Big Data Basis
We are living the era of information where data come from many sources for example: machines, people and organizations.
The machines can generate massive data per seconds using sensors. The Internet of Things (IoT) is included on this type of source and is emerging as one of the trendiest data sources for the variety of the information that can be collected. Some examples are connected security systems, cars, electronic appliances, lights in household, alarms, sensors used by satellites to forecast the weather and many others.
The personal devices generate tons of data that continuously grow and the organizations with their technological systems are capable to collect information from forms and many other data entries.
What is Big Data?
Although there’s not a universal accepted definition for Big Data it can be understood by the data sources that combine the 5 V’s: Volume, Velocity, Variety, Veracity and Valence. Not all are required to be present and any of them can drive the need of Big Data technology. Let’s take a quick tour for each concept:
Probably, this is one of the best-known characteristics of Big Data. This is related how to handle the storage efficiently of larger amount of data and to retrieve data in a fastest way for processing. When a company has large amount of data, let’s say historical information for example, this can allow them to turn this large amount of data into a business advantage to take decisions.
This term refers to the speed that data needs to be stored and analyzed. Processing data in real time is a singular goal of Big Data. For some business a late processing can lead to missing opportunities.
When I started studying Computer Science, I thought of data as tables, spreadsheets, databases, files, XML or JSON. But today we can find a wide variety of data that is collected, stored and analyzed to solve problems. We can find image data, stream audio, geographic maps as some samples of different types of data.
This refer to the quality of the data because sometimes based on the source the data can be imprecise. The data isn’t valuable by itself if it’s not accurate and the success results of the big data processing will depend how much reliability are the data sources. This is a challenge because data cannot be always normalized and for Big Data the data can be received from many sources as social media or IoT sensors where data can be unstructured with no predefined format.
This Big Data characteristics isn’t usually known. This is related with the measure of connectivity of the data. This require sometimes more complex analytical methods because of the dynamic behavior of the data. This is very interesting because data from the same or different data sources cannot be related at first instance but after modeling and observing the data trends it can be discovered new connections.
Organization’s Big Data Benefits
There’re many benefits that companies have experienced over the last years when have combined the organizational and the data source to generate value on their business.
A large amount of data can be analyzed to take a kind of X-Ray of current processes and aid to make decisions how to efficiently optimize their internal operations. For example, can assist to decide to invest to get higher sales, measure customer satisfaction to gain profit margins on the sales and improve products on the market.
When an organization process data on real time can make decisions much faster than competition.
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By Emilia Vega
Sr. Software Developer