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Tackling Big Data Analytics Pain Points

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Big data is revolutionizing analytics, and can be of enormous value to businesses - but only if it's managed and analyzed successfully.

Big data comes in a variety of forms and structures. In recent years, big data analytics has had a significant impact on business decisions, and while it can be of immense vale, it does come with some pain points.

In this article, I will discuss those analytics pain points, but first, let’s focus on some characteristics of big data.

Big Data Characteristics

Big data can be defined by several characteristics:

  • Volume — The term big data itself refers to size, and volume refers to quantity of data. The size of data determines the value of the data to be considered as big data or not.
  • Velocity — The speed at which data is generated is known as velocity.
  • Veracity — This refers to correctness of data. The accuracy of analysis depends on the veracity of the source data.
  • Complexity — Massive amounts of data come from multiple sources, so data management becomes a difficult process.
  • Variety – An important thing to understand is the category to which big data belongs. This further helps in analyzing the data.
  • Variability — This factor refers to the inconsistency which the data can show. This further hampers the process of managing the data effectively.

Now let’s discuss some of the pain points.

Lack of Proper Path

If data comes from different sources, then there should be a proper and reliable path for handling massive data.

For better solutions, the path should offer insight into customer behavior. This is the foremost motivation for creating a flexible infrastructure for integrating front-end systems with back-end systems. As a result, it helps in keeping your system running.


Data Classification Issues

The analytics process should start when the data warehouse is loaded with massive amounts of data. It should be done by analyzing a subset of key business data. This analysis is done for meaningful patterns and trends.

Data should be classified correctly before storage. Randomly saving data can create further issues in analytics. As the data is large in volume, creating different sets and subsets could be the right option. This assists in creating trends for handling big data challenges.

Data Performance

Data should be handled effectively for performance and decisions should not be made without insights. We need our data to perform effectively for tracking demand, supply and profit for consistency. This data should be handled for real-time business insights.


Overload can occur when trying to keep large amounts of data sets and subsets. The key pain point here is to select which information is kept from different sources. Here, reliability is also an important factor while selecting which data to keep.

Some types of information are not needed for business and should be eliminated to avoid future complications. An overloading issue could be resolved if some tools are used by experts for making an insight to create a big data project success.

Analytical Tools

Our current analytical tools provide insights into prior performance, but tools are needed for providing future insights. Predictive tools could be optimal solutions in this case.

There is also a need to give analytical tool access to managers and other professionals. Expert guidance can boost the business to a higher level. This leads to proper insight with less assistance given for IT support.

Right Person at the Right Place

The motto for many HR departments is “the right person at the right place,” and it is same for big data as well. Provide the data and analytics access to right person. This could assist in getting proper insights for predictions related to risk, costs, promotions, etc. and could convert analytics into actions.

The data collected by companies through emails, sales, tracking and cookies are of no use if you can’t analyze it properly. Analysis is important for providing what the consumer wants.

Forms of Data

There is a large amount of data collected, which can be structured or unstructured and from varying sources. Improper handling of data and lack of awareness about what to save and where to save it can hamper the handling of big data. The usage of each form of data should be known to the person handling it.

Unstructured Data

Data coming from different sources can have an unstructured form. It could contain data which is not organized in a standard, predefined manner. For example, emails, system logs, word processing documents and other business documents can all be data sources.

The challenge is to store and analyze this data correctly. A survey stated that 80% of the data generated daily is unstructured.


Data in an enterprise is difficult to manage due to its large size and the need for higher processing capacity. Traditional databases can’t process this efficiently. An organization can make better decisions if it can successfully manage and analyze massive data with ease.

It could be petabytes of data storing details of employees of an organization from different sources. If not organized properly, it could become difficult to use. The situation is worsened if even more unstructured data is coming in from different sources.

Big data has the potential to improve business decisions and analytics. Today banking, services, media and communications are investing in big data. The above pain points should be taken into consideration while working with massive amounts of data.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…