What is Big Data?

When we talk about Big Data we refer to data sets or combinations of data sets whose size (volume), complexity (variability) and speed of growth (speed) make it difficult to capture, manage, process or analyze them using conventional technologies and tools, such as relational databases and conventional statistics or visualization packages, within the time necessary for them to be useful.

Although the size used to determine whether a given data set is considered Big Data is not firmly defined and continues to change over time. Most analysts and practitioners currently refer to datasets ranging from 30-50 Terabytes to several Petabytes.

The complex nature of Big Data is mainly due to the unstructured nature of much of the data generated by modern technologies. Such as weblogs, radio frequency identification, sensors incorporated in devices, machinery, vehicles, Internet searches, social networks like Facebook, laptops, smartphones and other mobile phones, GPS devices and call center records.

Why is Big Data so important?

What makes Big Data so useful for many companies is the fact that it provides answers to many questions that companies did not even know they had. In other words, it provides a point of reference. With such a large amount of information, the data can be molded or tested in whatever way the company considers appropriate. By doing so, organizations are able to identify problems in a more understandable way.

The collection of large amounts of data and the search for trends within the data allow companies to move much more quickly, smoothly and efficiently. It also allows them to eliminate problem areas before problems end their benefits or reputation.

Big Data analysis helps organizations take advantage of their data and use it to identify new opportunities. That, in turn, leads to smarter business movements, more efficient operations, higher profits, and happier customers. The most successful companies with Big Data achieve value in the following ways:

Cost reduction. Large data technologies, such as Hadoop and cloud-based analysis, provide significant cost advantages when it comes to storing large amounts of data, in addition to identifying more efficient ways of doing business.

Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new data sources, companies can analyze information immediately and make decisions based on what they have learned.

New products and services. With the ability to measure the needs of customers and satisfaction through analysis comes the power to give customers what they want. With the Big Data analytics, more companies are creating new products to meet the needs of customers.

Challenges of data quality in Big Data

The special characteristics of Big Data mean that its data quality faces multiple challenges. Volume, Speed, Variety, Veracity, and Value, which define the problem of Big Data.

These 5 characteristics of big data cause companies to have problems to extract real and high-quality data from data sets that are so massive, changing and complicated.

Data Governance plan in Big data

Governance means making sure that data is authorized, organized and with the necessary user permissions in a database, with the least possible number of errors, while maintaining privacy and security.

It does not seem an easy balance to achieve, especially when the reality of where and how the data is hosted and processed is in constant motion.

In the end, considering the confidence in the data, extracting quality data, eliminating the inherent unpredictability of some, such as time, economy, etc., is the best to reach a correct decision making.