Big Data Challenges and Requirements

Big Data Challenges and Requirements

In this article, I am going to discuss the Big Data Challenges and Requirements. Please read our previous article, where we discussed Big Data Technologies. At the end of this article, you will understand everything about the Big Data Challenges and Requirements.

Big Data challenges

There are many more challenges that can be seen during the processing of data, storage of data, analyzing the data. There are many challenges where the solutions have been found now. But still, some challenges are difficult to solve.

Following are the big data challenges

1. Difficulty managing data quality

Organizations gather data from various sources and which have different formats. So, the problem arises in the data integration stage. For example, data from various websites are in different formats where the quality of data is not maintained. To overcome this type of problems various techniques should be used to clean the data first and then the modeling is done for the data

2. Lack of proper understanding of Big Data

Companies fail in their Big Data initiatives due to insufficient understanding. Employees may not know what data is, its storage, processing, importance, and sources. To overcome this problem, organizations should conduct various training sessions and seminars.

3. Improper selection of Big Data tool

Choosing a good analytics tool is a challenging task for organizations. In current conditions, there are multiple tools available in the market. Organizations sometimes fail to make a proper decision in choosing a good analytics tool. This results in a wastage of money, time, and effort. Professional help should be considered in this condition. Big data consultants can choose the right tool according to your need and hence help in minimizing organizations’ cost, time and efforts.

4. Lack of data professionals

To run these modern technologies and Big Data tools, companies need skilled data professionals. These professionals will include data scientists, data analysts, and data engineers who are experienced in working with the tools and making sense out of huge data sets.  Companies should invest in training existing staff and help them to understand the big data tools.

5. Integration of data from various sources

Organizations receive data from various sources like social media, customer logs, financial reports, emails, and reports created by employees. Collaboration of data is a challenging task for an organization where the quality of data. This problem can be solved by purchasing the right tool for the right task. 

Big Data Requirements

Big data analytics helps users collect and analyze large-sized data sets that have a varied mix of content. This analysis delivers insights into the content through the exploration of data patterns. This data set can include a variety of subjects, from buying preferences of customers to trends setting the markets. These insights are used by business owners to make informed decisions that are driven by data.

Data processing

The start of the analysis is raw data. The data processing feature includes the collection and organization of raw data that is intended to produce insights. Data processing includes data modeling that displays illustrative diagrams and charts from complex data sets. This helps the user visually interpret the numerical data and easily interpret that information to make an informed decision.

Data mining is a subset of data processing that extracts and analyzes data from various perspectives to deliver actionable insights. This is useful when the unstructured data is large in size and is collected over a considerable period of time.

Typical processes included in the data analysis are: modeling, data mining, importing data from a variety of file sources, and exporting the data to several types of outputs. These processes help enhance the use and transfer of the data collected through previous processes.

Predictive applications

Your organization defines the people and equipment that have the right to view and work on the data. This process is called “information identity management” or “access management.” This functionality assimilates data for accessing your system. This includes the access rights of individual users, computers, and software.

Identity management works with the methods of gaining access, generation of that identity, protection of that identity, and support for protective systems like the network protocols and passwords. The system determines if a particular user has access to a system and also the level of access that the user is permitted to use. Identity management aims to allow only authenticated users to access your system and data. This management is a vital part of your organization’s security protocols and includes fraud analysis and real-time protection systems.

The analytics of fraud can work with a variety of detection functions. Even today, several businesses implement these fraud prevention systems only after they have faced a threat. Thus, they work toward mitigating the impact of the attack rather than trying to proactively prevent it. Data analytics tools can help in the detection of fraud by testing your data repeatedly in order to determine its integrity. You can also inspect the large data set rather than depending on spot checks for financial transactions. Analytics serves as an early warning utility to swiftly locate and nullify fraudulent activities before they impact your business function.

Analytics

For offering flexibility and options to their users, big data analytics tools have several packages and modules:

Risk analytics

Analysis of risk studies the unpredictability and uncertainty surrounding the activity. The study can be applied alongside a forecasting mechanism for minimizing the negative impacts of unforeseen events. This study works to minimize risks by listing the organization’s ability to handle such an eventuality.

Decision management

Analytical tools include modules that help in making decisions and implementing processes that run the business. This module considers decisions as strategic assets. The module includes technology to automate sections of decision-making processes.

Text analytics

Text analytics examines the written text. This software helps find patterns in the analyzed text and delivers potential action points on your learning. This is useful for understanding your customers’ requirements and depends on their interaction with and input in your organization.

Content analysis

This analysis works on recognition and grading all types of documentation, including images, audio, video, etc.

Social media analytics

Social media analysis is a specialized form of content analysis that studies the interaction of your users on social media platforms, such as Twitter, Facebook, Instagram, etc.

Statistical analytics

Statistical analytics works with the collection and analysis of numerical data sets. This analysis aims to deliver samples from a large data set using statistical methods. The statistical analysis has five distinct steps: description of the data’s nature, establishing the relation between the data and the population that created this information, generation of a model that summarizes the relationships, proving and disproving the data’s veracity, and use of predictive analytics techniques for making correct decisions.

Predictive analytics

Predictive analytics is a natural progression to the statistical process. This process uses the collected and analyzed data to create “what-if” scenarios and the prediction of potential problems in the future.

Reporting

The reporting function helps users have complete control over their business. The real-time reporting collects current information and displays the data on an intuitive user interface. This simple-to-use interface enables users to make instant decisions in time-sensitive situations. It also prepares the user to be increasingly competitive in a market that is moving forward and modifying at a very fast pace.

The user interfaces or dashboards deliver data visualization tools to show metrics and key performance indicators (KPIs). The dashboard is often customizable to help the user see the performance of a selected report on a target data set or a specific metric.

Some of this targeted data could be insights based on location. These data sets gather information and sift data by location to determine the local demographics.

Security

Ensuring data security is vital for business success. Big data tools offer features to ensure security and safety. “Single sign-on” or SSO is one such security feature to allow authentication service for assigning a single set of login credentials to users for accessing multiple applications. SSO authenticates the user permissions and avoids having to log in multiple times in one session. SSO can also monitor usage and maintain a log of accounts of the user’s activity on the system.

Data encryption is yet another powerful security feature in big data platforms. Encryption uses algorithms and codes to jumble electronic bits into an unreadable format to avoid unauthorized entities viewing your data. Most web browsers offer some form of data encryption, but your business requires a more robust system for safeguarding critical data. During selection, ensure that your big data software requirement includes powerful encryption capabilities as a standard feature.

Technology support

To be useful across a variety of platforms and situations, your big data software should be compatible with the technology and tasks required for the business. One such example is A/B testing or split testing or bucket testing. This testing can compare two versions of an application or a website to determine the better performing set. A/B testing lists the method used by users to work with both the versions and delivers statistical analysis on the results to predict the version that will give the best performance for the requirement.

Another big data software requirement is integration with Hadoop, which is a set of open-source programs that work as a foundation for data analytics.

In the next article, I am going to discuss Big Data Distributed Computing and Complexity. Here, in this article, I try to explain Big Data Challenges and Requirements and I hope you enjoy this Big Data Challenges and Requirements article.

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