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Top 15 Challenges of Big Data Analytics: An Overview

Challenges /Issues of Big Data Analytics: In this tutorial, we will learn and analyse the different real challenges associated with big data analytics. By IncludeHelp Last updated : June 14, 2023

Most companies are using big data analytics to improve their decision-making capacity, enhance accountabilities, increase the productivity of employees, monitor performance, etc. Using big data analytics in your business is very important for the above reasons. While using it there may be some challenges like data volume, data variety, data velocity, data veracity, data privacy and security, uncertainty of data management, data quality, disparate data sources, technical expertise, scalability, etc., that you have to recognize, investigate the root causes, and find the perfect solution to fix them. Here, we will discuss some of the most common 15 big data analytics challenges.

Challenges in Big Data Analytics / Issues in Big Data Analytics

There are different challenges associated with big data; some common challenges are as:

Data volume

Dealing with large volumes of data is one of the primary challenges in big data analytics. A large size dataset can strain storage, processing, and analysis efficiently.

Data variety

Big data includes structured, unstructured, and semi-structured data from different sources such as social media, sensor data, log files, etc. To analyze this data, we need to integrate different data types and formats in a common form which is more challenging.

Data velocity

In today's digital world, data is generated at high speed. Real-time of streaming data sources pose significant challenges.

Data veracity

Analytics of big data deals with multiple sources, which may contain errors, inconsistencies, or duplications.

Data privacy and security

Ensuring data privacy, compliance with regulations, and protecting against data breaches are critical challenges. Once businesses discover how to utilize Big Data, it opens opportunities and possibilities. However, it also entails potential challenges like diverse data sources are utilized by the Big Data instruments used for storage and analysis. This inevitably increases the risk of data exposure, making it vulnerable. Thus, the increase in the volume of data heightens privacy and security concerns.

Uncertainty of data management

Find out which technology will be best suited to work on Big Data analytics is more challenging.

Data quality

Large quantities of unstructured and structured data in their native format are typically stored in data lakes and warehouses. When a data lake or warehouse attempts to integrate unstructured, inconsistent data from numerous sources, it confronts defects. Missing data, inconsistent data, logic conflicts, and duplicate data all result in data quality challenges.

Disparate data sources

As data sets are big in size and diverse; it's a bigger challenge to prepare them for analysis.

Technical expertise

To work with Big Data; another challenge is expertise in data science, programming language, technical concepts, Data Base, MongoDB etc are needed. Due to this, there is a shortage of working professionals in this field.

Scalability

As the volume and complexity of data increase, scalability becomes a significant challenge. Traditional data analytics systems may struggle to handle the massive computational and storage requirements. Implementing scalable infrastructure, distributed computing frameworks, and parallel processing techniques are essential for efficient big data analytics.

Skill Gap

Big data analytics requires expertise in data science, machine learning, statistics, and programming. However, there is a shortage of skilled professionals with the necessary knowledge and experience. Organizations face challenges in recruiting and retaining data scientists and analysts capable of extracting meaningful insights from large datasets.

Ethical considerations

Big data analytics raises ethical concerns regarding data usage, fairness, and bias. Analyzing large datasets may unintentionally lead to biased results or discriminatory practices. Organizations must address these ethical considerations to ensure the responsible and unbiased use of data.

Cost management

Big data analytics can be resource-intensive, requiring substantial investments in hardware, software, storage, and skilled personnel. Managing and optimizing costs associated with data storage, processing, and analytics infrastructure can be a challenge for organizations, especially smaller ones with limited budgets.

Data governance

Big data requires proper governance to ensure data quality, security, compliance, and ethical use. Establishing data governance policies, frameworks, and processes to manage data effectively and responsibly can be a complex and ongoing challenge.

Analytical complexity

Analyzing big data requires advanced analytics techniques, such as machine learning, data mining, and predictive modeling. Developing and implementing complex algorithms, managing computational resources, and extracting meaningful insights from massive datasets can be demanding tasks.