What is big data analytics?
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 of variety of subjects, from buying preferences of customers to trends setting the markets. These insights are used by the business owners to make informed decisions that are driven by data.
Big data is defined as data sets that are larger in volume than basic databases and their handling architecture. Simply put, big data is information that is beyond the scale handled by a spreadsheet like Microsoft Excel. Big data includes the processes of storage, processing, and visualization of the information. To draw insights, businesses need to carefully select big data tools and create a suitable environment around the information.
Differentiating between business intelligence and big data
While the terms “business intelligence” and “big data” are often used interchangeably, there are important distinctions and differences worth noting. Business intelligence is a collection of products and systems put in place for enabling the various business practices; it does not derive.
By contrast, big data is the information derived from the products and systems. Some people distinguish between the two terms by the size of the handled data, while others specify the differences between the approaches to the analytics. Big data generates information from the external expansive sources that are outside of a company’s own resources.
The tools involved in the processes of big data and business intelligence differ as well. Base-level business intelligence software has the ability to process standard data sources, but may not be equipped to manage big data. Other more advanced systems are specifically designed for big data processing.
As you can guess, trying to do big data analysis without specialized tools is impossible. Let’s now turn to the six critical big data software requirements that businesses should consider when making a selection:
Big data software requirements
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 rights 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 a strategic asset. 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 specific metric.
Some of this targeted data could be insights based on location. These data sets gather information and sift data by the 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 login 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.
Hadoop has four modules:
- Distributed file system: This allows storage of data across several linked storage devices.
- MapReduce: This module reads data and converts it into visualizations for easy interpretation by the user.
- Hadoop Common: This module contains the set of Java tools needed for reading the system’s stored data.
- YARN: This controls the resources of the systems that store data and run the analysis.
Big data software requires tight integration with these modules to export results collected in Hadoop to other systems. This integration eases operations, ensures flexibility, and helps facilitate communication in the organization and between several organizations.
In conclusion, big data software requirements need to be approached with the right understanding to help your projects succeed. The above checklist is a good starting point for helping your organization make correct decisions and implement an effective big data analysis operation.
