What is Big Data?
Data sets are so large and complex that non-traditional data processing applications must process them properly.
The data is the symbolic reproduction of an attribute or quantitative or qualitative variable; according to the RAE, "Information on something concrete that allows its exact knowledge or serves to deduce the consequences derived from a fact."
Therefore, the procedures to find repetitive patterns within that data are more sophisticated and require specialized software.
How does it work, and how is it used?
The main operation of Big Data is to inform the user. Have access to more information. The objective is to help evaluate the pros and cons to make the most appropriate decision.
Big Data works based on the so-called "5 Vs": volume, variety, speed, veracity, and value.
- Volume: The amount of data generated and stored. This volume is vital for storage, processing, and exploitation.
- Variety: The type and nature of the data help people analyze the data and use the results effectively. Big data uses text, images, audio, and video. To process data, particularly unstructured data, new technologies are needed to facilitate its analysis.
- Velocity: The speed at which data is generated and processed to meet the demands and challenges of your analysis. If the data is transferred directly to the memory, the speed will be higher, and the data will be obtained almost in real time. This requires a way to evaluate the data in real time. Speed is important in areas like machine learning and artificial intelligence.
- Veracity: the quality of the data captured can vary greatly and affect the analysis results. Uncertain data leads to correct decisions. For this reason, you should always check the data.
- Value: The data generated must be useful, actionable, and have value. It is essential to know the value of the available data, establish a way to clean it, and confirm that it is relevant to the intended purpose.
Examples of Big Data use
Predictive Models
With the help of Big Data analytics, Netflix and other organizations use predictive models and let you know what's new you might like by sorting past data and shows you've watched or marked as favorites.
IBM took all the crime data in Chicago and processed it for predictive analytics. With this, it was possible to detect where the crimes would occur before they happened in a very detailed way. It is very likely to reduce crime in a city by 30% thanks to the use of Big Data.
Machine learning
uses Big Data to develop machine learning models thanks to statistical and computational intelligence, which analyzes large amounts of information with minimal or no human supervision.
Customer experience
Big Data allows data collection from social networks, internet visits, call logs, and other sources to improve the customer experience through personalization and decision-making.
Comparative analysis
When you know how customers behave and observe it in real-time, it is possible to compare their patterns with the paths they have followed for other similar products and identify the strengths of an organization compared to its competitors.
It is also useful when conducting comparative analysis in different markets, such as real estate, where having a large amount of data available to analyze can mean an immediate competitive advantage.
Stock control
One of the Big Data areas that companies can have the most impact on companies today. Thanks to data analysis, sales predictions can be made based on different factors, time of year, production, customer opinion, and returns, thus anticipating possible lack of stock and carrying out specific promotions.
Fraud prevention
Big Data also helps, thanks to predictive analysis, to analyze fraud patterns and computer attacks on the companies themselves and the companies.
Types of big data
Structured data
Data that has a well-defined length and format, such as dates, numbers, or character strings. They are stored in tables. An example is a relational database and data warehouses.
Unstructured data
Data in the format as it was collected, needing a specific structure. They cannot be stored within a table since their information cannot be broken down into basic data types. Some examples are PDFs, multimedia documents, emails, or text documents.
Semi-Structured data
Data is not limited to specific fields but contains markers to separate the different elements. It is unusual for information to be managed in a standard way. For example, we have spreadsheets, HTML, XML, or JSON files.
Software or tools for Big Data
Python
Its use for Big Data is very efficient, partly due to the large existing community, which is why Python has many libraries already made by other users. However, it has against it that It could be a lot faster language in its execution, so it is usually used for integration tasks or tasks where there are no heavy calculations.
R Language
A programming language and software environment for statistical computation and graphics. Statisticians and other professionals interested in data mining, bioinformatics research, and financial mathematics most use the R language.
Elasticsearch
It is a search tool for large amounts of data, especially when the data is of a complex type. It allows us to index and analyze a large volume of data in real-time and make queries on it. With Elasticsearch, we can do complicated text searches, visualize the status of our nodes, and scale without too much need, if it is the case that we need more power.
Apache Hadoop
One of the best-known solutions for analyzing Big Data, which uses an open-source framework to store and process large data sets.
TensorFlow
An increasingly popular machine learning platform used for machine learning purposes.
Apache Spark
This tool allows you to store much of the processing data in memory and disk, translating into more incredible speed. It works with Java, Scala, Python, R, and SQL programming languages and with Hadoop Distributed File System (HDFS), Apache Cassandra, OpenStack Swift, and many other data storage solutions.
Apache Kafka
This solution lets users publish and subscribe to data sources in real-time. Kafka's main task is to transfer the reliability of other messaging systems to streaming data.
Benefits and importance
Speed in decision-making: Information is essential as a basis for correct decision-making, and much more so when we can dynamically handle all the information provided by Big Data. It is possible to analyze an opportunity before putting any product or service on the market.
Intelligent Strategic Marketing Plans
Parameters related to the specific profile of each user, their preferences, their tendencies, or their link to the brand can be analyzed.
We can develop targeted marketing campaigns with a high level of personalization.
Improvement in efficiency and costs
Big Data can quickly boost the speed at which a product or service evolves because we have a multitude of data with the information the market gives us.
In this way, the deadlines for developing a product or service are shortened over time, as well as the costs associated with the process that derives from its development.
Guarantee greater data security
Maintaining control of the data in an organized way allows possible complications to be detected in time threats and makes it easier to find sensitive information that is not adequately protected.
Improve accessibility to information
When the data in a company is digitized and ordered, accessing relevant information is much easier.
Therefore, implementing Big Data is an excellent way to optimize processes oriented toward business intelligence.
Obtain competitive advantages
Since access to this type of data allows predictive analytics tools to be updated, adjusted, and applied to a product in real time, it is possible to establish a better position in the market concerning the competition.
It has enormous potential and is vital for the progress of technology; it can improve a company's operations, provide better and more personalized customer service, optimize marketing campaigns, and contribute to better decision-making criteria.
The applications of big data are almost endless. Mechanical failures or errors can be minimized because the conditions in which they occur can be predicted.
Conclusion
The scope and possibilities of Big Data are immense. The amount of data that we have at our disposal is increasing, and the tools used for Big Data will evolve.
Large-scale data storage and management are likely to be done in ways that require less space and resources.
Big Data is already shaping the future of humanity, offering the best service to users.
Some of its sources are data obtained from financial transaction processing, medical records, company customer databases, emails, social media databases, etc., helping organizations harness their data and use it to identify new strategic business opportunities, leading to more innovative business moves or more efficient operations.
