What are the challenges and Application in Big Data

April 20, 2024 rathodharshal

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 the 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 whose designs are dedicated to supporting operational and analytical processing. The NoSQL (not only SQL) frameworks are used that differentiate it from traditional relational database management systems and are also largely designed to fulfill performance demands of big data applications such as managing a large amount of data and quick response times.

There are a variety of NoSQL approaches such as hierarchical object representation (such as JSON, XML, and BSON) and the concept of key-value storage. The wide range of NoSQL tools, developers, and the status of the market are creating uncertainty with data management.

2. Talent Gap in Big Data:

It is difficult to win the respect of media and analysts in tech without being bombarded with content touting the value of the analysis of big data and corresponding reliance on a wide range of disruptive technologies.

The new tools evolved in this sector can range from traditional relational database tools with some alternative data layouts designed to maximize access speed while reducing the storage footprints, NoSQL data management frameworks, in-memory analytics, and as well as the broad Hadoop ecosystem.

The reality is that there is a lack of skills available in the market for big data technologies. The typical expert has also gained experience through tool implementation and its use as a programming model, apart from the big data management aspects.

3. Getting Data into Big Data Structure:

It might be obvious that the intent of big data management involves analyzing and processing a large amount of data. Many people have raised expectations considering analyzing huge data sets for a big data platform. They also may not be aware of the complexity behind the transmission, access, and delivery of data and information from a wide range of resources and then loading these data into a big data platform.

The intricate aspects of data transmission, access, and loading are only part of the challenge. 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 you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This implies that the data coming from one source is not out of date as compared to the data coming from another source. It also means the commonality of data definitions, concepts, metadata, and the like.

The traditional data management and data warehouses, the sequence of data transformation, extraction, and migrations all arise the situation in which there are risks for data to become unsynchronized.

5. Extracting Information from the Data in Big Data Integration:

The most practical use cases for big data involve the availability of data, augmenting existing storage of data as well as allowing access to end-users employing business intelligence tools for the discovery of data. This business intelligence must be able to connect different big data platforms and also provide transparency to the data consumers to eliminate the requirement of custom coding.

At the same time, if the number of data consumers grows, then one can provide a need to support an increasing collection of many simultaneous user accesses. This increment of demand may also spike at any time in reaction to different aspects of business process cycles. It also becomes a challenge in big data integration to ensure the right-time data availability to the data consumers.

6. Miscellaneous Challenges:

Other challenges may occur while integrating big data. Some of the challenges include the integration of data, skill availability, solution cost, the volume of data, the rate of transformation of data, and the veracity and validity of data.

The ability to merge data that is not similar in source or structure and to do so at a reasonable cost and in time. It is also a challenge to process a large amount of data at a reasonable speed so that information is available to data consumers when they need it. The validation of the data set is also fulfilled while transferring data from one source to another or consumers as well. This is all about big data integration and some challenges that one can face during the implementation. These points must be considered and should be taken care of if you are going to manage any big data platform.

7. Dealing with Data Growth The most obvious challenge associated with big data is simply storing and analyzing all that information.

In its Digital Universe report, IDC estimates that the amount of information stored in the world’s IT systems is doubling about 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. And enterprises have responsibility or liability for about 85 percent of that information.

Much of that data is unstructured, meaning that it doesn’t reside in a database. Documents, photos, audio, videos, and other unstructured data can be difficult to search and analyze.

It’s no surprise, then, that the IDG report found, “Managing unstructured data is growing as a challenge rising from 31 percent in 2015 to 45 percent in 2016.?

In order to deal with data growth, organizations are turning to a number of different technologies. When it comes to storage, converged and hyper-converged infrastructure and software-defined storage can make it easier for companies to scale their hardware. And technologies like compression, deduplication, and tiering can reduce the amount of space and the costs associated with big data storage.

On the management and analysis side, enterprises are using tools like NoSQL databases, Hadoop, Spark, big data analytics software, business intelligence applications, artificial intelligence, and machine learning to help them comb through their big data stores to find the insights their companies need.

