Big Data characteristics and pain points

Big Data pain points

Big Data characteristics and pain points

Overview

Big data is based on three most important characteristics, known as volume, velocity and veracity. It comes in different forms and structure. Big data analytics is having significant impact in business decision. But it comes with some pain points.

In this article, I will talk about those analytics pain points. But, let us focus on some Big Data characteristics before moving further.








Big Data Characteristics

Volume – The name Big Data itself says size and volume says 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 – It refers to correctness of data. The accuracy of analysis depends on the veracity of the source data.

Complexity – Massive amounts of data comes from multiple sources, so data management becomes a difficult process.

Variety – An important thing to be known by analysts 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 being managing the data effectively.

Must read –  Use of Big Data in Smart City Projects 

Big Data Uses

  • Understanding customer behavior, their buying pattern and predicting their buying habits.
  • Trading today is done using data algorithms for selling and buying sharing.  High-Frequency Trading (HFT) also uses Big Data.
  • Real time traffic and weather information is also provided by Big Data tools. This helps in transforming the cities into smarter ones. Smart cities have the best transport infrastructure due to big data analytics. Numbers of cities are switching towards this approach.
  • Big Data can be used individually like the data generated from smart watches, Google Glass etc.
  • It is also used in professional sports for tracking performance of players, improving statistics management etc.
  • Optimizing business process, stocks can be processed based on predictions.
  • Scientific research and experiments gets benefitted as data generated by thousands of computers across data centers is managed by Big Data.
  • Big Data also has its part in optimizing machines, like Google’s self-driving car uses big data tools for operations. Its tools help in optimizing the performance of computers and data warehouses with optimization of energy grids as well.
  • Healthcare facilities get improved as Big Data predicts disease patterns, monitors and helps in predicting epidemics etc. It can also integrate data from medical records and encode DNA in minutes.
  • Financial Institutions use Big Data for detecting fraudulent practices. For Example, credit card companies detecting fraudulent transactions.
  • It is also used in detecting and preventing cyber-attacks
  • Big Data tools are also used by police force for predicting criminal activity, catching criminals, tracking their locations and searching relevant data from different documents. Here, the data is unstructured as it contains email-id, word processing documents etc.
  • Big Data is also used in monitoring social media engagements like analyzing tweets, posts, followers, contributors etc.
Also read –  How Open Data Platform simplifies Hadoop adoption?









Now let us discuss the pain points.Pain Points

Lack of proper path

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

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

Data Classification issues

Analytics process should be started when 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 pattern and trends.

Data should be classified correctly before storage. Randomly saving data can further create issues in analytics. As the data is in large numbers, so 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 the decision should not be made without insights. We need our data to perform effectively for tracking demand, supply, and profit for consistency.  For, this data should be handled for real-time business insights.

Must read – Big Data Success in the Cloud Platform 

Overload

Overload comes when there is a challenge to keep large number 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 while selecting which data to be kept.

Some type of information is not needed for business and should be eliminated to avoid future issues. Overloading issue could be resolved if some tools are used by experts for making an insight to create big data project a 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 professional. Expert guidance can boost the business at a higher scale. This leads to proper insight with less assistance given for IT support.Right person at right place

First line under staffing states “right person at right place” and it is same for big data also.  Provide the data and analytics access to right person. This could assist in getting proper insights for predictions related to risk, costs, promotion etc. and could convert analytics into actions.

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

Also read – SQL on Hadoop – How does it work? 

Forms of data

There is large number of data with different for which could be structured or unstructured and that too from different sources. Improper handling of data and lack of awareness about what to save where can hamper the handling of big data. The usage of each form of data should be known to the person handling it.Unstructured Data

The data comes from different sources and has unstructured form. It could contain data which is not organized in a pre-defined manner. For Example, emails, system logs, word processing documents and other business documents.

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

Must read  – Measuring the ROI in Hadoop adoption 

Must read – Explore Big data articles 









Summary

Data in an enterprise are structured and unstructured which are difficult to manage due to its large size and higher processing capacity. Traditional database couldn’t process this efficiently. Big Data is a technology used when there is a need to manage this data. An organization can take better decisions if they can successfully manage and analyze massive data easily.

It could be petabytes of data storing details of employees of an organization from different sources. If not organized properly, it could get difficult to use. The situation gets worsened if more amounts of unstructured data come from different sources.

Big Data approach if done correctly can help in handling large number of data. It has the potential to improve business decisions and analytics. Today banking, services, media and communications are investing in Big Data. The above given pain points should be taken into consideration while working with massive amounts of data.

 

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