web hosting


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.

Enterprise-level companies work with a large volume of data, which makes their analysis and subsequent decision-making complex. It’s necessary to combine data from diverse sources in order to obtain insights and analyze information about consumers and the market. In this article, we are going to address the four types of data analytics that you can (and should) use in your business.

Descriptive analysis

In a business, this refers to the main metrics within the company. For example, profits and losses in the month, sales made, etc. This data analysis answers the question, “what’s happening now?” Companies can analyze data on the customers ​​of a specific product, the results of campaigns launched, and other pertinent sales info.

Descriptive analysis allows companies to make immediate decisions with a high level of surety since they’re using concrete and up-to-date data. The information coming from this type of analysis is often displayed in graphs and tables, which allows the managers to have a global vision of the monitored data.

Predictive analysis

Predictive analysis has to do with either the probability of an event occurring in the future, the forecast of quantifiable data, or the estimation of a point in time in which something could happen through predictive models.

This type of analysis makes forecasts through probabilities. This is possible thanks to different predictive techniques, which have been honed
in the stock and investment market.

Diagnostic analysis

The next step in complexity of data analysis, diagnostic analysis requires that the necessary tools must be available so that the analyst can delve deeper into the data and isolate the root cause of a problem.

Diagnostic analysis seeks to explain why something occurs. It relates all the data that is available to find patterns of behavior that can show potential outcomes. It is essential to see problems before they happen and to avoid repeating them in the future.

Prescriptive analysis

Prescriptive analysis seeks to answer the question, “what could happen if we take this measure?” Authoritative studies raise hypotheses about possible outcomes of the decisions made by the company. An essential analysis for managers, it helps them to evaluate the best strategy to solve a problem.

Analyzing data is essential to respond to the constant challenges of today’s competitive business world. It’s no longer enough to analyze the events after they have occurred — it’s essential to be up to date with what’s happening at each moment. Monitoring systems are necessary tools in the business world of today because they allow us to analyze to the second what is happening in the company, enabling immediate action — and hopefully bypassing severe consequences.

An excellent example of this is a traffic application that helps you choose the best route home, taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic restrictions.

While different forms of analysis can provide varying amounts of value to a business, they all have their place.

Processing techniques and data analysis

In addition to the nature of the data that we want to analyze, there are other decisive factors when choosing an analysis technique. In particular, the workload or the potentialities of the system to face the challenges posed by the analysis of extensive data: storage capacity, processing, and analytical latency.

Stream or stream processing is another widely used feature within Big Data analytics, along with video analytics, voice, geo-spatial, natural language, simulation, predictive modeling, optimization, data extraction and, of course, the consultation and generation of reports. When making decisions aiming for the highest value to one’s business, there’s a wide variety of advanced analytic styles to choose from.

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.

To increase your domain authority is not something that can be done in a few days, it is a medium-term strategy. From the moment you register your domain, ensure that the theme always follows the same line — a domain that radically changes thematically is seen by Google as unreliable and can be penalized. Post new content on a frequent basis which is also useful for the user, increasing the chances of other websites linking that content.

Tips for increasing domain authority now

1. Domain age– The older it is, the more reliable a domain is in the eyes of search engines. If you have been managing a website in your domain for a long time, with which you have gradually gained traffic, for search engines your website is performing the purpose for which it was created, and that has made it a “reliable” site.”

2. The popularity of the domain- The popularity of a domain is measured by the number of websites that link it. SEO and link-building are important factors to determine the reputation of a site and get links back. Getting quality links is one of the most critical tasks to increase your web presence, and you can do it in several ways: writing blogs and articles, commenting on other blogs, writing on forums, press appearances, posting on social networks, etc.

3. Site size– Imagine your website as a tree and your blog posts or individual web pages as the branches of that tree. The more content our website generates, the more likely we are that other users will be able to find this content and that it will linked from other sites. Also, the size of our publication also helps our site to be seen as a purveyor of “quality content.”

Not only does the number of links matter, but they must be high-quality. It is preferable to have a single link from a domain with a high impact, such as a newspaper or a well-known website, than to have several links that come from small and anonymous sites with low results.

How to measure the authority of your domain?

SEO Toolbar MOZ– An extension that you can install in your favorite browser (Google Chrome or Mozilla Firefox) that you can activate to measure the authority of a domain or search in Google, and deactivate when you do not need it (removing fixed bars that occupy an unnecessary space in the browser).

Open Site Explorer– If you do not want to install anything, I recommend that you use this tool that will measure the authority of the domain and tell you the best quality linkage that is contributing substantially to that authority.


  • Having a great domain authority will help you have a good search position for your content.
  • Not all domains of great authority have a great positioning or visibility in the search engine since there are other factors that can make for better or worse SEO; internal links, the health of the back-links, lack of quality content, duplicate content, broken links, etc.
  • The construction of the authority of a website is not built in a day — we have to do it based on effort and work, to gradually reap the results.
  • In order for your domain to have good authority you will need 1 to 2 years, the process can’t be rushed.

The use of social networks can not only help you generate traffic to your website and ensure that the content reaches more users, but it is also another factor that search engines take into account to determine the authority of a domain. If your content is shared and linked by users in different social networks, search engines will interpret that your content is useful and adds value to the user.

One of the best ways to increase the number of times your content is shared on social networks is through videos and infographics that complement your articles. It’s demonstrated that visual content is the one that tends to generate shares in social networks and that results in more virality.

