March 26, 2018

Five Challenges Implementing Big Data Projects

Five Challenges Implementing Big Data Projects


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In this competitive world, a company that makes an informed decision can produce a real result and improve their marketplace. Real results include increasing revenue, reducing cost, maintaining a relationship with the customer or even improving quality of product offered. All this can happen with the help of “Big Data.”

In today’s world, ‘Big Data’ refers to the extremely large volume of data, which computationally analyze to find out the trend in the market, pattern of consumption and even consumer behavior. Real-time availability of trends and patterns, help strategist to take informed decision to remain in the game.

“Big Data” are collectible from different sources ranging from social media websites to online shopping websites along with online payment websites, which can provide insights on what customers mostly spend on and how much. This helps to understand customer’s need and its capacity to spend on it.

However, every new technology comes with challenges as well that needs to be addressed to improve the use of it. “Big Data” is no different as there are challenges attached to the usage. If we don’t overcome these challenges, the technology would no longer be as useful as people thought about. Here are the following five challenges, which needs to be addressed to make the technology more productive.

1. Skills Building Capacity

“Big Data” system runs real fast that nobody can keep up with, as in a matter of months, frameworks, capabilities and new tools evolve and mature—resulting in skill gap, which can put the “Big Data” initiative in jeopardy. Lack of data management skills and data analytics skills among employees can easily impede the technology.

Data analytics is the most required skill for the technology as it would help to analyze the data and draw pattern and trends, which is useful for the businesses, otherwise, it is as useless as any set of numbers.

2. Expenditure

Where a large volume of data makes “Big Data” the best tool to devise a strategy, on the other hand, it also creates fuss as where to store such big data is? As growing volume and increased variety of data from new businesses and existing applications make matters more complicated. Of course, there are cloud services and servers to store such big and complicated data, but It incurs a huge cost. Please bear in mind, to draw a pattern and trend or forecast consumer behavior, a long stretch of data is required for data analysis.

Organizations need to manage their infrastructure to get value from big data without breaking the bank as storage of large volume of data can result in an unsustainable cost.

3. The Unexpected Data

Big data comes from a range of sources such as mobile apps, legacy applications, transactional systems, social media and website logs. This makes it even more complicated and sometimes inefficient to forecast the required infrastructure to process such data. A single non-routine event can suddenly change the workload and data volumes that nobody can predict.

Organizations are challenged by big data growing volume and a wide variety of sources on infrastructure/storage capacity. They also have to determine how they will address computing requirements and fluctuating storage to run system smoothly.

4. Vulnerability

As data goes big, security becomes a greater concern. Big data collects data from a variety of existing and new sources. Organizations struggle to retain control of data access, protect infrastructure and secure data assets. Eventually, they also need to determine how to ensure security without compromising on performance and agility. In some cases, government regulations are strict, and organizations must adhere to compliance requirements as well.

5. Making “Big Data” aProfitable Proposal

Despite, promising technology, making “Big Data” a business case with such challenges is a challenge itself. To get approval from higher management for infrastructure and capacity building is challenging task owing to the cost of the technology and return on it.

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