Challenges of Big Data
The handling of big data is very complex. Some challenges faced during its integration include uncertainty of data management, big data talent gap, getting data into a big data structure, syncing across data sources, getting useful information out of big data, volume, skill availability, solution cost, etc.
1. The Uncertainty of Data Management
One disruptive facet of big data management is the use of a wide range of innovative data management tools and frameworks designed to support operational and analytical processing. NoSQL frameworks are used to differentiate from traditional relational database management systems and are designed to fulfill performance demands of big data applications, such as managing large amounts of data and quick response times. The wide range of NoSQL tools, developers, and the market status create uncertainty with data management.
2. Talent Gap in Big Data
It is challenging to gain respect in tech without being bombarded with content touting the value of big data analysis and corresponding reliance on a wide range of disruptive technologies. New tools evolved in this sector include traditional relational database tools, NoSQL data management frameworks, in-memory analytics, and the broad Hadoop ecosystem. There is a lack of skills available in the market for big data technologies. Experts typically gain experience through tool implementation and its use as a programming model, apart from big data management aspects.
3. Getting Data into Big Data Structure
Analyzing and processing large amounts of data is the intent of big data management. Many people may not be aware of the complexity behind the transmission, access, and delivery of data from various resources and then loading these data into a big data platform. The requirement to navigate transformation and extraction is not limited to conventional relational data sets.
4. Syncing Across Data Sources
Once you import data into big data platforms, data copies migrated from various sources on different rates and schedules can rapidly get out of synchronization with the originating system. This implies that data from one source may be out of date compared to data from another source. The traditional data management and data warehouses face risks for data to become unsynchronized.
5. Extracting Information from Data
The most practical use cases for big data involve the availability of data, augmenting existing storage of data, and allowing access to end-users using business intelligence tools for data discovery. Business intelligence must connect different big data platforms and provide transparency to data consumers to eliminate the requirement of custom coding. Ensuring right-time data availability to the data consumers becomes a challenge as demand may spike due to different aspects of business process cycles.
6. Miscellaneous Challenges
Other challenges include data integration, skill availability, solution cost, data volume, data transformation rate, and the veracity and validity of data. The ability to merge data that is not similar in source or structure at a reasonable cost and time is challenging. Processing large amounts of data at a reasonable speed to make information available to data consumers when needed is also a challenge. Validation of the data set is essential while transferring data from one source to another or consumers.
7. Dealing with Data Growth
Storing and analyzing large amounts of information is a significant challenge. According to IDC’s Digital Universe report, the amount of information stored in the world’s IT systems is doubling every two years. By 2020, the total amount will be enough to fill a stack of tablets that reach from the Earth to the moon 6.6 times. Enterprises have responsibility or liability for about 85 percent of that information. Much of this data is unstructured, making it difficult to search and analyze. Technologies like compression, deduplication, and tiering can reduce space and costs associated with big data storage. NoSQL databases, Hadoop, Spark, big data analytics software, business intelligence applications, artificial intelligence, and machine learning help enterprises analyze big data.
8. Generating Insights in a Timely Manner
Organizations aim to use big data to achieve business goals like decreasing expenses, establishing a data-driven culture, creating new avenues for innovation, accelerating capabilities deployment, and launching new products and services. Extracting insights from big data and acting on them quickly is crucial. Organizations are investing in ETL and analytics tools with real-time analytics capabilities to respond to market developments immediately.
9. Recruiting and Retaining Big Data Talent
Organizations need professionals with big data skills to develop, manage, and run applications that generate insights. The demand for big data experts has driven up salaries. Organizations have options to deal with talent shortages, such as increasing budgets for recruitment, offering more training opportunities to current staff, and investing in analytics solutions with self-service and machine learning capabilities. These tools help organizations achieve big data goals even without many big data experts on staff.
10. Integrating Disparate Data Sources
The variety associated with big data leads to challenges in data integration. Big data comes from various sources like enterprise applications, social media streams, email systems, and employee-created documents. Combining and reconciling this data to create reports can be difficult. Vendors offer ETL and data integration tools to make the process easier. Many enterprises invest in new big data tools to address these challenges.
11. The Uncertainty of Data Management
One disruptive facet of big data management is the use of a wide range of innovative data management tools and frameworks designed to support operational and analytical processing. NoSQL frameworks are used to differentiate from traditional relational database management systems and are designed to fulfill performance demands of big data applications, such as managing large amounts of data and quick response times. The wide range of NoSQL tools, developers, and the market status create uncertainty with data management.
12. Talent Gap in Big Data
It is challenging to gain respect in tech without being bombarded with content touting the value of big data analysis and corresponding reliance on a wide range of disruptive technologies. New tools evolved in this sector include traditional relational database tools, NoSQL data management frameworks, in-memory analytics, and the broad Hadoop ecosystem. There is a lack of skills available in the market for big data technologies. Experts typically gain experience through tool implementation and its use as a programming model, apart from big data management aspects.