There have always been analytical data systems, but the fact is that, with the emergence of information technology, we all generate vast amounts of data continuously. Also, we have developed tools to capture data that we do not knowingly disclose, and they are manifold: access controls, access to wifi, email, social networks, geolocation, the use of our phone, Internet cookies, our credit cards and more.  We are generators, conscious or unconscious, of data and more data.

What is Big Data for?

Well, the info that we generate forms a valuable and gigantic data package that, properly analyzed and managed, can give information about our habits, our tastes, our way of buying, our health, our socio-economic position, political ideas, customs, and beyond.

And that information is gold when it comes to being able to understand the consumer, create the profile of the client, create advertising or communication campaigns, improve the service, launch products, improve them or vary their prices.

3 tips to sell more thanks to Big Data

The success of an online store is often due to strategies that allow you to multiply sales opportunities as well as the level of personalization and customer satisfaction.

73% of online shoppers prefer to make transactions on websites that use their data to offer them a more relevant shopping experience, according to Digital Trends. Most visitors prefer to be recognized when it is not the first time they visit a website and appreciate that the offers they propose are related to their tastes, interests and past experiences.

According to mybuys.com, 48% of customers spend more when their shopping experience is personalized in the different channels they use. Follow our advice so that your visitors become customers that you can subsequently retain by taking into account their tastes and expectations.

1. Exploit your store data

What are the products that attract the attention of your visitors? Which ones end up buying?  The control panel will be of great help.

Has a product been consulted frequently but hardly bought? Consider why and draw conclusions. If your prices are not competitive, reduce the margins and propose corresponding items to compensate for the loss, or look for other suppliers.

If an item is particularly profitable; adopt the necessary means to sell more units. Reserve a prominent position for it, incorporate the opinions of customers, put it as part of a pack (consisting of several items sold at a lower price when purchased together) to increase your average basket and publicize other items.

Google Analytics provides you with precious information about your visitors: geographical origin, age, sex. You will also know the way your visitors arrive (Google, Facebook, price comparison, marketplaces, links to other websites, etc.).

To take advantage of this data, create your free account on Google, and then copy and paste the code that you will receive to insert it into the label provided for it in your administration space or codebase.

      2. Take advantage of the cross-channel

E-mailing and newsletter

E-mail is first in advertising support, generating traffic to websites and offers a very powerful virality: 44% of Internet users have already shared offers received by mail, and 28% indicate that they have visited a store after receiving an e-mail from you (Study E-mail Marketing Attitude, 2014).

To get the most out of e-mailing, segment your customer base according to the purchase frequency and the amount of orders.

In this way, you will be able to carry out more effective, specific actions to refine the segmentation through particular offers. Both for a large consumer of products at a moderate price, and a specific buyer of products at higher rates, the analysis of your website’s data will allow you to propose specific offers through channels.

Thus, the objective of Big Data, like conventional analytical systems, is to convert the data into information that facilitates the decision-making, even in real time, of many aspects of the company’s strategy and, specifically, from the marketing point of view. If we know our consumer, we can sell more and target better. Marketing actions will be more effective, and we will be able to measure our investments’ returns much better.

For the first time, we can generate databases with tens of millions of entries of users in collective creation processes over the Internet. In turn, we obtain data from a multitude of new sensors, which allow us to collect an increasing number of data that must be processed, structured and managed to transform them into useful information.

There is much to be done, new roads to open — we need to explore innovative paths where business, science, medicine, education, politics, law, and even art collect that massive amount of information and can predict educational trends, treatment of diseases or earthquakes, identify vaccines, monitor new conditions, the optimal amount of electricity we need, or better understand animals and nature, for example.

All this while we continue asking questions about the origin and right of data and information use and the erosion of privacy.

  • Every two days, humanity creates as much information as civilization had until 2003.

  • The amount of average information a person is exposed to in a day is the same as that of a 15th-century person who was exposed throughout his life.

When the volume of data exceeds our cognitive capacity, when the traditional tools do not allow processing all the data obtained, we need new methods that will enable us to transform them into useful information: visual and accessible.

What’s the impact?

Information is part of the planet; it is like a part of your nervous system. We can understand big data as the ability to collect, analyze, triangulate and visualize immense amounts of information in real time, something that human beings have never done before.

This new type of tool – big data – is beginning to be used to face some of the most significant challenges of our planet. The global conversation about usage, the tremendous potential of information, and the concerns about who owns the data that you and I produce.

It is essential to recognize the effect mentioned above, collect and analyze vast amounts of information in real-time, and observe how we can live, interact, and grow in this information environment.

Where are we going?

The digital universe evolves so fast that any advance is obsolete in 18 months, imagine what this means and the impact it has on the planet. Although, for now only large companies like IBM and governments think about the use of big data, it is essential that each of us think about how this will ultimately affect our lives.

Big data has been created to do good. However, it could also have unintended consequences, such as the use of this medium for personal purposes. At this time no law governs big data. All the regulation is being decided by big corporations that use it as they want and maybe when we start thinking about it; it’s too late.

The world may one day capitalize on big data for all, but for now, it is one of the most significant challenges humanity faces.

For the first time, computers no longer only help us process information, they are the only ones capable of managing the volumes derived from big data. The human mind can not process the millions of data generated by a particle accelerator. The border between formal sciences and experimental sciences is blurred, and the computer ceases to be an aid to become an indispensable and irreplaceable piece of scientific research.

In the immediate future, the economic value will pass from the services to the data, the algorithms to analyze them and the knowledge that can be extracted.