5 Pitfalls to Avoid in Big Data Projects

Big Data has pervaded the business world and has made its presence felt in every sphere of the industry. There is a rush among organisations to embrace Big Data in order to gain benefit out of this phenomenon. Most people are of the idea that Big Data is all about huge volumes of data.

They have the impression that with the advancement in technology and the upgradation of their infrastructure, they can make a mark with Big Data analytics and improve their business.

However, there are a few pitfalls that they must avoid while they are on their way to incorporate Big Data into their business. The success of their Big Data endeavours depends on whether they are aware of these pitfalls and how they avoid them.

Desk by André Freitas

Here are the 5 pitfalls that must be avoided in Big Data projects:

1. Underestimating data relevance

Big Data is present around us at all times in various sizes and forms. Each of the data sets that exist around us has some relevance. Businesses need to find out the relevance of each data set in order to attain success in the field of analytics.

Today data is available as structured data, semi-structured data and unstructured data. Businesses need to derive the relevance from the analysis of all these categories of data. Without proper relevance the very purpose of analysis will be lost.

So, data relevance must be given prime importance in Big Data projects.

2. Ignoring data quality

Big Data analysis depends on data received from various data sources. But, poor quality of data can hamper analysis and in turn, the business improvement goals of any company. Due to the presence of semi-structured and unstructured data sets in Big Data, the quality of data may be lower than desired.

Enterprises can improve the quality before processing the data. There are several ways through which companies and enterprises can improve the quality of data before it is processed. Such efforts can improve the overall results of analytics and help the company to achieve its objective through application of analytics.

3. Overlooking data contextualization

Contextualization of data is of high importance in Big Data analysis. The absence of proper contextualization can lead to a high degree of inaccuracy. This can produce distorted results in analysis, which is not at all the desired consequence of data analytics.

Contextualization is one of the essential steps that all enterprises must follow while dealing with analysis of data. Technology has advanced a great degree today and can be utilized for contextualization of data. Using proper methods for contextualization can be beneficial to increase the accuracy of data and enhance the chances of deriving better results from data processing.

4. Misjudging data complexity

Big Data comprises of various data sets, each layered with complexity that is not obvious to the end-user. The complexity exists within the data. The structure of data, its content and metadata are a few factors that enhance the complexity of data to a high level.

It is simply not feasible to develop a solution for a data set without first fully understanding the complexity issue.

The degree of complexity in data increases when metadata appears along with data. Even multiple formats can also be a cause for concern during the processing of data.

Hence, it is very important to address the complexity issue of data in order to achieve accurate results from analytics.

5. Disregarding data preparation

Processing of Big Data is not an easy thing to do. Companies must prepare the data before it can be processed. Even while the processing cycles are in progress, they must be ready to provide any additional input required by the process. Without proper preparation of the data the end-result of analytics can be distorted from reality.

Every company or enterprise generally follows some methods to prepare the data for processing. However, these methods may not have the ability to prepare the data entirely for processing. As such, companies need to pay extra attention to data preparation.

Enough time must be given to prepare the data for downstream processing as well.

These are some of the pitfalls of Big Data that must be avoided in order to benefit from data analytics. There are some other risks as well that must be handled by companies engaged in Big Data analysis. However, with proper planning and careful preparation all the pitfalls can be avoided.

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