Big Data Analytics Research Topics

Big data analytics is well-defined as the large and complex datasets to operate the process with all the applicable and standard tools. The required big data engine is functional in the process. The significant processes of assembling with finest elements which are established, seamless and maintainable techniques are essential for the process of big data analytics. In these experienced years, we have finished 300+ projects. So, keep in touch with us for your finest big data analytics research topics.

Why is it important?

Big data analytics is used by analysts, managers, executives, and various customers to access the model scenarios, and insights and to do their jobs and regulate the business. The data analytics process is used to predict the financial products and services for customers to appreciate the service in the process of sales.

Top 10 + Interesting Big Data Analytics Research Topics

Different types of big data analytics

  • Prescriptive analytics
    • The analytics defines a solution for the specific problem and it is functional for both predictive and descriptive analytics. Mainly, prescriptive analytics is dependent upon machine learning and artificial intelligence
    • The use cases in the prescriptive analysis are
    • The algorithm is created through prescriptive analytics and the flight fares based on weather, holiday seasons, customer demand, and oil prices are regulated through this analytics
    • This analytics is significantly functioning for the benefit of airline
  • Predictive analytics
    • The analytics is focusing on the historical and present data to create the finest data prediction for the future
    • Machine learning, data mining, and artificial intelligence are used to analyze the present data to predict the future and it is functioning for the market trends and customer trends
    • The notable use causes in predictive analytics are listed below
    • Predictive analytics is used to create an algorithm that predicts the activities based on fraudulent through the functions of all the historical payment data and user behavior in the company
  • Diagnostic analytics
    • The techniques such as data recovery, data mining, and drilling down are examples of diagnostic analytics. This analytics is used to understand the reason for the problem. In addition, various organizations use diagnostic analytics to provide an in-depth insight into the problem
    • The use cases in diagnostic analytics are listed down
      • Different reasons such as not enough payment options, high shipping fees, loading not proper, etc. are used for the functions of diagnostic analytics
      • The eCommerce company is used to report the sales details and the details about the products added to the cart by the customers
  • Descriptive analytics
    • Descriptive analysis is the description of past data in a form that people can easily read. It assists in report generation such as sales, revenue, and profit for the company. In addition, it assists the tabulation metrics of social media
    • The use cases in descriptive analytics are highlighted below
    • Dow chemical company is used to recognize the underutilized space with the assistance of descriptive analytics
    • The space consolidation assists the company to save up to some million per annum

How big data analytics works

Big data analytics is used to collect the data, process the data, clean the data and analyze data through the assistance of operationalizing the big data

  • Collect data
    • Data collection is the basic process that all organizations will follow and it is used to collect structured data and unstructured data from various sources
    • The unstructured data and raw data is considered complex task to warehouse thus it is assigned to metadata and stored in the data lake
    • The data warehouse is used to collect some data and the business intelligence tools and solutions to access the easy process
  • Process data
    • The data is collected and warehoused for the process. In addition, the collected data should be organized correctly to receive the appropriate results based on the analytical queries, when it is related to the unstructured and massive data
    • The small batches of data are considered stream processing and the process is used to reduce the delay time among analysis and collection and it leads to the fast decision making
    • It is considered the expensive and complex task
    • Batch processing is an option for large data blocks over time and it is beneficial for the huge turnaround in analyzing and collecting data
  • Clean data
    • The scrubbing is essential for data quality enhancement and it is stronger for result production, where the data might be huge or small
    • Format of data is essential and if it is duplicative or irrelevant data, it leads to the elimination
  • Analyze data
    • Big data requires maximum time to state its usability. In addition, big data is turned over into big insights through the advanced analytical process
  • Deep learning
    • It emulates the patterns based on human learning with the functions of artificial intelligence and machine learning
    • The layer algorithm and pattern recognition are functional in data abstraction and the complex data functions
  • Predictive analytics
    • It includes the historical data in an organization and it is functional to predict the future and recognize the forthcoming opportunities
  • Data mining
    • It is used to identify the patterns and the associations among the anomalies by creating the data clusters

The above mentioned are the functions of big data analytics and it is beneficial for all researchers. Significantly, the life cycle of big data analytics is also considered the required knowledge. Thus, our research experts have highlighted the phases in big data analytics.

Lifecycle phases of big data analytics

  • Business case evaluation
    • It states the reason and the intention of the analysis process
  • Identification of data
    • Data is recognized among the broad variety
  • Data filtering
    • The recognized data is filtered and this process eliminates the corrupted data
  • Data extraction
    • Data is transformed into the compatible format non-compatible data are extracted for this transformation process
  • Data aggregation
    • Various data from several data sets are integrated
  • Data analysis
    • The beneficial information is discovered through evaluating data with the statistical and analytical tools
  • Data visualization
    • Using the tools such as QlikView, Tableau, Power BI, and big data analysis is capable to generate graphical visualizations of the analysis process
  • Outcome of analysis
    • It is available for the business stakeholders to precede their further action

In addition, our knowledgeable research experts in this field of big data analysis, big data analytics provide plagiarism-free research reports for scholars. The following is about the application areas in big data analytics.

Big data analytics application areas

  • Smart traffic light
    • Smart traffic is considered one of the finest parts of the smart cities process. The process is used as the regulation of traffic flow within the city
    • The transportation system is developed with all the traffic patterns
    • The process gathers data from all the traffic lights through the creation of an intelligent decision system
    • It is based on real-time big data analytics
  • Smart Education
    • The applications are used to connect the people with the active learning environment and that permits the people to adapt to various changes based on the society and the environment
    • It assists the organizations based on education for the personalized learning process
    • The education processes which are used to enhance the following functions
      • Productivity
      • Efficiency
      • Effectiveness
  • The smart services based on education are even more intelligent and flexible to offer useful data, assistance in all-time learning, developments in assessment and control

Big data analytics research topics

  • The verifiable and secure access control scheme for the big data storage
  • Prediction of traffic flow with big data
  • Unstructured big data analytics for the retrieving eCommerce
  • Communication network time series prediction based on big data method
  • Customer predictability with conversation prediction from the clickstream

To this end, we hope that you get a full knowledge of big data analytics research topics. Now, it is time to select your topic from this field. We assist in all areas based on big data analytics. In this field, we have years of experience.