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Differences Between a Data Analyst and a Data Scientist

In the information age, data has become an essential and essential element for any brand that wants to develop a precise and effective strategy and achieve the engagement of its target.

For this, many companies invest a lot of money in recruiting the best talent in this field, but when it comes to choosing which is better, a data scientist or a data analyst? And more importantly, do companies know what the difference between them is?

Although both professions are vital for the marketer world, it is essential to understand the differences between their jobs depending on the approach you want to give to a strategy. The truth is that the industry tends to name these professionals indistinctly and has generated a confusion that we want to clear up.

Advent of the data scientist

Companies saw the availability of large volumes of data as a source of competitive advantage and realized that if they used this data effectively, they would make better decisions and be ahead of the growth curve. The need arose for a new set of skills that included the ability to draw client/user perceptions, business acumen, analytical skills, programming skills, analytical skills, machine learning skills, visualization of data and much more. It led to the emergence of a data scientist.

Data scientists and Data analysts

Data scientist– You probably have a strong business sense and the ability to communicate effectively, data-driven conclusions to business stakeholders. A data scientist will not only deal with business problems but will also select the right issues that have the most value to the organization.

A data scientist and an analyst can take Big Data analytics and Data Warehousing programs to the next level. They can help decipher what the data is saying to a company. They are also able to segregate relevant data from irrelevant data. A data scientist and an analyst can take advantage of the company’s data warehouse to go deeper into them. Therefore, organizations must know the difference between data scientists and data analysts.

Data scientists are a kind of evolution of the role of analysts but focus on the use of data to establish global trends on the problems of a company to solve them and improve business strategy.

Data Analyst– Your job is to find patterns and trends in the historical data of an organization. Although BI relies heavily on the exploration of past trends, the science of data lies in finding predictors and the importance behind those trends. Therefore, the primary objective of a BI analyst is to evaluate the impact of certain events in a business line or compare the performance of a company with that of other companies in the same market.

The data analyst has the primary function of collecting data, studying it and giving it a meaning. It is a process that can vary depending on the organization for which you work, but the objective is always the same, to give value and meaning to some data that by itself has no use. Thus, the result of analyzing, extrapolating and concluding is a piece of relevant information by itself, comparable with other data and use to educate other industry professionals about its applications.

An analyst usually relies on a single source of data such as the CRM system while a data scientist can conclude from different sources of information that may not be connected.

Main differences between the two

  • Usually, a data scientist expects to ask questions that can help companies solve their problems, while a BI data analyst answers and answers questions from the business team.
  • It is expected that both functions write queries, work with engineering teams to obtain the correct data and concentrate on deriving information from the data. However, in most cases, a BI data analyst is not expected to construct statistical models. A BI data analyst typically works on simpler SQL databases or similar databases or with other BI tools/packages.
  • The role of the data scientist requires strong data visualization skills and must have the ability to convert data into a business history. Typically, a BI data analyst is not expected to be an expert in business and advanced data visualization.

Companies must know how to distinguish between these two functions and the areas in which a data scientist and a business analyst can add value.

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Network

7 reasons why you need a firewall with VLAN support to protect your network

What is a VLAN?

According to Wikipedia, a VLAN, an acronym for virtual LAN (Virtual Local Area Network), is a method to create independent logical networks within the same physical network. The IEEE 802.1Q protocol is responsible for the labeling of the frames that is immediately associated with the VLAN information.

What does this mean? Well, it’s simple, it’s about logically dividing a physical network, you’ll understand it better with the following example:

Imagine a company with several departments in which you want them to be independent, that is, they can not exchange data through the network. The solution would be to use several switches, one per department, or to use a switch logically divided into small switches, that is precisely a VLAN. We already have the different departments separated, but now we need to give them access to services like the internet, the different servers, and more.

For this, we have two options:

  • Use a switch or layer 3 and 4 switch, that is, with the ability to “route” the different VLANs to a port.
  • Or use a firewall with VLAN support, that is, in the same physical interface, it allows to work with several VLANs as if it had several physical interfaces.

Types of VLANs

Level 1 VLAN

The level 1 VLAN defines a virtual network according to the port of the switch used, also known as “port switching.” It is the most common and implemented by most switches in the market.

Level 2 VLAN

This type of VLAN defines a virtual network according to the MAC addresses of the equipment. In contrast to the VLAN per port, it has the advantage that computers can change ports, but all MAC addresses must be assigned one by one.

Level 3 VLAN

When we talk about this type of VLAN it should be noted that there are different types of level 3 VLANs:

  • VLAN-based network address connects subnets according to the IP address of the computers.
  • Protocol-based VLAN allows creating a virtual network by type of protocol used. It is very beneficial to group all the computers that use the same protocol.

How does a VLAN work per port?

