediate instances is information.

The process will vary depending on the sample (type, quantity, and quality of data) and depending on our objectives, but the process is somewhat similar to this:

  • Data – We receive a series of data, which may be few or many, may be useful or not, we still do not know.
  • The data are selected – Now we have to see them, one by one and we have to really see which ones are useful to us. Based on this we will have a list of selected data.
  • Pre Process – Now with that data selected, now perhaps only 20% of those that were original, we have to organize them to be able to enter them into some processing system.
  • Processed data – They are no longer just selected data, now they are organized and processor, now we are faced with a professed transformation of those data because we are looking for a result.
  • Transformed data – It is no longer raw data much less, and practically has the form of information and in fact, roughly we can find certain things that may get our attention.
  • Patterns – When we repeatedly have precise information and apply it to look for patterns, in some occasions that information can be useful, reliable and obviously timely, but nobody has the absolute truth; Some piece of information may have some error/deviation, however slight it may be.

Global warming, terrorism, DoS attacks (carried out on a computer system to prevent the access of its users to their resources), pandemics, earthquakes, viruses — all pose potential risks to your infrastructure. In the 2012 Global Disaster Recovery Index published by Acronis, 6,000 IT officials reported that natural disasters caused only 4% of service interruptions, while incidents in the servers’ installations (electrical problems, fires, and explosions) accounted for 38%. However, human errors, problematic updates, and viruses topped the list with 52%.

The 6 essential elements of a solid disaster recovery plan

Definition of the plan

To make a disaster recovery plan work, it has to involve management — those who are responsible for its coordination and ensure its effectiveness. Additionally, management must provide the necessary resources for the active development of the plan. To make sure every aspect is handled, all departments of the organization participate in the definition of the plan.


Next, the company must prepare a risk analysis, create a list of possible natural disasters or human errors, and classify them according to their probabilities. Once the list is completed, each department should analyze the possible consequences and the impact related to each type of disaster. This will serve as a reference to identify what needs to be included in the plan. A complete plan should consider a total loss of data and long-term events of more than one week.

Once the needs of each department have been defined, they are assigned a priority. This is crucial because no company has infinite resources. The processes and operations are analyzed to determine the maximum amount of time that the organization can survive without them. An order of recovery actions is established according to their degrees of importance.

In this stage, the most practical way to proceed in the event of a disaster is determined. All aspects of the organization are analyzed, including hardware, software, communications, files, databases, installations, etc. Alternatives considered vary depending on the function of the equipment and may include duplication of data centers, equipment and facility rental, storage contracts, and more. Likewise, the associated costs are analyzed.

In a survey of 95 companies conducted by the firm Sepaton in 2012, 41% of respondents reported that their DRP strategy consists of a data center configured active-passive, i.e., all information supported in a fully set data center with the critical information replicated at a remote site. 21% of the participants use an active-active configuration where all the company’s information is kept in two or more data centers. 18% said they still use backup tapes; while the remaining 20% ​​do not have or are not planning a strategy yet.

For VMware, virtualization represents a considerable advance when applied in the Disaster Recovery Plan (DRP). According to an Acronis survey, the main reasons why virtualization is adopted in a DRP are improved efficiency (24%), flexibility and speed of implementation (20%), and cost reduction (18%).

Essential components

Among the data and documents to be protected are lists, inventories, software and data backups, and any other important lists of materials and documentation. The creation of verification templates helps to simplify this process.

A summary of the plan must be supported by management. This document organizes the procedures, identifies the essential stages, eliminates redundancies and defines the working plan. The person or persons who write the plan should detail each procedure, and take into consideration the maintenance and updating of the plan as the business evolves.

Criteria and test procedures of the plan

Experience indicates that recovery plans must be tested in full at least once a year. The documentation must specify the procedures and the frequency with which the tests performed. The main reasons for testing the plan are verifying its validity and functionality, determining the compatibility of procedures and facilities, identifying areas that need changes, training employees, and demonstrating the organization’s ability to recover from a disaster.

After the tests, the plan must be updated. As suggested, the original test should be performed during hours that minimize disruption in operations. Once the functionality of the plan is demonstrated, additional tests should be done where all employees have virtual and remote access to these functions in the event of a disaster.

Final approval

After the plan has been tested and corrected, management must approve it. They’ll be in charge of establishing the policies, procedures, and responsibilities in case of contingency, and to update and give the approval to the plan annually. At the same time, it would be advisable to evaluate the contingency plans of external suppliers. Such an undertaking is no small feat, but has the potential to save any company when disaster strikes.

