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In today’s modern world technology is the beginning and the end, as well as everything in between for every enterprise in the market. From the smallest of the small business to the largest of the multinational corporations of the world, everything utilises technology for their day to day operations and in order to do that they use data. As the gradual rise of the Internet of Things has occurred with time along with the necessary connection of the digital ecosystem, almost every single thing that we remotely utilise in our daily business processes eventually produces vast amounts of data.

 

The data produced from every single process are in available in a wide variety of formats, volumes and styles, containing a wide range of information and with the passing of time they are produced at an extremely rapid scale, which only increases with time. However, in order to cultivate actionable insights all of this data has to be harnessed effectively so as to deliver the best of client experience. But the raw and constantly increasing amount of data cannot be effectively made useful unless it is modified to a useful compact format so as to keep it working and to make space for future data. Thus, the large amount of data has to be captured, or “ingested” into a central repository, such as an Enterprise Data Lake before any of the analytical processing, predictive modeling, or even reporting can happen.

 

Data Ingestion is absolutely essential for any process based technological system as the raw data is immense and unmanageable, even on a shorter term. It has to be ingested in order to be categorised and put into a repository from which it can be evoked and used in the future. The lack of this will only result in a data chaos. Moreover, the production of data is constant and continuous, which only leads to more and more data being churned out every second. If all of this data is not ingested into a repository, the entire system will overflow with superfluous data, leading to practically a breakdown, and stopping processes from even functioning. Therefore, it is of utmost importance.

 

The real challenge for organizations comes in the way of ingesting the data fast, while standardizing it, is. Without all the data being stored in one central repository which serves as the ultimate “source of truth”, the system is at a risk of creating disparate groups which perform analytics using incomplete and inaccurate and sometimes even opposing data sets, which leads to incorrect and inconsistent results, thereby slowing down the working. The ingestion has to happen constantly and rapidly enough to keep up with the production of data. This is where a third-party vendor comes of help. Using their own superior data ingestion tools, they can help ingest the data into useful format, capture metadata, track data lineage from its source to create a streamlined path of analytics, in an automated, governed and cost effective manner.

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