It is not easy to name the top 5 Big data challenges as it is data with volume, velocity, variety, and veracity. A massive amount of unstructured, raw, coming from different platforms and uncertain and imprecise is called big data. It can be defined as:
The volume of big data is primarily terabytes or even exceeding exabytes.
Data is created at the pace of approximately 1.7 megabytes per person per second.
The data is associated with various platforms as it is unstructured and raw when it is received.
A massive amount of data is imprecise and uncertain.
Top 5 big data challenges:
Big data faces the following top 5 challenges:
Big data security and loopholes:
Usually, at the time of big data deployment, security issues are ignored at the initial stages and phases, which is a significant challenge for the big data deployment process. Organizations and enterprises focus on analyzing, managing, storing, understanding, monitoring, and regulating the security data set aside. It sets the ground for attackers and hackers. Data breaches, record breaches, and database heists can lead to the loss worth up to the millions. Without spending a fortune, you can get multiple advantages from EES corporation agile, reliable, and scalable Cloud Big Data Consulting services, such as finding meaningful insights, performing data analytics, etc.
Big data security issues and loopholes can be handled in the following ways:
- Organizations and companies should recruit more cybersecurity professionals.
- Data encryption and segregation should be implemented in every phase.
- Identity and access control and management should be regulated continuously.
- Endpoint security should be practiced.
- Real-time security tools and techniques should be used.
Scarcity of professionals and spending a lot of money:
Most companies and enterprises lack skilled data professionals compatible with modern and advanced data tools and techniques. These qualified data professionals and expertise include data analysts, data specialists, and data engineers. Data managing and handling tools evolve and advance as per data need and time passage; meanwhile, data knowledge and understanding are not evolved accordingly. It creates a gap and space between tools and data professionals.
Big data adoption needs a lot of money investment such as:
- Hiring and recruitments
- Electricity and power expenses
- Cloud services
- Maintenance of frameworks
- Software development
- Software configuration
- Hardware maintenance and repair
Following are the best possible solutions for meeting the challenge of scarcity of professionals and spending a lot of money:
- Companies and organizations should invest more in recruiting and hiring skilled professionals.
- Companies should conduct more training programs and workshops.
- Enterprises should buy artificial intelligence-powered knowledge analytic solutions.
- Hybrid cloud solutions for companies with low budgets.
- Data lakes offer the most reasonable data storage opportunities.
- Optimized algorithms help reduce and decrease the power consumption of computers and systems being used.
Lack of understanding and knowledge of Big data:
Most of the time, organizations and enterprises neglect the comprehension of big data. Enterprises ignore the pros and cons of big data, the infrastructure needed to deploy correctly, and all other basic knowledge of big data. Suppose the current processes and algorithms of big data are not altered according to the requirements of the enterprise, instead of advancement. In that case, it can resist the organization from going for improvement and progress.
As big data is capable of massive changes, its knowledge and understanding are vital for all employees, including top management to ordinary workers. The utilization and deployment of big data depend on the acknowledgment of workers.
Enterprises and organizations should conduct regular workshops and training to ensure acknowledgment.
Big data growth issues:
One of the biggest challenges for companies, organizations, enterprises and businesses is to handle the rapidly increasing quantity of data. Usually, information received is unstructured, scattered, and dispersed from different sources such as text files, documents, audios, videos, and emails, making it difficult to search and find in a database. As the company progresses, data stored also grows exponentially as time passes.
Big data growth issues can be solved by several techniques such as compression, deduplication, and tiering techniques.
The compression techniques are used to reduce and compress the data bits leading to a significant reduction in the actual size of the data.
Deduplication includes removing and eliminating the duplicated and unwanted data from the bulk of data to decrease the overall data size.
Data tiering is technique companies and organizations use to store data bulks in appropriate and suitable spaces and environments, data storage tiers. Data tiers include the followings:
o Public clouds
o Private clouds
o Counting on the information size
o Flash storage
Integrating data from all different platforms is one of the most challenging works for IT personnel. Most of the data come in dispersed and disassembled forms from other platforms. Sometimes half of the platforms are not supported locally. The developer must continuously alter the program or source code if the data is received in an unsupported form. It leads to the slow processing of the advancement cycle of the organization or enterprise.
In that way, many big data processing platforms create a huge hindrance and challenge for IT to re-arrange the complete infrastructure.
The best solution for this challenge is the software automation tools with APIs for considerable data, records, and databases.