In a world highly digitized and rich in data, its processing from efficient technologies, to enable its capture, storage, Real-time processing and analysis represents a great step forward to overcome the challenges of Big Data.
Although the reliability of the information is sought as a priority, the requirement for clean data does not follow the same logic as in relational environments, where all data is structured, they are more scarce and offer infinitely poorer information if what we seek is to answer fundamental questions for the business, since these can only be answered in the Big Data key.
In Big Data projects, However, efficiency is sought in the result in a more flexible way, and this implies, necessarily, strive for data quality, even when it is obtained in another way, since we are working in real time, with big data coming from different sources, high volume and complexity. Specifically, with Hadoop we identify false data within a context, from a series of variables that guide us on the veracity or falsity of the information.
Data can come from many different sources, including the sensors, smartphones or internet, especially the social web, and its analysis is carried out with a myriad of objectives, that can range from scientific research to the detection of human actions or, as an example, monitoring of machines to control their operation.
The reading and processing of sensor data make it possible to carry out analyzes that make it possible to take advantage of one of the largest sources of data that exist at the current technological moment. In reality, smart sensors, cloud computing and digital interconnection are the basis of the new society or paradigm of the Internet of things.
Recognize false data
When it comes to identifying fake data in Big Data projects, either from sensors or another data source, data scientist will establish rules that alert you of some parameters of normality.
It is essential to consider that the false data that we are interested in detecting will be those that are related to the company's needs, so it's about being selective, and its assessment will be carried out in a context that will obey a certain program.
The objective is discriminate data that are relevant because they are within the margins established as standards or, in the case of variables analysis, with the purpose of create context based on an algorithm containing those that the data scientist deems necessary.
If we are working with sensor data, we will easily identify those who are out of range expected, because at the time of programming we will have certain guidelines that will serve as a reference, with what will become of them since we will discard the data or not.
The relevance of the data scientist
The challenge of making sense of data cannot be met without a professional who can provide appropriate use of technology, whose purpose is none other than to extract information capable of guiding the company's strategic decisions.
Although the Hadoop platform is essential to obtain valuable information from Big Data at low cost, it could not be achieved without the figure of the data scientist, a multidisciplinary professional who needs a very specialized preparation.
Finally, their role is also key at the time of identify false data, since the interpretation of the data within a given context serves as an orientation in this regard and constitutes a practically infallible compass to find the way that leads to reliable information.
Image source: renjith krishnan / FreeDigitalPhotos.net
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