who's challenges in handling big data , Big Challenges with Big Data

 

Big Challenges with Big Data

The challenges in Big Data are the real implementation hurdles. These require immediate attention and need to be handled because if not handled then the failure of the technology may take place which can also lead to some unpleasant result. Big data challenges include the storing, analyzing the extremely large and fast-growing data.

Challenges of Big Data

Storage

With vast amounts of data generated daily, the greatest challenge is storage (especially when the data is in different formats) within legacy systems. Unstructured data cannot be stored in traditional databases.

Processing

Processing big data refers to the reading, transforming, extraction, and formatting of useful information from raw information. The input and output of information in unified formats continue to present difficulties.

Security

Security is a big concern for organizations. Non-encrypted information is at risk of theft or damage by cyber-criminals. Therefore, data security professionals must balance access to data against maintaining strict security protocols.

Finding and Fixing Data Quality Issues

Many of you are probably dealing with challenges related to poor data quality, but solutions are available. The following are four approaches to fixing data problems:

• Correct information in the original database.
• Repairing the original data source is necessary to resolve any data inaccuracies.
• You must use highly accurate methods of determining who someone is.

Scaling Big Data Systems

Database sharding, memory caching, moving to the cloud and separating read-only and write-active databases are all effective scaling methods. While each one of those approaches is fantastic on its own, combining them will lead you to the next level.

Evaluating and Selecting Big Data Technologies

Companies are spending millions on new big data technologies, and the market for such tools is expanding rapidly. In recent years, however, the IT industry has caught on to big data and analytics potential. The trending technologies include the following:

• Hadoop Ecosystem
• Apache Spark
• NoSQL Databases
• R Software
• Predictive Analytics
• Prescriptive Analytics

Big Data Environments

In an extensive data set, data is constantly being ingested from various sources, making it more dynamic than a data warehouse. The people in charge of the big data environment will fast forget where and what each data collection came from.

Real-Time Insights

The term "real-time analytics" describes the practice of performing analyses on data as a system is collecting it. Decisions may be made more efficiently and with more accurate information thanks to real-time analytics tools, which use logic and mathematics to deliver insights on this data quickly.

Data Validation

Before using data in a business process, its integrity, accuracy, and structure must be validated. The output of a data validation procedure can be used for further analysis, BI, or even to train a machine learning model.

Healthcare Challenges

Electronic health records (EHRs), genomic sequencing, medical research, wearables, and medical imaging are just a few examples of the many sources of health-related big data.

Barriers to Effective Use Of Big Data in Healthcare

• The price of implementation
• Compiling and polishing data
• Security
• Disconnect in communication

Some of the Big Data challenges are:

1. Sharing and Accessing Data:
   • Perhaps the most frequent challenge in big data efforts is the inaccessibility of data sets from external sources.
   • Sharing data can cause substantial challenges.
   • It include the need for inter and intra- institutional legal documents.
   • Accessing data from public repositories leads to multiple difficulties.
   • It is necessary for the data to be available in an accurate, complete and timely manner because if data in the companies information system is to be used to make accurate decisions in time then it becomes necessary for data to be available in this manner.
2. Privacy and Security:
• It is another most important challenge with Big Data. This challenge includes sensitive, conceptual, technical as well as legal significance.
• Most of the organizations are unable to maintain regular checks due to large amounts of data generation. However, it should be necessary to perform security checks and observation in real time because it is most beneficial.
• There is some information of a person which when combined with external large data may lead to some facts of a person which may be secretive and he might not want the owner to know this information about that person.
• Some of the organization collects information of the people in order to add value to their business. This is done by making insights into their lives that they’re unaware of.
3. Analytical Challenges:
   • There are some huge analytical challenges in big data which arise some main challenges questions like how to deal with a problem if data volume gets too large?
   • Or how to find out the important data points?
   • Or how to use data to the best advantage?
   • These large amount of data on which these type of analysis is to be done can be structured (organized data), semi-structured (Semi-organized data) or unstructured (unorganized data). There are two techniques through which decision making can be done:
     • Either incorporate massive data volumes in the analysis.
     • Or determine upfront which Big data is relevant.




4. Technical challenges:
   • Quality of data:
     • When there is a collection of a large amount of data and storage of this data, it comes at a cost. Big companies, business leaders and IT leaders always want large data storage.
     • For better results and conclusions, Big data rather than having irrelevant data, focuses on quality data storage.
     • This further arise a question that how it can be ensured that data is relevant, how much data would be enough for decision making and whether the stored data is accurate or not.
   • Fault tolerance:
     • Fault tolerance is another technical challenge and fault tolerance computing is extremely hard, involving intricate algorithms.
     • Nowadays some of the new technologies like cloud computing and big data always intended that whenever the failure occurs the damage done should be within the acceptable threshold that is the whole task should not begin from the scratch.
   • Scalability:
     • Big data projects can grow and evolve rapidly. The scalability issue of Big Data has lead towards cloud computing.
     • It leads to various challenges like how to run and execute various jobs so that goal of each workload can be achieved cost-effectively.
     • It also requires dealing with the system failures in an efficient manner. This leads to a big question again that what kinds of storage devices are to be used.