big data


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.

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.

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.