Top 8 Challenges and Solutions of Big Data
Global industries are facing challenges in volume, velocity, and variety of big data and they require to capitalize on them by tackling the challenges with commitment. Many organizations are unaware and unequipped of the big data challenges and in this blog, we cover them from scratch. It will help you understand the challenges briefly to bring solutions with efficient big data processing.
Overview of Big Data
There is no fixed size in big data but it can be related to the organization for handling large amounts of data with the following categorization
- Volume: Data is being generated in companies through social media, IoT, eCommerce, mobility, and so on and it will be difficult for the organization to handle them efficiently.
- Velocity: With the rapid generation of data, every organization has to deal with popular trends
- Variety: Grouping and classification of data will be the biggest task as the gathered data would be in a variety of formats. It can be email, word processing, presentation, image, video, document, or RDBMS (Relational Database Management System).
Challenges of Companies and Solutions with Big Data
The big data processing journey will not be smooth always and it becomes very normal for businesses around the world. As per the survey of NewVantage Partners, 95% of Fortune 1000 companies have already adopted Big Data for gaining valuable results from their investment. Below are the Top 8 Challenges and Solutions of Big Data faced by global companies.
- Data Sources
- Data Growth
- Real-time Insights
- Data Validation
- Data Security
- Big Data Skills
- Increased Salaries
- Resistance to Big Data Adoption
The biggest big data integration challenge is to combine data from sources like social media pages, employee documents, financial reports, presentations, customer logs, emails, and so on for generating insights for consolidated reports.
Solution: Data integration is crucial for analyzing, reporting, and business intelligence processing and there are a large number of ETL integration tools available in the market for the data integration process. The IDG report stated that the most companies are planned to invest in the integration technology and use one of the following integration tools
- Microsoft SQL
- IBM Infosphere
- Informatica PowerCenter
- Oracle Data Service Integrator
The storage is another challenge in big data processing as the data is growing heavily. Companies are struggling for storing the huge amount of data that is gathered as images, documents, audios, text files, and so on. The collected data will be unstructured and most of them will not be in databases. Thus, extracting and analyzing unstructured data will be very difficult in the big data processing.
Solution: The exponential data growth can be handled efficiently with the converged and hyper-converged software-defined infrastructure or storage. They can compress, deduplicate, or tier the data for reducing space occupation which helps in cost-effective data storage. Big data tools like Hadoop, NoSQL Database, and Spark along with technologies like Artificial Intelligence, Business Intelligence, and Machine Learning are used by companies to deal the data storage problems.
Datasets will have no value if they don’t provide real-time insights. If they are quicker for some but time-taken for others for data extraction and analysis. The fundamental idea is to create actionable insights for performing result-centric tasks as follows.
- Launching new initiatives for innovation and disruption
- Fastening up the service deployment processes
- Cost-cutting by operational cost-efficiency solutions
- Deploying new products and services
- Encouraging the best data-driven culture.
Solution: Companies have to invest more in ETL and Big Data Analytics tools for extracting real-time capabilities to stay updated with the exact market trends.
The data gathered by organizations may not be similar when they collected similar types of resources from various sources. Here, validating the data will be the problem in the big data processing. The collected data must be accurate, usable, and secured along with the standard of data governance. Data governance is a steadily growing thing as per the recent survey of AtScale.
Solution: This big data challenge will be tackled and it will be complicated to deal with data governance to validate the policy changes that are integrated with technologies. Companies can ensure data accuracy by employing a skilled and dedicated team to manage data governance and investing in ad-hoc management solutions.
Security of data is the biggest concern in big data processing and the organizations that have sensitive data or personal user information are at high risk. Unsafe data is the easiest target for cyberattacks and malicious access. Organizations may have strong security protocols and they might think that they are enough for their data repositories.
But investing in additional measures with big data tools like identity and access authority, data segregation, and data encryption helps more for safe data storage and analytics. Below are the possibilities to handle security challenges in Big Data.
- Employing specialized cybersecurity professionals
- Data segregation and data encryption methodology implementations
- Identity and access authorization controls
- Endpoint automation security
- Real-time and accurate monitoring processes
- Utilization of big data security tools like IBM Guardium
Big Data Skills
Big data processing requires expert data scientists, data analysts, and data engineers to handle big data challenges efficiently to extract valuable insights used for the growth of companies. But companies are having huge skill gaps and they are in demand of skilled professionals which leads biggest big data challenge.
The annual requirement for the big data experts is increasing exponentially as per the report of ZipRecruiter and the US companies are ready to offer US$107,892 as an average annual pay. Whether companies must recruit new professionals or train their existing employees with required industry skills.
Solution: Big Data tools for handling data growth are evolving but the data professionals are not updating themselves with the right skills. Therewith, companies can invest in AI or ML-powered data analytics solutions that can even handle by non-experts with basic knowledge. By this, companies can cut the cost of resources to achieve the best big data solution.
The annual compensation for big data professionals is increasing significantly as per the report of Robert Half Technology Salary Guide. The average salary is between US$135,000 and US$196,000 for entry-level big data engineers, it is around US$116,000 for big data scientists, and it is around US$118,000 for Business Intelligent Analysts every year.
Solution: Instead of employing new employees for the increased salaries, companies can train their existing employees with a significant hike on their current CTC. They can even implement MI and AI-Powered technology solutions and train their employees with the purchased tool.
Resistance to Big Data Adoption
Many organizations are resisting big data adoption while others are introducing the best data-driven culture for their businesses. Below are the possible reasons for companies to adopt big data solutions.
- Lack of understanding about the big data processing for their businesses
- Limited organizational alignment
- Less middle-level management to understand the adoption needs
Due to this lack of understanding of big data, companies are failing to invest in big data processes. Thus, employees will be unaware of big data and its importance. When employees are failed to understand the big data processes, they can’t follow accurate and appropriate procedures or methods, or protocols to handle big data effectively. It leads to a setback for the companies.
Solution: Introducing big data knowledge to the employees brings massive success for all organizations. They can conduct webinars, seminars, real-time hands-on, training programs, workshops, etc for their employees to practice with the big data world. It helps them in a better decision-making process and creates strong leaders who know how to capitalize on big data opportunities effectively and efficiently.
Big Data Risks in Various Industry Sectors
Following are the big data risk possibilities for various industries.
Healthcare challenges: Healthcare analytics platforms can have challenges in efficiency in diagnoses, prescribing required medicines, giving medical results in digital format, predictive analytics implementation to identify patterns, real-time monitoring, data exchange development, interoperability architecture, personalized patient care, AI-based analytical platform for multi-sourced data integration, predictive and prescriptive platform model to the minimized gap for accurate results.
Security Management challenges: Fake data generation, granular access control, data security systems, data provenance, and real-time data security are the possible big data challenges for security management platforms.
Cloud Security Governance challenges: Performance management, cost management, security issues, and governance control are the possible big data challenges of cloud security governance platforms.
Those 8 top Big data challenges can be resolved by implementing innovative solutions as it is important to stay updated with the trending market competition. It is very much important to understand the challenges and solutions exactly for tackling them uniquely. The biggest possible way to face the big data challenge is investing in the right resource and training the employees with the right big data tools.
If you are running a company and you require big data solutions, we suggest you train your employees with our Best Big Data Training in Chennai at Softlogic. We provide expert training with hands-on exposure to various big data tools that are trending in the market in our Big Data Training Institute in Chennai.