The IEEE 802.1Q protocol is responsible for the tagging (TAG) of the frames that gets immediately associated with the VLAN information. It consists of adding a tag or TAG to the header of the structure that indicates to which VLAN the frame belongs.

Based on the “tagged” VLANs, we can differentiate between:

  • TAGGED– When the connected device can work directly with VLAN, it will send the information of the VLAN to which it belongs. Thanks to this feature, the same port can work with several VLANs simultaneously.

When we configure a port with all the VLANs configured in TAGGED, we call it Trunk and it is used to join the network device in cascade. This system allows the packets of a VLAN to pass from one switch to another until finding all the equipment of said VLAN. Now we need to give them access to services like the internet, the different servers, and more.

For this, we have two options:

  • Use a switch or layer 3 or 4 switch, that is, with the ability to “route” the different VLANs to a port.
  • Or use a firewall with VLAN support, that is, in the same physical interface, it allows working with several VLANs as if it had several physical interfaces, each of which will give access to a VLAN to the services.

Choosing one or the other depends on whether the firewall used supports VLANs, if we pass communications through the firewall, we will always have more control over them, as I will explain later.

Advantages of segmenting your network using VLANs

The main benefits of using VLANs are the following:

  • Increase Security- By segmenting the network, groups that have sensitive data are separated from the rest of the net, reducing the possibility of breaches of confidential information.
  • Improve performance- By reducing and controlling the transmission of traffic on the network by division into broadcast domains, performance will be enhanced.
  • Reduction of costs- The cost savings result from the little need for expensive network upgrades and more efficient use of links and existing bandwidth.
  • The higher efficiency of the IT staff- The VLAN allows to define a new network over the physical network and to manage the network logically.

In this way, we will achieve greater flexibility in the administration and the changes of the network, since the architecture can be changed using the parameters of the switches, being able to:

  • Easily move workstations on the LAN.
  • Easily add workstations to the LAN.
  • Easily change the configuration of the LAN.

Advantages of having a firewall with VLAN support

  • More significant cost savings- We will not have to invest in a switch with “routing capacity,” and it will be worth a layer 2, currently very economical.
  • Greater security and control- We do not “route” one VLAN to another without any power, being able to create access rules between the VLANs and inspect all traffic.
  • The higher performance of the network- We will have the possibility to prioritize by QoS (Quality of service) specific VLANs or protocols.

Voice over IP (VoIP) traffic since it requires:

  • Guaranteed bandwidth to ensure voice quality
  • Priority of transmission over network traffic types
  • Ability to be routed in congested areas of the network
  • Delay of less than 150 milliseconds (ms) through the network

Therefore, as you have seen, having a Firewall with VLAN support supposes a series of significant advantages when managing your information systems. Not only will you get performance improvements, but you’ll also simplify your administration tasks.

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Data

The Difference Between Structured Data and Unstructured Data

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.

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Optimization

What is Stochastic Optimization?

The Stochastic Optimization seeks the best decision in a scenario dependent on random events, dependent on chance, whether those events the prices of a product, the duration of a task, the number of people in the queue of a cashier, the number of breakdowns in a fleet of trucks, or even the approval of a regulation, come on, anything.

Stochastic?

Stochastic is a particularly feared word. It is since is known that most functional languages ​​have been made a bad idea, by jealous experts who want to keep their secrets. There we have the legal jargon, the economic, or closer to our work; rumor has it that the creator of the C ++ language made it so complicated to differentiate good programmers from bad guys. The word “stochastic” is not dangerous; it means simply random, dependent on chance. The idea is quite simple, but as an adjective, it can complicate any discipline.

Problems of Stochastic Optimization

The problems of stochastic optimization are in general much more complicated than those that do not consider chance, mainly because luck implies that we do not have a single scenario to be optimized, but a set of possible scenarios. For example, if we want to maximize the design of an energy distribution network, we will work in an uncertainty scenario, in which we do not know the actual demand for energy at the time of use of the system. Instead of the demand data, we would have an estimate, perhaps a finite set of possible demands with an associated probability.

With this, we can already intuit that the world of the company is full of stochastic problems, what is usually done to solve them ?. In scenarios with simple decisions, that is to say, few decision variables and with few states, all the possibilities can be explicitly enumerated using decision trees that are also very intuitive.

Stochastic Optimization

Although it is considered that this discipline was born in the 50s with Dantzig and Beale, historically optimization has had enough to be restricted to non-stochastic problems, essentially due to the complexity that stochastic problems entail. Facing real problems is still impossible in many cases, but advances in computing capacity and the development of optimization techniques have allowed problems to be solved until recently unthinkable. In addition, sometimes, only a stochastic approach can greatly improve the solution, which translates into cost savings, service improvement, increased benefits, among others, all of them factors not insignificant.

An optimization problem has:

  • a series of variables or decisions that must be taken.
  • a series of restrictions that limit those decisions.
  • and an objective function, a measure of cost or quality of the set of decisions taken.

