What are the big data challenges that businesses confront, and how can they be addressed? In the age of big data, the volume, velocity, and variety of data being generated have created a new set of challenges for businesses and organizations. From managing data to analysis and storage, big data presents a unique set of obstacles that require innovative solutions.
Implementing best practices for big data, following latest trends, and understanding the main big data components can help organization overcoming the challenges associated with it and increase your big data management project’s success while enjoying all the rewards.
The challenges of big data are:
Challenge #1- Sheer Volume of Data
Every day, it is estimated that 2.5 quintillion bytes of data are created, and guess what? The majority of this data is generated by various types of enterprises. As a result, the organization now faces new challenges in terms of obtaining, maintaining, and generating value from data.
With the advancement of digital technologies and the widespread use of mobiles and tablets, the amount of data being generated is growing at an exponential rate. Traditional data management systems can’t cope with this high volume of data.
Organizations need to have a robust and scalable infrastructure to be able to manage and store such large volumes which usually involves implementing special techniques and technologies such as Hadoop or cloud-based storage solutions.
Successfully addressing these challenges requires a combination of scalable infrastructure, distributed computing frameworks, efficient data transfer mechanisms, and effective data curation strategies.
Challenge #2- Data Silos
Data silos pose significant challenges for big data due to the inability to consolidate all data in one source for data analysis. With this issue in mind, the decision-making process that the higher management of your organization is seeking from the data stored on your premises will be incomplete. This negatively impact the ability to achieve a holistic view of the organization, inhibiting the potential for gaining meaningful insights and making data-driven decisions.
In addition, having multiple data sources scattered across your organization and departments will create redundancy in storage hardware thus increasing the cost.
To effectively leverage the power of big data, one of the most critical tasks to do is to break down these silos and help provide a workplace where data is managed in a centralized way with maximum security.
Challenge #3- Data Quality
Data quality is one of the most critical factors that can positively or negatively impact the reliability and trustworthiness of the insights that should be derived from your data.
However, it is super hard to achieve high data quality with the current growth rate and the multiple sources available. Organizations should overcome some barriers related to data inconsistency, incompleteness, inaccuracies, and duplications to ensure that decisions are made based on high quality data.
Poor data quality can lead to erroneous conclusions, flawed predictions, and unreliable insights which can have severe consequences for organizations relying on big data analytics.
Addressing these challenges requires implementing robust data governance practices, data cleansing techniques, data validation processes, and data quality monitoring frameworks to ensure the integrity and usability of the data. For more info about this topic, you can check out this guide describing the top tips to improve data quality.
Challenge #4- Data Variety
Unlike traditional data sources that primarily dealt with structured data, big data encompasses a wide range of formats including text, images, videos, social media interactions, and more. Managing and analyzing this mix requires sophisticated tools capable of handling various data types.
The challenge lies not only in the sheer volume and velocity but also in the ability to extract meaningful insights from these disparate sources. Organizations must navigate the complexities of integrating and interpreting diverse data formats to derive comprehensive and accurate conclusions.
Challenge #5- Data Integration
Data integration is one of the key pillars to overcome in order to have an effective big data implementation. The main purpose is to have the ability to consolidate data across multiple repositories and systems in one of the best business intelligence tools to be able to slice and dice it for advanced analytics.
This might require lots of efforts at the beginning of the implementation but without it, you will not be able to manage the complete view of your data.
Challenge #6- Lack of Talent
Big data requires specialized skillset and expertise to effectively harness the potential of large-scale data. It involves working with complex data sets, the usage of advanced technologies and techniques, experience with machine learning and artificial intelligence.
The demand for data scientists, data engineers, and data analysts surpasses the available talent pool, leading to fierce competition for skilled individuals. Additionally, these skilled employees need to have the needed patient to always stay up to date in order to remain valuable.
Organizations are struggling to find and retain these qualified individuals who are ready to help store and analyze their data to identify opportunities and gain insights about their daily operations. Bridging the talent gap necessitates investments in training programs, educational initiatives, and creating an environment that attracts and nurtures data-driven talent.
In 2020 the number of data science job postings exceeded the number of people searching for data science jobs by a ratio of 3 to 1.
Challenge #7- Security Concerns
As organizations accumulate massive datasets, the risk of unauthorized access, data breaches, and cyber threats escalates. Moreover, the diverse sources and formats of Big Data introduce complexities in implementing robust security measures.
Protecting data integrity and ensuring confidentiality become high priority tasks particularly when dealing with personally identifiable information and proprietary business insights.
Challenge #8- Data Governance
Data governance is in charge of establishing rules and regulations for an organization’s information state. Due to the nature of big data, the ability to govern these large volumes of data become more and more complex.
Almost consistently a new rule or industry specific regulations are being added by governments or organizations to provide a roadmap on the best methods to manage customers and users’ information with care.
Read more about the difference between data management and data governance.
Challenge #9- Data Automation
Data automation presents a significant challenge for big data due to the complexity and scale of data processing tasks. Big data environments involve enormous volumes of data that require efficient and timely processing to derive insights and drive decision-making. Automation plays a crucial role in streamlining data-related tasks, such as data ingestion, data integration, data transformation, and data analysis.
Taking this into consideration, automating data related tasks is extremely efficient but challenging to implement as it involves the availability of scalable infrastructure and technologies to drive the data processing workflows.
Challenge #10- Data Analysis
If you want to reap all the benefits of big data, your organization should give priority to analyzing big data because it represents huge opportunities to better understand how your business is operating and how your customers are interacting with your brand. However, having the ability to process and visualize these huge number of data is certainly not an easy task.
It requires advanced skillset, technologies, and infrastructure to be able to effectively analyze data without any impediments.
Challenge #11- Going from Unstructured to Structured Data
According to existing studies, more than 80% of data collected is unstructured, which is a major problem.
Unstructured data includes text documents, social media posts, images, videos, and sensor data, among others, which lack predefined organization or format. Extracting valuable insights from unstructured data requires transforming it into a structured format that can be easily processed and analyzed. However, this conversion process poses several challenges.
