What Are The Key Challenges of Big Data Today?

In the digital world now, data is produced on an unstoppable basis, from mobile devices and social media to IoT devices and online shopping. Businesses are awash in data. Sometimes data is referred to as the “new oil,” yet it is not as simple as all that. The challenges of big data are that the problem is not in collecting or storing the data, but in deeply understanding the same and using it wisely. The most important challenges of big data are a lack of decision-making, cutbacks on operations, and even result in financial loss for organizations. So, what are the hurdles? And how are companies overcoming these hurdles and solving the same?

A well-supported reality about Big Data’s Obstacles, echoed by major industry figures.

Lots of data is collected daily, companies have more data than ever, but the real challenge is turning it into useful ideas.

The facts on the challenges of big data by top leaders:

•  Mr. Satya Narayana Nadella, CEO of Microsoft, said: “We don’t have a data problem; we have an insight problem.”
• Doug Laney,( Data and Analytics Strategy) revealed: “The challenge is not in collecting data, it’s in knowing what to do with it.”

Challenges of Big Data- How Organizations are affected in Reality?

Real-world enterprises, or multinational corporations, and even the most influential companies in the technology industry struggle with the challenges of big data. For smaller companies or start-ups to be ready for the future, understanding their experiences helps them overcome the same.

• Netflix has to handle a massive amount of information. So, the problems they face like suggesting recommended shows and detecting streaming issues, but it’s not easy to get the data quickly or make sure it’s always correct.

• Amazon has to handle huge volumes of customer records, logistics, and product data daily. The challenges include: real-time analytics, managing storage, and personalization are the continuous struggles Amazon faces.

• Medical care systems like Apollo Hospitals in India, the Cleveland Clinic (USA) manage the unstructured data records of patients, handwritten notes, reports, and old systems across various branches are the key challenges.

• Public Sector Organizations run essential services from citizen records and traffic systems, but managing outdated systems with modern big data tools is the key challenge.

The above influential organizations are consistently working to overcome the challenges of big data in real-time.

The Significant Challenges of Big Data

The below are the challenges commonly faced by many industries, and they fall into categories like Volume, Variety, Veracity, and Velocity are the 4V’s of big data. Let us discuss in detail.

1. Data Volume and Shortage

The Increasing Scale of Data is the First Major Obstacle in Big Data Management

The volume explosion is the first barrier businesses face with Big Data. The traditional databases cannot store such huge data from billions of devices generating data. So, to handle all their information, businesses use modern solutions like cloud storage and data lakes. Moreover, using the cloud-based system does not solve all the issues, as storing big data, especially videos, audios, social media, costs even start increasing, and it’s still tough to handle repeated data, backups, and making sure people get the data when they need it.

2. Data Quality and Consistency

Inaccurate Data  Remains the Second Most Major Challenge in Big Data

Data reliability and standardization present another major obstacle. Data is extracted from several platforms, ranging from social networks, emails, and video footage. So, the major issue is that not all the data received is accurate, equal, and consistent. As a result, there is a chance of errors, missing values, and even duplicates. Inaccurate data leads to trust issues and unwise choices. 

Especially, in sensitive areas like banking and healthcare institutions, it harms a company’s reputation and can break important rules when data is not accurate and consistent.

For Example, in online shopping like Amazon, there may be duplicates in customer data. 

Pooja Sharma

Mrs.P. Sharma

Pooja S

The above names refer to the same person, but the system treats them as different people, which leads to miscalculating customer orders and confusing sales reports. This is bad and messy data.

3. Data Security and Privacy

Earning Trust Is a Slow Process, But Big Data Mistakes Can Ruin It Fast

The third most sensitive challenge of big data is security. The more data a company gathers, whether it’s personal information or important business details, the higher the risk of data being stolen or leaked, and just a hack could cost a company a lot of money and legal trouble. 

GDPR( General Data Protection Regulation) protects people’s personal information. Moreover, it gives control to ask why the data is being collected and ask for permission, and keep it safe.

HIPAA (Health Insurance Portability and Accountability Act) protects health record information. It is a law in the United States. To maintain the confidentiality and security of your data, HIPAA plays a crucial role.

