What is Big Data? – Definition, Importance, and uses

Do you know that someone has already predicted your next move or the next one of the sectors your business is in? Are you aware of the amount of data you work with every day? Do you know what to do with that information? We are going to answer these questions and many others in this post, read on to learn more.

What is Big Data?

To talk about Big Data we can use other similar terms such as big data, massive data, large-scale data, or data intelligence. All of these refer to large sets of data that are difficult to process by conventional means and therefore require some specific software to process this stored information, which is growing at great speed.


The 7Vs of Big Data – The Most Important Features

Depending on where you look you may find that they talk about the 3, 4, or 5Vs of Big Data, this time we bring you 7Vs:

  1. The volume of information (Volumen): how much data is generated every second, minute, or day. This is the most known characteristic of Big Data, the higher this value is, the better predictions will be achieved, and therefore, the better your business results will be.
  2. Velocity(Velocidad): how fast we can create, store, and process this data in motion to have the information available in real-time.
  3. Variety of data (Variedad): what type of data are you storing and where does it come from? As a summary, we can remark on structured data (e.g. Excel table) and unstructured data (e.g. audio, images, video).
  4. Veracity (Veracidad): measures the level of uncertainty, i.e. the reliability of the information we receive.
  5. Viability (Viabilidad): here is where the business intelligence of your company comes in. What is your company’s capacity to use effectively this large volume of data?
  6. Data visualization (Visualización): after processing the data (already in spreadsheets or tables with structured information) it must be represented in a way that allows you to evaluate and improve the performance of your business in a “simple” way.
  7. Data value (Valor): the possibility of making a decision or taking an action based on the data processed and stored. It does not refer to the numerical value (or the format we choose) itself.

These would be the defining characteristics of Big Data, being the main 3Vs, or at least the best known since the beginning, volume, velocity, and variety.

Basic concepts around Big Data

Before we move on and go into more detail, let’s define several concepts in an introductory way so that you don’t miss anything in the rest of the entry. These are:

  • Internet of Things (IoT): concept used to refer to the connection of everyday objects (machinery, human operations, etc.) to the Internet.
  • Business Intelligence (BI): strategies that allow, from a representation of the data (generates knowledge and a better understanding of your company), making the right decisions to grow your business.
  • Artificial intelligence (AI): a programmed intelligent system that solves problems and optimizes its results as it learns (if it is provided with more data, it will be refined).
  • Machine learning: is the field of artificial intelligence that is responsible for developing models with which computers learn from the data it receives.
  • Data Analysis: a process of inspection, cleaning, and transformation of data to visualize only the useful information to support decision-making.
  • Data Science: a field comprising scientific methods, processes, and systems to better understand data, regardless of its form (structured or unstructured). It involves various fields such as statistics, predictive analytics, and machine learning.
  • ETL process: Extract, Transform, and Load. It is a procedure that allows your business to move, reorder, and clean data from one database to another or for integrations between different software.

Relationship between IoT and Big Data

The IoT is responsible for capturing information from intelligent objects from connected devices. This provides a massive amount of data (Big Data) that must be properly managed through desired analytics.

We should take into account the great potential of IoT combined with Artificial Intelligence, as we have already mentioned, it will automatically optimize your processes as the amount of data received increases.

A clear example of data acquisition that we all use every day would be your smartphone or your smartwatch. More related to our business, we could consider the information collected from machinery or software such as an ERP.


Differences between Business Intelligence and Big Data

The terms Big Data and Business Intelligence create confusion among entrepreneurs, managers, and staff who are just starting in this field. Their main differences are that Big Data focuses on the entire process of getting, storing, and processing information while Business Intelligence concentrates on the analysis of data to make timely decisions for the growth of your business. These analyses are performed through the use of dashboards like the one shown in the image. Some exciting tools to carry out these can be Google Data Studio (free tool), Microsoft PowerBI (very economical software, less than 10 euros per month and user) or others like Tableau or Qlik Sense (more expensive software, from 15 euros to +100 per month and user).