8. Generating Insights in a Timely Manner

Of course, organizations don’t just want to store their big data they want to use that big data to achieve business goals. According to the New Vantage Partners survey, the most common goals associated with big data projects included the following: Decreasing expenses through operational cost efficiencies Establishing a data-driven culture Creating new avenues for innovation and disruption Accelerating the speed with which new capabilities and services are deployed Launching new product and service offerings

All of those goals can help organizations become more competitive but only if they can extract insights from their big data and then act on those insights quickly. PwC’s Global Data and Analytics Survey 2016 found, “Everyone wants decision-making to be faster, especially in banking, insurance, and healthcare.”

To achieve that speed, some organizations are looking for a new generation of ETL and analytics tools that dramatically reduce the time it takes to generate reports. They are investing in software with real-time analytics capabilities that allow them to respond to developments in the marketplace immediately.

9. Recruiting and Retaining Big Data Talent

But in order to develop, manage, and run those applications that generate insights, organizations need professionals with big data skills. That has driven up demand for big data experts and big data salaries have increased dramatically as a result. The 2017 Robert Half Technology Salary Guide reported that big data engineers were earning between $135,000 and $196,000 on average, while data scientist salaries ranged from $116,000 to $163, 500. Even business intelligence analysts were very well paid, making $118,000 to $138,750 per year.

In order to deal with talent shortages, organizations have a couple of options. First, many are increasing their budgets and their recruitment and retention efforts. Second, they are offering more training opportunities to their current staff members in an attempt to develop the talent they need from within. Third, many organizations are looking to technology.

They are buying analytics solutions with self-service and/or machine learning capabilities. Designed to be used by professionals without a data science degree, these tools may help organizations achieve their big data goals even if they do not have a lot of 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 a lot of different places enterprise applications, social media streams, email systems, employee-created documents, etc. Combining all that data and reconciling it so that it can be used to create reports can be incredibly difficult. Vendors offer a variety of ETL and data integration tools designed to make the process easier, but many enterprises say that they have not solved the data integration problem yet.

In response, many enterprises are turning to new technology solutions. In the IDG report, 89 percent of those surveyed said that their companies planned to invest in new big data tools in the next 12 to 18 months. When asked which kind of tools they were planning to purchase, integration technology was second on the list, behind data analytics software.

11. Validating Data

Closely related to the idea of data integration is the idea of data validation. Often organizations are getting similar pieces of data from different systems, and the data in those different systems doesn’t always agree. For example, the ecommerce 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, the fastest-growing area of concern cited by respondents was data governance.

Solving data governance challenges is very complex and usually requires a combination of policy changes and technology. Organizations often set up a group of people to oversee data governance and write a set of policies and procedures. They may also invest in data management solutions designed to simplify data governance and help ensure the accuracy of big data stores and the insights derived from them.

12. Securing Big Data

Security is also a big concern for organizations with big data stores. After all, some big data stores can be attractive targets for hackers or Advanced Persistent Threats (APTs).

However, most organizations seem to believe that their existing data security methods are sufficient for their big data needs as well. In the IDG survey, less than half of those surveyed (39 percent) said that they were using additional security measures for their big data repositories or analyses. Among those who do use additional measures, the most popular include identity and access control (59 percent), data encryption (52 percent), and data segregation (42 percent).

13. Organizational Resistance It is not only the technological aspects of big data that can be challenging people can be an issue too.

In the New Vantage Partners survey, 85.5 percent of those surveyed said that their firms were committed to creating a data-driven culture, but only 37.1 percent said they had been successful with those efforts. When asked about the impediments to that culture shift, respondents pointed to three big obstacles within their organizations: (I) Insufficient organizational alignment (4.6 percent) (II) Lack of middle management adoption and understanding (41.0 percent) (III) Business resistance or lack of understanding (41.0 percent)

In order for organizations to capitalize on the opportunities offered by big data, they are going to have to do some things differently. And that sort of change can be tremendously difficult for large organizations.