The above rules show how sensitive data is to be handled, and the companies must follow the same.

4. Data Integration and Compatibility

The Separation of Information Into Silos: One More Big Data Roadblock

The next obstacle is data silos which means separate systems or departments that dont share information with each other. Many companies use different systems like web analytics, customer relationship management (CRM), Enterprise Resource Planning (ERP), etc.. but these tools often do not connect. One of the toughest challenges is integrating all the data into unified view is the biggest challenge. Mismatched file formats, old software, or unstructured data like images, videos, or voice recordings make integration more difficult. Consequently, companies’ decision-making slows down, and useful insights become hard for departments or teams to see the full picture. As a result, the overall performance becomes hard for organizations.

5. Processing Speed and Real-Time Analytics

Rapid Data, Outdated Infrastructure: A New Obstacle in Big Data

Another serious challenge is the processing speed and real-time analytics, meaning that in today’s dynamic society, everyone wants instant results. So, businesses can’t wait for hours or days to process data. Handling a huge number of data sets instantly is the biggest challenge. Future data systems like Apache Spark and Hadoop demand high maintenance, which older technologies can’t provide. 

6. Skill Gaps and Talent Shortage

Human-Centered Challenges in Big Data

Despite having top tools at their disposal, most businesses are unable to make full use of big data because they just don’t have the manpower necessary to put all the information to work. It’s a matter of people. 

Data scientists, analysts, and engineers are being snapped up faster by businesses than reservoirs on a hot day in August. Moreover, on the corporate side, whereas lines are trained to breathe meaning into data sets, business people simply lack the know-how for statistical reasoning in analysis.

Indeed, this problem is so acute that many companies find themselves subcontracting analytic tasks to of house situation, which obviously can lead to the loss of control and security of data. 

7. High Cost Of Infrastructure

The Economic Side of Big Data Struggles

The seventh and final biggest challenge is the high cost of infrastructure, which means companies require computers with high speed and the latest AI tools, and even regular updates for running and creating a big data ecosystem, which costs a huge investment. Cloud-based solutions help companies with less data, but as data grows quickly, the cost increases. So, small and medium-sized enterprises struggle to afford or invest in security, software, and training for their team.

Therefore, Return on Investment (ROI) becomes a struggle in the challenges of big data. 

Practical Solutions based on problems for the common challenges of Big Data

Potential Gains In Big Data Management

The pie chart below illustrates how the challenges of Big Data can be addressed in the future.

Henry Harvin- Big Data Analytics Course

Henry Harvin was founded by Kounal Gupta in the year 2013, and has developed into a top education technology company offering 1200+ courses, and over 3,00,000 students enroll in and pass out annually. The vision is to focus on the growth of individuals, especially students, and develop businesses globally. Moreover, to deliver quality education, they invest in the latest technology, expert guidance, and top learning materials.

Henry Harvin is a globally recognised institution linked with global organizations like, United Kingdom Accreditation Forum (UKAF), International Association for Six Sigma Certification(IASSC), Project Management Institute(PMI). 

Certification: Secure a top-rated Big Data Analyst certification from Henry Harvin, recognized by the government of India and NSDC 

Modules covered under Big Data Analytics Course: There are 7 modules covered in the course, with the latest frameworks of Hive, Spark, and Hadoop.

Through Henry Harvin’s Big Data Analytics course, any IT professional, business leader, or anyone handling data can become a Certified Big Data Analyst. Additionally, Henry Harvin’s structured curriculum and hands-on practice give confidence to handle big data and challenges in the data-driven world.

Conclusion

As a result, to overcome the challenges of big data and make use of the practical solutions to solve the same, turns your business into a success story. Data continues to grow and become complex, but to handle those challenges, businesses need to understand the solutions of using the technique of strategy, skilled people, and the latest technology. With these three businesses can not just survive, but thrive.

Recommend Reads

• What is AWS Big Data?
• What are the benefits of Apache Spark over Hadoop?
• Data Management: Definition and Importance
• Exploring The Role Of Big Data In Transforming Industries
• A Beginner’s Guide to Data Collection- Definition, Types, Examples, Tools

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