Differences and relationship between Big Data and Artificial Intelligence

Both concepts work in the data environment but focus on different tasks that are related to each other. Big Data acts as a massive input of data that needs to be processed. Artificial Intelligence would be in charge of treating this processed information, through the use of algorithms, with which to interpret the behaviors of this data and automatically find (it will learn “on its own”) optimal solutions as if they were performed by humans.


Implementation of Big Data in companies

There are an infinite number of uses that can be given to your massive data in favor of your business, some of them can be:

  • Predictive maintenance: there is a multitude of indicators with which to determine potential failures of machinery and installations. These can be analyzed with the methods already discussed in this entry from structured data (year, make or model of equipment, …) and unstructured (e.g. sensor data input). With this, it could be

possible to optimize the service time and avoid unexpected shutdowns by keeping the facilities operational when we want them to be.

  • Fraud prevention: big data makes it possible to identify patterns of fraudulent behavior and speed up the analysis of various types of data.
  • Operational efficiency: improve decision-making in any of your business processes (plant production, project management, stock control, etc.) to avoid unnecessary waste and focus resources on the areas of the company that generates the greatest benefit.
  • Driving innovation: by studying dependencies between people, companies, and processes. We study trends that show what tomorrow’s customers will need.

In addition to these alternatives, other use cases will be highlighted by company departments or by the sectors in which they are:

Big Data in Marketing and Sales

Customer information is analyzed and processed to understand their preferences better and segment them to improve the results obtained based on predictive models.

We will help you to understand it better with some examples:

  • Product development and launch: anticipate customer needs. Netflix or Google would be a clear example when they recommend you to watch certain series/movies or read certain news.

Customer experience: prepare personalized offers, reduce customer dropout rate or find proactive ways to maximize the value offered to your customer.

Big Data in Logistics

The objective here is to optimize the logistics process based on the data collected:

  • Improve the Last Mile: track your transport status in real-time.
  • Optimize logistics routes: use factors such as traffic, distance, or probability of accidents, … to find the best delivery route in real-time.
  • Control your warehouses and stocks: through automation and the use of IoT you can have total control of your inventory (inputs, outputs, rotation, …). This can help you to optimize your planning to avoid stored material that cannot be disposed of.

Big Data in Human Resources

Here are some specific examples that can help you in your daily activities in this area:

  • Optimizes personnel selection: it works with one or several databases of people and applies the desired filters to facilitate the search for hiring managers.
  • Performance evaluation: measure and analyze the performance of your staff objectively (avoiding possible biases).
  • Personalized training: identify the employee’s needs or points of improvement to train him/her. This will improve both retention and the results obtained by this person.

Big Data in Finance

This is one of the sectors that have taken most advantage of these technologies through the use of Big Data:

  • Risk management: evaluates and anticipates customers that may generate non-payments to take the appropriate measures.
  • Fraud control: distinguishes normal activity from criminal behavior (e.g. banking sector).

Compliance with regulatory requirements in financial institutions: detects anomalies concerning data control and protection so that you can correct them in time.

Big Data in Sports

Here the objective is to know yourself and your opponent to achieve the desired results. In the United States, it is widely implemented in the MLB (>97%), NBA (>80%), or NFL (>60%). In other regions of the world, such as Spain, it is being applied mainly in soccer, although it also stands out in other sports such as basketball, cycling or badminton.

In addition, it allows for evaluation of the recruitment of talent, both technically and economically, based on their performance on the track or to prevent the risk of injuries with training models with customized load management.
We recommend the movie MoneyBall, which deals with this issue.

Big Data in Health

From medical history or genetic material, disease diagnoses can be predicted so

that new drugs can be investigated based on this information.
Another key use is the more efficient administrative management of healthcare by controlling appointments, check-ups, or automatic renewals depending on the circumstances.

