Guest Blog

December 24, 2022

A recent big data problem, its impact, and a possible solution for it

In this blog, we will discuss some of the most recurring big data problems and their proposed solutions for organizations.

 

The global AI market is projected to grow at a compound annual growth rate (CAGR) of 33% through 2027, drawing upon strength in cloud-computing applications and the rise in connected smart devices. The problem is that algorithms can absorb and perpetuate racial, gender, ethnic and other social inequalities and deploy them at scale- especially in customer experience and sales environments where AI usage is really taking off.

Data specialists realize that AI bias is simply a data quality problem, and AI systems should be subject to this same level of process control as an automobile rolling off an assembly line. AI bias can be solved with robust automated processes around their AI systems to make them more accountable to stakeholders.
 

One of the most challenging difficulties of Big Data is ensuring the safety of these vast stores of information. Oftentimes, businesses are so delving into, archiving, and analyzing their data sets that they neglect to ensure the security of that data until afterward.

Since exposed data repositories might attract malevolent hackers, this is usually not a wise choice. It has been estimated that the average cost to a business due to a stolen record or knowledge breach is $3.71 million. Josh Thill, Founder of Thrive Engine 

 

Read more about data breaches in this blog

 

When trying to choose the most effective instrument for the massive management of information and data storage, which is easier, HBase or Cassandra, as a data storage platform? How does Spark compare to Hadoop MapReduce when it comes to data analytics and storage? These are concerns for businesses, and sometimes they can’t get the answers they need. Because of this, they frequently go for the wrong technologies and make poor choices. It causes a loss of resources such as time, energy, and manpower. 

 

A company’s data can originate from a wide variety of places, including employee blogs, ERP systems, customer databases, financial reports, emails, presentations, and reports made by the company’s personnel. It may be difficult to compile all this information into usable reports. Companies frequently overlook this area. Perfect for analysis, reporting, and business intelligence when integrated data is essential. Don Evans, CEO of Crewe Foundation 

 

Experts forecast that firms will lose $6 trillion to cybercrime by 2021 as data leak is on the increase. Several advanced analytics platforms fall short in protecting crucial data, putting organizations at risk of litigation and undermining consumer confidence. To safeguard the crucial data about your consumers in today’s environment, you need a data platform you can trust.

The best data integration technologies rely on cloud computing so that data is always maintained in a safe and healthy environment. Aimee Howard, Quality Assessor at Aerospheres 

 

Research says that we, as humans, generate 2.5 quintillion bytes worth of data on a daily basis. Quintillion. That’s more than the number of stars in our universe. With this data comes the opportunity to understand human behavior, improve customer experiences and unlock powerful insights that could never be seen before. 

One of the biggest issues plaguing big data is how to effectively store and process the data. This is especially difficult when it comes to dealing with large numbers of data points. 

  

How it is impacting different industries 

  

  1. Healthcare industry: Big data is being used to diagnose and treat diseases. Big data is also being used to improve patient care by tracking patient data and analyzing it to find trends and patterns. 
  2. Retail industry: big data is being used to track customer behavior and patterns. This information is used to improve customer service and to target advertising to specific customers.   
  3. Education industry: Big data is being used to track student data and to improve the quality of education. Alaa Negeda, Senior Solution Architect

But we still didn’t conquer big data

Major challenges organizations faced due to big data

The biggest challenge of big data is that while many companies have it, they don’t know what to do with it. They need to learn how to filter it properly, differentiate the useful from the useless, and make sense of it.

 

1. Scalability issue will large volume of data

Big data also poses a scalability challenge due to its sheer size. Slow-downs, disruptions, and errors often occur when employees need to be trained to handle large volumes of data. Big data’s vastness also makes it difficult to analyze and interpret, leading to incorrect decisions.  Jonathan Merry, Owner of Bankless Times 

 

2. Managing vulnerability of big data

One of the biggest problems with big data today is data security. Big data, in its nature, is too big, fast, or complex for normal software systems to handle with traditional methods, and that poses many security issues. Even regular data is constantly vulnerable to cyber-attacks and hacking. There are many ways in which big data security can and should be improved, like with better end-to-end encryption, stronger authentication methods, better data segmenting, etc. Maria Britton, CEO, Trade Show Labs 

 

3. Large amount of data generated

Data growth is another big data challenge of the 21st century due to the volume, variety and velocity of data being generated. The amount of data generated exceeds the ability of businesses to store and process it. This affects different industries in different ways.

For example, to personalize marketing campaigns and improve customer service, retailers collect data about users’ online activities and also use sensors to track their physical movements to better understand their shopping habits.

This requires multiple systems in place to continuously collect, manage and process data from various sources to analyze consumer behavior and trends. In the healthcare industry, for example, large amounts of data need to be analyzed in order to make informed decisions about patient care.
 

This makes it difficult for hospitals to provide timely and accurate care. The transportation industry faces a similar challenge. With the advent of self-driving cars, a huge volume of real-time data needs to be processed to ensure that cars are navigating roads safely. This data needs to be stored and analyzed to improve traffic flow and reduce congestion.