A data center is the place where the computing, storage, networking and virtualization technologies that are required to control the life cycle of the information generated and managed by a company are centralized.

It plays a fundamental role in the company operations, since data centers help them to be more efficient, productive and competitive. At the same time, they adjust to the new needs of the businesses and respond quickly to even the most demanding consumers.

Data centers have adapted to this new reality and have developed services, not only to store valuable information of a company, but also with the purpose of automating processes and guaranteeing that each enterprise takes advantage of 100% of their data.

How a data center can help your business

  • Higher productivity– By having a data center, companies can increase their agility and productivity by simplifying their administrative processes and obtaining flexible and scalable environments that meet each of their objectives. Most companies and individuals have to deal with problems related to the flow of their work, customer service, and information management on a daily basis. All these situations distract the management teams, impairing their ability to keep the boat afloat and focus on sales or product development.
  • Technological flexibility– Through data centers, companies can also obtain flexibility in their technical infrastructure, since part of their information can be migrated to the cloud, operated on internally, or given to a third party. It brings other benefits such as low operating costs, high levels of security, and confidentiality of their information.
  • Automatization– A data center can help automate your processes and services. Thanks to advances in artificial intelligence, now you can establish automated customer service channels and monitor the tasks of each area of ​​your company through project management platforms.
  • Physical security– A data center provides an efficient team to perform a series of activities, such as monitoring alarms (and in some cases, calling security agents for emergencies), unauthorized access, controlling access through identity confirmation of the collaborator, issuing reports, and answering telephone and radio calls.
  • Refrigeration and Energy– Excellent cooling and energy systems ensure the proper functioning of equipment and systems within a data center. Refrigeration plays the role of maintaining the temperature of the environment at the right levels so that everything operates in perfect condition. Generally, to avoid damage and problems with the power supply, the system as a whole has no-breaks and generators, in addition to being powered by more than one power substation. This ensures performance and efficiency — your business does not need to invest in either of these critical services, saving you a lot of money.  
  • Business visibility– Companies can have visibility into the traffic of their data centers, both physical and virtual, since they allow gathering business intelligence information, identifying trends and acting quickly and intelligently. This facilitates quick decision making.

You can try to establish your servers, with limited human resources and resources at hand, to protect all your know how, or you can trust an expert and ensure the computer security of your company and the welfare of your business — but a data center is always a good option. You get everything you need with an affordable price and all the features you would want.

Data centers must be designed with an appropriate infrastructure to support all the services and systems of the company, in such a way as to allow the perfect functioning of the center and foresee its future growth by adapting to emerging technologies.

Do not forget that the primary function of a data center is to provide technology services for the development of your operations and ensure the integrity and availability of your business information. So make sure your provider helps solve the needs of your company. In a world where information has become an invaluable asset, each company is tasked with making the best use of their data and protecting themselves.

It is said that data is the new oil of this era because it nourishes the economy in one and a thousand ways. Social networks, search engines, and e-commerce platforms use data to generate personalized ads; some companies use it to optimize processes and thus save money or to create products increasingly oriented to the needs of their customers.

The point is that currently this data is delivered for free every time a person registers on a platform, when using a browser and visiting a page that, through cookies, stores the user’s movements within the site. Telephone companies can also obtain lots of data because they know the location of users at any given time.

Even when a person goes out, and sensors or cameras capture the image or movements in the city, digital data is produced that is used to create solutions that could translate into money. It is how the big data universe works.

What would happen if companies could be charged for the use of that personal information?

Sometimes you let companies use your data, just by accepting privacy conditions without reading, downloading apps that need access to view your photos, allow a GPS to know at all times where we are, or storing images in a cloud, to name a few.

Aware of the growing value of information in the economy, more and more companies are emerging that try to treat people’s personal info with care as a differential value.

One solution would be to create a decentralized market of data so that users can appropriate their information and sell it safely and anonymously.

It is estimated that, at present, the data that a user passively generates annually just by browsing the web, using social networks or different applications can be worth USD $240.

From the point of view of the data-buyer

Organizations receive anonymous data packages and use them for their research or projects. Being a decentralized market of anonymous data, the challenge is to know if that information is reliable because there could be many false profiles generated from different devices to create money.