Phases of Big Data – Data Life Cycle

Massive data follow a life cycle as shown in the following image. We explain what each of the phases consists of.

data flow
  1. Data source: you need to evaluate where you want to take the data from ( machinery, customers, production lines, administrative processes). Once you decide on these we will focus on the capture.
  2. Get: how you are going to obtain the data, for what purpose, and under what legitimacy.
  3. Store: what data the company will store, for how long, and where.
  4. Use: how we treat the data to obtain the results that allow us to evaluate and decide if something needs to be improved.
  5. Monitor: keep control analyzing and make decisions based on the data obtained.
  6. Secure: establish a data security policy so that data is not stolen and/or lost.
  7. Delete: according to the set storage time, the corresponding records will be deleted.

Professional Profiles in Big Data

These professionals are in high demand, and there is a shortage of talent in the market for digital needs, so their incorporation and retention are becoming increasingly expensive. In the following table, we indicate a remuneration study of 2021 developed by Michael Page (study in euros done in Spain)with the costs of an employee (including fixed, variable, and perks) to the company according to the employee’s experience. As you can see, the costs are high, which is why many businesses prefer to outsource these services to consulting firms such as MORHE, at least initially.


The five most sought-after profiles are indicated in the following lines.

Data Analyst

This is the most sought-after profile in the field we are dealing with. They are in charge of performing statistical analysis of the different sources of information (evaluating their quality, meaning, and usefulness) available to the business.

These analysts have the following skills and knowledge:

  • Degree in Statistics, Mathematics, or Engineering.
  • Proficiency in statistical software (R, SAS) and programming languages (SQL, Python).

Ability to extract, clean, analyze, model, and interpret data.

Data Scientist

This is a step above the analyst since they are in charge of performing a deeper analysis of the information. They develop mathematical models based on statistical programming and machine learning.

These scientists should have the following characteristics:

  • Degree in Mathematics, Physics, or Computer Engineering.
  • He is proficient in statistical software, programming, and massive data analysis systems.

Knowledge of data extraction, cleaning, and modeling.

Chief Data Officer (CDO)

He is the person in charge of the company’s data, both at the business and technological level, validating the technologies to be used.

The director should comprehend the following skills:

  • Bachelor’s degree in Mathematics, Computer or Telecommunications Engineering, or MBA master.

Expert in new technologies and customer-oriented.

Data Architect (Data Architect)

It ensures the platforms that contain the data work properly. To this end, it performs programming, hardware, and cybersecurity tasks.

In this case, you should have skills and knowledge such as:

  • Computer Science or Mathematics background.
  • Experience with unstructured data management (Hadoop, Spark, Cassandra, etc.).

Knowledge of programming (Java/Scala, SQL, Python) and databases (e.g. Oracle or PostgreSQL).

Big Data Architect

Their functionality performed would be similar to that of the data analyst but working with a much higher volume of data. They manage the design and implementation of solutions involving the management of a large volume of data.

The defining characteristics of a data architect are:

  • Degree in Computer Engineering, Telecommunications, or Mathematics.
  • Advanced knowledge in Big Data environments (Apache Hadoop, Spark, HBase, Kafka, Impala, and Hive), relational (MySQL, PostgreSQL) and non-relational databases (MongoDB or Cassandra), use of ETL data processing tools (Kettle, Pentaho) as well as cloud environments (Amazon Web Services, AWS).

Big Data best practices

The transition your company needs to make to successfully implement Big Data should consider the best possible practices:

  • Align data with business goals: clearly set objectives and obtain quality data according to the resources available to your business.
  • Mitigate the skills shortage: train your team to accept this new technology, it will help you in the transition mentioned above.
  • Alignment with cloud operating models: take advantage of the benefits offered by the cloud (speed, contained costs, flexibility, up-to-date software, or backups) to work with large volumes of data without headaches.

We wish this post has been of interest to you, for any questions please do not hesitate to contact us at our email or by any of our social networks, we will be happy to answer you.