3 Common Challenges of Corporate Data

challenges of corporate data

Over the past few years, hardware has become quite cheap and powerful. This has lead to huge advancements in fields like Machine Learning, Artificial Intelligence and also Cryptography. With the ever increasing economy, it has been difficult for corporates to scale in traditional ways. As a result, they are now focusing on using technology as a tool to help them scale better.

One of the most important parts of driving any corporation is to take the right decisions. Decisions are driven by data. Since the data is growing at a huge pace, it is getting increasingly difficult to manually analyze the data. So, many corporations are now adopting data science and machine learning to make sense of their data in order to extract information from it. For any machine learning algorithm to be successfully executed on any data, the data has to be in a certain format and structure. Corporations are facing challenges in applying machine learning simply because their data is unstructured and poorly formatted. They are facing some major challenges in applying machine learning on their data. Here are the 3 of them:

Fragmented data

Let us consider a large organization like a bank. Banks typically have a large number of branches, distributed across the country. The branch is the element of the bank that is customer facing, so each bank branch generates a lot of customer data – deposits, loans, etc. However, due to legacy infrastructure, this data in most of the banks isn’t centralized. The data, as a result, is quite fragmented and is distributed across various branches. For someone who wants to write a Machine Learning algorithm to compare the overall growth of the bank, it will be very difficult to assemble this data at one central server and perform computations on it.

Huge volume of data

Another big issue that arises with such large corporates is that the data volume is humongous. Consider for instance the State Bank of India (SBI). SBI has more than 700 million bank accounts. This is about twice the population of united states. The storage and processing of such a huge amount of data brings a lot of overhead in terms of infrastructure and costs. Corporations have to, therefore, manage huge clouds for storing this data. This becomes a big challenge.

Poor quality of data

The standards across various units (for instance bank branches) aren’t well defined, so each unit tends to generate a different kind of data. This becomes a major challenge in managing the data since the data isn’t consistent at all. This can be attributed to the fact that the regulations often differ within a country and so, different states may have different requirements/expectations from the customers due to which some details may be needed in a certain area which may be optional in other areas. As a result, there isn’t any consistency in data at all. This leads to poor quality of data which isn’t ideal for training machine learning algorithms.

Due to these issues, corporations face severe challenges in extracting information from the data. Often, the data is never even used, which leads to a huge loss. Solutions to this issue will be a game changer in the corporate world.

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