What Is Big Data?
Capabilities of machines are limited. Amongst their performance limitations, is the size of data they can process. While machines nowadays are capable of handling large-sized data, the exponential increase in the size of data is still a big problem.
Data in today’s date is gigantic and is increasing exponentially. Due to this increase in size, traditional methods of storing and processing data fail badly.
To overcome this problem, we need a system or process that can deal with this enormous amount of data. Big Data is the process that uses advanced and real-time processing units and readily available hardware that can easily handle gigantic data.
Data is no longer of the same form it used to be in older times. While the data in older days was mainly transactional, it is now a blend of transactional and unstructured data. This unstructured data is collected privately but is available publicly.
This kind of data has resulted in an all-new architecture for both public and private data centers. As a result, there are a lot of challenges for Big Data. These Big Data challenges should be conquered to provide data quality and accuracy.
See Also: What Could Possibly Go Wrong With Big Data?
Big Data Challenges That Should Be Conquered?
This article is all about what challenges will Big Data face to maintain data accuracy and quality. Continue reading to know what they are:
1. Problem Faced by Big Data in Data Integration:
Since it is known Big Data process manages and integrates massive data amounts, any error in collecting and processing the data may lead to erroneous results.
Because of the massive data collected by organizations, Big Data faces a lot of problems in data integration. It is also very tough to monitor how effective is the integration process.
This mainly happens because of the false perceptions relating to the ways in which data should be collected, verified, stored and then finally utilized. These erroneous perceptions may lead to inaccurate results and hence is a big challenge that should be taken care of.
2. Data Complexity:
It is seen that data complexity is increasing exponentially with time. As a result, Big Data system should be more advanced and accurate, and this can only be achieved when several constraints and aspects are considered.
Now a day’s raw data goes through multiple stages and sources like operations, consumers, and many more. Thus, the complexity of data has increased manifold. What adds more is the kind of technologies that are used to process the data through different stages and channels.
The data complexity and technologies involved make it extremely hard for Big Data to process it.
3. Challenges Big Data Faces in Providing Data Security:
Amongst the Big Data challenges, another challenge is Data security.
Data collected from innumerable sources cannot be stored anywhere. One major requirement that needs to be taken care of is security. Organizations and people have started using cloud services to store data because data stored on clouds is easily accessible.
Although cloud services are an easy option to store data, it is still insecure.
All these problems can be avoided if there are measures taken at the elementary level.
If Big Data conquers it, the entire data processing and integration will become smooth.
4. Data Value:
Older philosophies of how data should be stored have been changed. All thanks to the data value. Currently, the kind of data involved stands important for organizations and hence its usefulness has increased.
The current scenario requires data to be stored for longer time periods and should also be easily addressable.
This accurate and long-term data proves beneficial to analyze data and produce the desired result.
Big Data Challenges: Problems Will Persist:
While there are so many challenges that Big Data face, organizations and businesses need to find a way that can make the process of locating, extracting, arranging and then storing it easy.
With such a massive amount of data, Big Data challenges don’t seem to resolve soon.
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