13. Getting Data into Big Data Structure
Analyzing and processing large amounts of data is the intent of big data management. Many people may not be aware of the complexity behind the transmission, access, and delivery of data from various resources and then loading these data into a big data platform. The requirement to navigate transformation and extraction is not limited to conventional relational data sets.
14. Syncing Across Data Sources
Once you import data into big data platforms, data copies migrated from various sources on different rates and schedules can rapidly get out of synchronization with the originating system. This implies that data from one source may be out of date compared to data from another source. The traditional data management and data warehouses face risks for data to become unsynchronized.
15. Extracting Information from Data
The most practical use cases for big data involve the availability of data, augmenting existing storage of data, and allowing access to end-users using business intelligence tools for data discovery. Business intelligence must connect different big data platforms and provide transparency to data consumers to eliminate the requirement of custom coding. Ensuring right-time data availability to the data consumers becomes a challenge as demand may spike due to different aspects of business process cycles.
16. Miscellaneous Challenges
Other challenges include data integration, skill availability, solution cost, data volume, data transformation rate, and the veracity and validity of data. The ability to merge data that is not similar in source or structure at a reasonable cost and time is challenging. Processing large amounts of data at a reasonable speed to make information available to data consumers when needed is also a challenge. Validation of the data set is essential while transferring data from one source to another or consumers.
17. Dealing with Data Growth
Storing and analyzing large amounts of information is a significant challenge. According to IDC’s Digital Universe report, the amount of information stored in the world’s IT systems is doubling every two years. By 2020, the total amount will be enough to fill a stack of tablets that reach from the Earth to the moon 6.6 times. Enterprises have responsibility or liability for about 85 percent of that information. Much of this data is unstructured, making it difficult to search and analyze. Technologies like compression, deduplication, and tiering can reduce space and costs associated with big data storage. NoSQL databases, Hadoop, Spark, big data analytics software, business intelligence applications, artificial intelligence, and machine learning help enterprises analyze big data.
18. Generating Insights in a Timely Manner
Organizations aim to use big data to achieve business goals like decreasing expenses, establishing a data-driven culture, creating new avenues for innovation, accelerating capabilities deployment, and launching new products and services. Extracting insights from big data and acting on them quickly is crucial. Organizations are investing in ETL and analytics tools with real-time analytics capabilities to respond to market developments immediately.
19. Recruiting and Retaining Big Data Talent
Organizations need professionals with big data skills to develop, manage, and run applications that generate insights. The demand for big data experts has driven up salaries. Organizations have options to deal with talent shortages, such as increasing budgets for recruitment, offering more training opportunities to current staff, and investing in analytics solutions with self-service and machine learning capabilities. These tools help organizations achieve big data goals even without many big data experts on staff.
20. Integrating Disparate Data Sources
The variety associated with big data leads to challenges in data integration. Big data comes from various sources like enterprise applications, social media streams, email systems, and employee-created documents. Combining and reconciling this data to create reports can be difficult. Vendors offer ETL and data integration tools to make the process easier. Many enterprises invest in new big data tools to address these challenges.
21. Validating Data
Closely related to data integration is the idea of data validation. Organizations often get similar pieces of data from different systems, and these data do not always agree. For example, the e-commerce system may show daily sales at a certain level while the Enterprise Resource Planning (ERP) system has a slightly different number. A hospital’s Electronic Health Record (EHR) system may have one address for a patient, while a partner pharmacy has a different address on record.
The process of getting those records to agree, as well as making sure the records are accurate, usable, and secure, is called data governance. In the At Scale 2016 Big Data Maturity Survey, data governance was the fastest-growing area of concern. Solving data governance challenges is complex and usually requires a combination of policy changes and technology. Organizations often set up a group to oversee data governance and write policies and procedures. They may also invest in data management solutions to simplify data governance and ensure the accuracy of big data stores and the insights derived from them.
22. Securing Big Data
Security is a significant concern for organizations with big data stores, as some big data stores can be attractive targets for hackers or Advanced Persistent Threats (APTs). However, most organizations believe their existing data security methods are sufficient for their big data needs. According to the IDG survey, 39 percent of those surveyed said they were using additional security measures for their big data repositories or analyses. Among those who use additional measures, the most popular include identity and access control (59 percent), data encryption (52 percent), and data segregation (42 percent).
23. Organizational Resistance
Technological aspects of big data are not the only challenges; people can be an issue too. According to the New Vantage Partners survey, 85.5 percent of those surveyed said their firms were committed to creating a data-driven culture, but only 37.1 percent had been successful. The impediments to that culture shift included:
- Insufficient organizational alignment (4.6 percent)
- Lack of middle management adoption and understanding (41.0 percent)
- Business resistance or lack of understanding (41.0 percent)
To capitalize on the opportunities offered by big data, organizations must do things differently, and such change can be difficult for large organizations.