The data associated with the constraints and the objective function are usually known values, but what if random events gave these values? Then the problem is stochastic optimization.

There are two particularly uncomfortable questions:

What happens to feasibility when the restrictions are random?

A solution is feasible when it satisfies all the restrictions, but with arbitrary limits, we cannot speak strictly of feasibility but probability that a certain answer is viable. Thus, in the problem of planning the power distribution network, a restriction could be “the capacity of the distribution network is greater than or equal to the demand” but if the demand turns out to be very high, it can happen that we can not satisfy it.

How to redefine the objective function?

The answer to this question is less obvious, we could redefine the objective function as the expected value of the previous objective function, but it could be more interesting to reduce the risk of the decision using the worst case scenario. In general, these types of problems have been addressed using linear programming.

What are these problems complicated?

The complexity of these problems lies in their size. Think of the seemingly harmless lady who leaves the hairdresser. Suppose you also have to decide whether to go by bus or walk to do a message. Each new random variable and each new decision multiply the possibilities. Instead of considering four possible outcomes we should consider tens or hundreds.

If we continue to consider elements of uncertainty that may threaten the woman, the number of possible outcomes will increase exponentially. Let’s go back now to the problem of planning the energy distribution network; the demand is random, the future prices of energy are arbitrary, the production of wind energy is absolute, as well as the costs of fuels. The outcome of any decision in this context will depend on what happens with each of those random events.

Conclusion

Most of the real problems are of stochastic nature; there are few businesses in which all the data are known in advance, we cannot keep avoiding them. The stochastic optimization can allow us to face problems that until now have been solved by “intuition,” by “common sense,” or because “of all life has been done like this,” in a more efficient way, providing solutions that will place us in clear advantage over our competitors.

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Data

Benefits of Storage for Big Data

The development of technologies and the Internet has dramatically increased the volume of data handled by large companies. Consequently, this has accelerated the evolution of data management models, until the creation of data governance. Data governance has, among other functions, to manage the data storage function, decide how, when and what gets stored.

The main challenges of data governance are:

  • Lack of human resources
  • Too much time spent cleaning and examining the data
  • Access to data
  • Lack of technological resources

The increase in the volume of data has brought technological challenges to deal with from storage to processing.

What is data storage?

The volume of data that a large company generates grows exponentially day by day. The data storage function seeks to meet the objectives set from data governance; implement good practices and policies on how, when and what is stored.

What is the big data?

Big data is a technology that allows the massive and continuous analysis of data, and that relies on data storage in a cloud (storage in the cloud); In addition, this technology allows solving some of the problems of data management or governance.

From data storage to big data

Here are some benefits of data storage in the cloud for big data:

Accessibility– the data that is in the cloud can be accessed from anywhere and at any time. A company with multiple branches can have its employees discuss projects and share information without having to gather them; employees can work from different places without losing competitiveness.


Reduction of costs– when a company invests in servers for storage, it incurs other expenses such as maintenance, security, personnel, IT consultants. While if data storage services are contracted in the cloud, costs can be reduced; thanks to the fact that you only pay for the storage consumed, optimizing the use of resources.


Optimization of space– local servers occupy spaces that can be allocated to productive areas. Big data helps data governance migrate to the cloud. The data storage service in the cloud optimizes the use of space. How much space can you occupy, the physical files and servers in a large company?


Maintenance– the maintenance of the servers of a company that provides services in the cloud, is not the responsibility of the contracting companies; so data management is freed from that responsibility. This in addition to saving time and money allows you to concentrate on other aspects of data management.


Security– companies that provide cloud storage services are at the forefront of information security technologies; what reduces threats and minimizes risks; Large companies that are often subject to cybercrime save resources thanks to the security of cloud storage.

The management of information in the workplace requires a rethinking in many areas and levels; In the human factor, whether managers or employees, everyone will have to improve the methods used. It is fundamental to change the vision, establish strategies and policies of information management and review what the market is offering in order to reach higher levels of competitiveness. It is essential that companies today allocate part of their investment to have an update in their work tools.

These traditional forms of work, do not respond adequately to the pace of massive data growth, and before this, there is a delay in the daily tasks of business. In the same way, consequently, the question arises.  Why not devote more to state-of-the-art technology and leave in its benefits a constant and avant-garde rating of our business? As a decision maker it is essential to update processes and policies year after year, enabling IT is a flexible, friendly and high potential way for your company to become a success story.

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Data

What can happen while handling large amounts of Data?

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.

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Data

What is the Difference between Data, Information, and Knowledge?

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.

Data

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.

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.

Knowledge

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.

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Data

What Are Different Types of Data Analysis?

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.

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Disaster Recovery

Your Steps to a Disaster Recovery Plan

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.

Priority-setting

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.

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Data Center

How Can a Data Center Transform Your Business?

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.

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