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Adamo Software
Adamo Software

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How to deal with Big data challenges

Vast amounts of data from business are being generated and wrapped in a huge data package which is Big Data. Which drives the concern of big data challenges?

Currently, Big Data is considered the next revolution in the field of Software Development. Software companies are now making huge investments in big data analytics due to the advantages of data in today's development processes. Additionally, Big Data can leverage a custom software system to store and process data; it also helps the business keep up with travel, healthcare app development trends and predict the user's needs.

However, only 37.1% of the companies successfully applied Big Data solutions to their business, according to a NewVantage Partners survey. Commonly, many outsourcing software companies get stuck at the initial stage of their Big Data projects due to a lack of awareness of the Big Data challenges. In this article, we will discuss the challenges and the solution to tackle Big Data issues.

Overview of big data service

Generally, Big Data is a set of data that is huge in volume and growing exponentially with time. This set of data usually are difficult to process when using traditional data processing applications. With the use of Big Data analytics, multiple operations can be performed on a single platform. The tools can store a huge amount of data, preprocess, and analyze the data and then visualize it. Big Data is currently integrated into a custom software system to help software developers analyze every component of their software from the user perspective.

In practice, Big Data is classified into three types:

  • Structured Data
  • Unstructured Data
  • Semi-Structured Data

Structured Data

In the Structured type, data can be processed, stored, and retrieved in a fixed format. Every piece of information is grouped into rows and columns, which is much easier for a program to sort through and collect data. Structured data is the simplest type, which requires less preparation before processing, and each field in structured data is discrete and can be assessed separately or jointly along with data from other areas.

Unstructured Data

On the other hand, unstructured type refers to the data that lacks a specific form or design, which makes it difficult and time-consuming to analyze and process. Unstructured data is placed in a lake that contains the raw format of the data. Nowadays, organizations have a wealth of data available; however, they are difficult to derive value out of it since it is the raw form or unstructured format. That's why the ability to extract value from unstructured is one of the significant impacts behind the substantial growth in Big Data.

Semi-Structured Data

Semi-structured data is a form of structured data; however, it does not conform with the formal structure of data models. The semi-Structured type splits the gap between structured and unstructured data, it can be more challenging to work, but the limits and complexity in structured data don't exist. Still, semi-structured data is considerably easier to analyze than unstructured data.

Challenges of Big Data analytics

Undoubtedly, Big Data gives users the ability to aggregate various data from hundreds of sources in real-time. However, the fact is only a small number of organizations described their big data project as successful. From the user perspective, adopting this software development means they take the Big Data challenges below:

Handling Voluminous Data

The modern world is the world of sharing and connecting; as a result, the amount of data will be sufficient to fill a stack of tablets, which is equivalent to 6.6 times the distance from the earth to the moon, according to the IDC report-in 2020.

Unsurprisingly, Big Data management challenges are becoming more and more dramatic. Additionally, the rise of complex data sources such as the Internet of Thing (IoT), video, social media, and smart devices, which drive the online business transaction up to 450 billion per day; and the number of connected smart devices reaching 50 billion in just five years will generate a massive amount of data.

Big data security problems

Surely, Big Data involves dealing with data from numerous sources, and these sources mostly use a distinct format and collection method, which will increase the chances of hackers exploiting a vulnerability in the system. Due to the criticality of the data, a minor attack from cybercrimes can result in huge losses. Especially in application development, cybercrimes' attack leads to malware infections, data breaches, and service disruptions.

Confusion while Big Data tool selection
Users tend to be confused while selecting the best tool for Big Data analysis and storage, and sometimes they are unable to choose the right one. Selecting an ineffective analysis tool is the big data problems might cost the companies tons of money but not receive any advantages.

Infrastructure failures

Inevitably, Big Data contains huge amounts of data is vital for a company to operate requires a vast and complex hardware infrastructure. And the more complex of the data, the more advanced hardware systems will be needed. The challenges appear when storing those complex data that the company gathered. Onces the system fails, it affects the storing data process and makes it more difficult to work with.

Solutions to overcome Big Data challenges

A massive amount of data generated every day has motivated an emergent need for software development to organize, examine, and handle voluminous data efficiently. And to produce a beneficial conclusion from this huge amount of data, integrated Data Mining in Big Data is required.

Data Mining solution in Big Data

Data Mining in Big Data helps organizations better turn the raw data into useful data. Additionally, Data Mining provides businesses to resolving Big Data problems and issues in this challenging information age include:

  • Gather reliable data and information among vast amounts of data
  • Deliver selected and valuable data for data scientists to quickly initiate automated predictions of trends and behavior, also discover hidden patterns.
  • Detect fraud, build risk models and improve product safety

Cloud-based solution

As Big Data involves huge amounts of data which some times, system might fails due to the data complexity. In order to maintain the continuity and avoid sudden faults can happen, any Big Data storage should be backed in a cloud-based service to continue the fluency and the user can work even if failure happened.

Big data contains the most sensitive information of the business, providing a critical competitive differentiator. And, to enhance security, any piece of data information in the system must be encrypted and stored in two different tables.

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