Learn How Large File Data Ingestion can combat the Challenges posed by 4Vs of Big Data

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The world has witnessed an unprecedented pace of big data growth in recent times. Statistics says that every day a whopping number of 2.5 quintillion bytes of data are created. In the last two years alone, 90% of data has been created through sensors (used to garner information related to climate), purchase transaction records or details, digital pictures or videos, GPS signals on the phone, social media posts, and more. It is estimated that more than ten thousand libraries of Congress can be filled in with large volumes of data. 

Data has grown not only in terms of volume but also variety. Enterprises that rely on a number of processes, systems, or technologies to execute myriad B2B operations produce large data streams of various types including, marketing data (that includes data produced from the market segmentation, website log data, traffic data, and more), operations data (that includes data produced from various operations such as competitor analytics, sales data, online transactions, and much more), and consumer data (that includes data produced from sources such as banking records, banking data, stock market transactions, employee benefits, insurance claims, etc.)

Big Data Proliferation- The 4Vs

The sudden evolution of data has given rise to big data. On processing big data optimally, organizations can extract valuable insights to make smarter business decisions. Further, big data helps organizations understand customer needs, playing a central role in improving their branding and reducing churn. However, processing Big data is easier said than done, especially due to the presence of 4 components, the 4Vs:

Volume: Volume includes the size of data. It is measured in GB, Exabytes, and TB. Large volumes of data are impossible to tackle, especially using conventional methods. 



Variety: Data can be of different types including, heterogeneous, unstructured, and structured data. Data such as images, audios, videos, etc. come under this category. 

Velocity: The frequency of data to be processed is the velocity of data. Higher frequency of data causes a disturbance in the processing speed of enterprise systems, giving rise to downtimes and breakdowns. 

Veracity: Veracity is the degree to which is precise and accurate. Large streams of data are often imprecise, uncertain, and difficult to trust. 

Owing to these 4Vs of Big data, the quality and speed of the processing are severely impacted. As the quality and speed are compromised, enterprises can experience a breakdown of data flows and application failures that can result in data losses and delays in important business operations. In addition, the ability of businesses to make decisions decreases as a lot of time, money, and effort goes into waste for discovering, extracting, preparing, and managing rogue data sets. 

Large File Ingestion

Now processing such enormous big data growth (which is further complicated by 4Vs) is a mammoth task. Statistics confirm that most companies are able to analyze only 12% of the data they have. Lack of proper technology is the primary reason why enterprises fail to process such enormous data. The role of large file ingestion comes into play here. 

Companies need to have access to an adept automated data Ingestion Software to effectively ingest big data for delivering useful insights. Automated data ingestion is far better than a manual one and makes the process far simpler and faster. It offers a number of benefits such as:

1. Companies with manual data ingestion system in place are unable to achieve the desired goal within the set time frame, ultimately losing their competitive edge. The delays of projects can be minimized by relying on automated data ingestion platforms. It helps to deliver projects on time, improving companies’ time-to-market goals. 



2. Adopting an automated data ingestion approach can allow enterprises to scale up. This is because, these data ingestion techniques make the operation smoother and better, offering scalability. 

3. Data is extremely crucial, and not employing correct techniques to ingest it lays a huge risk on companies’ rate of growth and innovation. Automated data ingestion mechanism mitigates risks of slowing down and increased discrepancies caused due to human intervention.

Use integration platforms with an automated data ingestion mechanism to savor these benefits and more and bid goodbye to the conventional mechanisms now!

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