Banks, for example, could be financial data verifiers and telephony companies could be responsible for verifying geolocation. The truth is that all entities that can collect and control data could eventually become verifiers.

Who would want to buy data that circulates for free?

For starters, it should be noted that although several companies collect information, not all can do so in an adequate, safe and orderly manner. Proof of this is that there are companies responsible for processing the large volume of information that is circulating on the web and then offering it, anonymously, to different companies.

Within the various measures that are specified in this regulation is the portability of data — which will allow the user to receive the personal information that has been provided to an entity, in a structured and commonly used format, to grant to another organization. It will work like number portability, but in this case, the asset that the user has is his personal info.

This initiative puts greater responsibility concerning one’s data in the hands of the user. In this sense, rights of the user are recognized, and a mechanism is provided to enforce these. 

Democratize access to data and the benefits it generates

The battle for some is not to oppose the collection and processing of data but to ensure that users can also take advantage of this new form of wealth generation. At present, the benefits are concentrated in few hands, but through some new proposals, data could be democratized and its benefits distributed in a more equitable way.

With a positive outcome, we will be able to cash in on our data and have extra income just for doing data-generating day-to-day activities.

In the centralized internet model, the user transfers his data to large giants such as Facebook, Google or Microsoft. In return, he receives information of all kinds and for different utilities: from a job offer to meeting friends and beyond.

Due to the accelerated pace of technology, young people today have to start preparing their studies for the future with professions that do not yet exist or are beginning to exist due to technological advances.

Studies have already shown that two out of every three young people belonging to the ‘millennial’ generation are convinced that they will devote themselves in the future to professions that do not yet exist due to technological advances.

Professions previously in-demand are no longer necessary and new ones are born each day. To get on the wave successfully, it’s essential to train and do it consistently.

Data scientist

Big data is here to stay. Data science takes advantage of the advances of connectivity and Internet penetration to generate, record, and model vast volumes of information following the scientific method. Its objective is to identify, process, and convert large amounts of data into valuable information for decision-making in any field.

What skills do you need to master to be a data scientist?

  • Mathematical and statistical skills.
  • Big data architecture through the use of software such as Hadoop, relational and non-relational databases, and using programs and languages such as Cassandra, MongoDB, MySQL or PostgreSQL.
  • Programming languages ​​such as R, Python, S, C, SAS.
  • Management of databases such as SQL and programming in HIVE.
  • Data visualization programs with software such as Kibana, Tableau, Clip View, or even Excel.
  • Being curious to look for relationships between data points that do not necessarily seem related.

A fundamental ability to be a data scientist that is considered a “soft skill” is to be curious to look for relationships between data that are not connected or logical to each other — an intuitive, exploratory mind is key.

Expert in artificial intelligence

It is not a secret that, in the technological sector, AI experts receive astronomical salaries due to the high demand of this profile and the shortage of specialists.

Artificial intelligence creates systems capable of learning and prediction from reading data — either from other systems or directly from the environment. This information is processed and stored in the form of “knowledge” that is then used to issue recommendations and actions.

As with the introduction of office computing, artificial intelligence will not replace workers as much as it will force them to acquire skills to complement it. As technology changes the skills needed for each profession, workers will have to adjust. That’s why it’s essential to learn about artificial intelligence now, while it’s still in its relative infancy.

What requirements do you need to become a sought-after AI expert?

  • Know the basics of data processing.
  • Master the development of applications or software with programming languages like ​​R, Python, C #, and C++, among others. Unlike traditional software, whose objective is limited and focused on a series of specific tasks, the one used in AI is focused on constant learning.
  • Mastery of big data architecture.
  • Extensive knowledge of machine learning and machine learning software.

The possibilities of developing AI can be grouped into:

  1. Specific– focused on reading information of a single type and provides solutions based on a specific purpose.
  1. General– seeks to copy the multiple ways of thinking and acting, emulating a human being. The AI then decides on their learning patterns and decisions — although this is still not fully developed because it is a vast and complex problem and requires more robust technological solutions.

Society is changing, and that’s why we have to be prepared for the future before it happens. There are new developments in biotechnology, genetic engineering or robotics; these also begin to provide new forms of employment that will be decisive for innovation in the societies of the future.

For entering the world of AI, it is advisable to have studied some software engineering and have a high command of mathematics, statistics, and programming. With the mastery of these skills, you can create systems that use information to generate knowledge and make decisions in the mode of patterns and probabilities. These talents will serve well in the AI-driven economy of the future.