 

4. Data poisoning

A more widespread problem in 2022 and will continue to do so in 2023, as machine learning and AI become even more essential. Data poisoning can alter the results of your machine learning or your AI programming and can wreak havoc on your metrics. To avoid it, data storage should be of utmost importance, as well as monitoring the data that’s used by your ML or AI programs to make sure it’s reliable and accurate. Kyle MacDonald, Director of Operations, Force by Mojio
  

 

5. Meltdown 

A recent big data problem is the meltdown and CPU vulnerabilities. These vulnerabilities allow attackers to steal computer systems’ sensitive data, including passwords and encryption keys. A possible solution for this problem is to use hardware-based security features, such as Intel’s Software Guard Extensions (SGX) and ARM’s TrustZone, to protect sensitive data.

These technologies allow data to be stored in a secure enclave, which authorized users can only access. By using hardware-based security features, organizations can prevent attackers from accessing sensitive data and ensure the safety of their systems. Boris Jabes CEO and Co-Founder of Census 

Investing in the right software is the only way for businesses to fix their Data Integration issues. Several of the basic methods for integrating data are described below:
 

  • Talend Data Integration
  • Centerprise Data Integrator
  • ArcESB
  • IBM InfoSphere
  • Xplenty
  • Informatica PowerCenter
  • CloverDX
  • Microsoft SQL QlikView  

 

 

Possible solutions for big data problems 

Increased companies need data protection; thus, they are hiring cybersecurity experts. Additional measures made for security are Encryption of Data Separating Information Controlling who has access to what and where security at the endpoints is being implemented. Continuous security checks use IBM Guardian, one of its security technologies, to keep your data safe.

To solve these roadblocks, organizations must invest in the right technology and personnel to help them effectively manage their big data. Organizations should identify their key performance indicators (KPIs) and use data to measure them.

They should also establish goals, objectives, and metrics related to the KPIs so they can track the progress of their data initiatives.

1. Developing better data management tools

Organizations need to develop effective methods for managing big data. This includes creating systems for tracking and storing data, as well as for analyzing and using that data. 

Companies should develop new data management and analytics tools that can help businesses process and use large amounts of data efficiently. These tools include machine learning algorithms and artificial intelligence platforms that can help identify trends and patterns in datasets for effective data deduplication and compression.

In addition, companies should also consider investing in appropriate big data storage solutions that can accommodate the large volume of data generated depending on the data size and importance. Amey Dharwadker, Staff Machine Learning Engineer at Facebook

 

2. Integrating big data into existing systems

By integrating big data into existing systems, organizations can make it easier to access and use that data. This can improve the way that that data is used, which can lead to improved decision-making. 

 

3. Developing new big data solutions

 In order to exploit big data to its fullest potential, businesses need to develop new solutions. These solutions can include tools for extracting insights from the data, as well as for processing and analyzing that data. 

Companies are no longer limited by traditional methods of collecting information; they can now tap into huge wellsprings of untapped potential that span multiple sectors and industries. 

This enables them to identify new opportunities for growth as well as uncover previously hidden correlations between different types of data. Additionally, it allows them to enhance their existing processes with deeper knowledge about customers’ preferences or operations management decision-making. 

The ability to integrate data from a variety of sources is essential in today’s big data business environment. Companies need solutions that enable them to collect, store and analyze data quickly and easily. Big data integration solutions provide organizations with the capability to bring together data from multiple sources into a single system for better insights, visibility and decision-making.

 

4. Integration of big data for process automation

Integrating big data from multiple sources is critical for businesses that want to maximize the value of their investments in technology. By leveraging advanced analytics tools, companies can gain insight into customer behavior and identify new opportunities for growth. 

Companies can also use big data integration solutions to automate processes such as customer segmentation, product promotion, risk management, and fraud detection. With the help of these solutions, companies can gain a competitive edge by leveraging their data assets more effectively. Rajesh Namase, Co-Founder and Professional Tech Blogger at TechRT 

 

5. Improve storage methods

One option is to simply improve existing storage methods so that they can handle larger volumes of data. This could involve anything from developing new algorithms to making physical changes to
storage devices themselves.

 

6. Use more efficient processing methods

Another option is to focus on processing data more efficiently so that less storage space is required overall. This could involve anything from using compressed file formats to
investing in faster processors.

7. Delete less data  

A third option is for companies to be more selective about which data they keep and which they delete. This could involve implementing better retention policies or investing in tools that help
identify which data sets are most important.

Although there isn’t a silver bullet when it comes to solving this problem, hopefully, one (or more) of these solutions will help alleviate some of the pressure that companies are feeling when it comes to storing big data. Deepak Patel from bloggingko.com 

 

Take proactive measures to resolve big data problems 

This article has discussed the latest problem related to big data in the 21st century and how it is impacting different industries, as well as providing a possible solution for it. Prabhsharan Singh, Full Stack Developer 

By taking proactive measures such as implementing security protocols and being transparent with customers, companies can ensure that they are using the power of big data responsibly and protecting those involved from any potential misuse or abuse. 

   

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