Big Data

What Are Important Problems in the Field of Big Data?

What Are Important Problems in the Field of Big Data?

What Are Important Problems in the Field of Big Data?

To shed light on the most pressing issues in the field of big data, we’ve gathered insights from ten industry leaders, including CEOs, Founders, and other professionals. From overcoming high storage costs and unstructured data to maintaining data integrity against manipulation, these experts share their perspectives on the most important problems in big data.

  • Overcoming High Storage Costs and Unstructured Data
  • Addressing Poor Data Quality and Data Silos
  • Prioritizing Data Privacy and Security
  • Selecting the Right Analytical Method
  • Navigating Data Ownership and Ethical Considerations
  • Tackling Data Integration Challenges
  • Choosing the Appropriate Big Data Solution
  • Ensuring Data Quality and Accuracy
  • Dealing with Long System Response Times
  • Maintaining Data Integrity Against Manipulation


Overcoming High Storage Costs and Unstructured Data

The main problems of big data are connected to the high cost of data storage and the large amount of unstructured information gathered at once. These records require certain conditions and space, which sooner or later end. 

Our team actively engaged in solving this issue and successfully arrived at an excellent solution. Archiving outdated information that wasn’t in use for a long time turned out to be the perfect option. Thus, space on the cloud server is freed up and it is not overloaded.

Vladislav Podolyako, Founder and CEO, Folderly


Addressing Poor Data Quality and Data Silos

The number of times I’ve seen big-data solutions filled with unstructured bits of data in various formats, full of duplicates or just generally of poor quality, is depressingly high. This makes any sort of insight that you would pull out of the mess essentially worthless, especially if you also have to deal with data silos within your organization. It’s a good way to get errors due to numbers from different departments not syncing together. 

This is an exponential problem, as the more data you collect, the worse the general quality gets in the long run without a significant commitment to clean data.

Dragos Badea, CEO, Yarooms


Prioritizing Data Privacy and Security

In the dynamic field of big data, ensuring data privacy and security emerges as a significant challenge. With the rapid growth of data volumes and complexities, concerns escalate regarding unauthorized access, potential data breaches, and the improper handling of sensitive information. Upholding personal data privacy and implementing robust security measures become pivotal in fostering trust in data-driven technologies.

Khurram Mir, Founder and Chief Marketing Officer, Kualitatem Inc.


Selecting the Right Analytical Method

Choosing the right analytical method is the biggest problem in the field of big data. Developing sophisticated analytical techniques to extract meaningful insights from vast and complex datasets is a central problem.

Choosing the proper analytical method and implementing it relatively quickly includes developing advanced analytical techniques, machine-learning algorithms, and visualization methods to effectively uncover patterns and insights hidden within large and complex datasets. Additionally, the method determines the way results might be presented. Without meaningful presentation and understandable visualization, the results might be useless.

The more experience one has, the better the analytical method will be chosen. If a poor method is used, there is a high chance for biased results, and that is one of the most risky scenarios, no matter the field or industry.

Irina Poddubnaia, CEO, Founder, TrackMage


Navigating Data Ownership and Ethical Considerations

There is a big question mark when it comes to data ownership and control. In this day and age of cybersecurity attacks, there are numerous ethical considerations over the use and management of big data, especially since sensitive user information is involved. 

It’s no secret that big data can be exploited. If misused, these big data sets can be used by companies and other invested organizations for profit through marketing use, forwarding agendas, or even political manipulation.

Carolyn James, Consultant and Trainer, Website Insights


Tackling Data Integration Challenges

Despite all the technological advancements in big data, data integration continues to be a challenge. It involves gathering, transforming, and consolidating data from multiple sources to create a unified view. 

However, this process is not straightforward because of the unique structures, formats, and qualities of each dataset. We also have complexities arising from the sheer volume of data generated from social media, IoT devices, and customer interactions.

Karl Robinson, CEO, Logicata


Choosing the Appropriate Big Data Solution

Choosing the correct big-data solution for your business’s needs is a significant difficulty when dealing with large data. Because your big-data tool is designed to help you reduce big-data problems, it would be a shame if it became a problem in and of itself! To address this difficulty, it is best to conduct a thorough study before diving into a specific tool. 

Also, examine the level of assistance provided by the tools you’re evaluating. If a demo is available, take advantage of it because it will offer you an idea of how the big-data solution will operate specifically for your company.

Tim Parker, Director, Syntax Integration


Ensuring Data Quality and Accuracy

When it comes to big data, one of the most important challenges is ensuring data quality and accuracy. With the enormous volume of data being generated every day, it’s crucial to have reliable and trustworthy information to make informed decisions. 

Data quality issues can arise from various sources, such as incomplete or inconsistent data, data duplication, or outdated information. These problems can impact the reliability and validity of analytical insights and hinder businesses from gaining meaningful insights.

At Click Intelligence, we prioritize data quality by implementing rigorous data-validation processes, using advanced algorithms to detect and correct errors, and regularly updating our databases. By ensuring the accuracy and integrity of our data, we can provide our clients with reliable insights that drive successful marketing strategies.

Simon Brisk, Director, Click Intelligence Ltd


Dealing with Long System Response Times

As a PR agency professional, one of the key challenges we face in the field of big data is dealing with long system response times. Timely access to data is crucial for tracking media mentions, measuring campaign performance, and generating comprehensive reports for our clients. Delays in data processing can hinder our ability to provide timely insights and meet client deadlines.

Matias Rodsevich, CEO, PRLab


Maintaining Data Integrity Against Manipulation

I’ve personally experienced the significance of data integrity in the age of big data. I remember when we incorporated data analytics to forecast demand and inventory needs. Unfortunately, we became a victim of data manipulation by a competitor. They inflated traffic on our online store with fake IP addresses, leading our algorithms to misinterpret it as genuine customer interest. 

Consequently, we overestimated demand, resulting in overstocked inventory and substantial financial loss. This incident emphasized how vulnerable businesses can be to fake data generation. Accurate data forms the bedrock of informed decisions; its distortion can mislead strategic planning and impact operational efficiency. 

Hence, addressing this vulnerability remains one of the most critical issues in the field of big data.

Lucas Riphagen, Co-Owner, TriActiveUSA


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