Report on Different Types of Data Analytics Frameworks

Introduction

            The essential tool castoff to identify the complications in today’s market scenery. In order to determine the tactical decision in order to enhance the data operations is data analytics. The Data Analytics Frameworks is the genetic outline method to build up the process of data analytics with necessity techniques. Data analytics agenda is a foundation to insight the real-time data applications. It also involve in predicting the risk managements involved in the open- platform of data integration. Depending upon the conditions and dynamic approaches of market, data analytics outline is available various types. (Olajiga, Olu-lawal, Usman, & Ninduwezuor-Ehiobu, 2024). Data Analytics Frameworks are available in different formats that include:

1. Data Analytics Frameworks: Traditional Business Intelligence (BI) Framework

           Among Data Analytics Frameworks, Business Intelligence Frame work is important. Competitive intellect framework is a process applied on operating stage of every business organization. Looking to observe the competitors in the market and implementing the tactic methods to change the state. and also its affairs in order to enhance their position. Firms utilizing BI framework to analyse the financial conditions and collect the information about the competitive characteristics of future tendencies (Ranjan, 2005).

• Data Collection: In Business Intelligence framework, the information should be gathered from different websites, articles and sieve the data to correlated content which supports to the tactic plan of management.
• Data Warehousing: It is the process of keeping the filtered data in a specific format and permits to analysis process. The structured data should be stored in data storage component like MongoDB. The data is useful for further process in future to determine the capability of a company.
• Data Transformation: After collecting and storing the information, the next step in business intelligence is to analyse the data which is useful to capitalize the business and to determine proper decision to reach the goal.
• Reporting and Dashboarding: The resulted data after examining process reports through network-based application in order to deliver the information to shareholders by dashboarding techniques like graphical, mapping and histogram charts.
• OLAP (Online Analytical Processing): Multidimensional analysis is a tool to represent the business operations like broadcasting, examining, demonstrating and scheduling. This tool wraps the traditional techniques to invest new trends to reach out position of the business market.

Advantages

            Competitive framework provides numerous benefits such as; resolving the business complexities, redirecting the economic situations, increasing the performance of an organization and analysing the potential risks (Adil & Abdelhadi, 2022).

2. Predictive Analytics Framework

            The framework which is used to insight the predictions of future outcomes. It might apply in daily activities contracts with statistical probability functions. Predictive framework utilizes the scale of competitive framework methodologies in order to address any tenses. It is used to determine the future patterns with the help of arithmetical methods like MI, mining and modelling of data (Kumar, Harshak, Syamanth, Viswanadh, & Kumar, 2022).

• Data Collection and Preparation: The Information should be crawling from interior and peripheral sources then correct the reliability of information and the important step is to processing the gathered information to resolve the risks flaws.
• Statistical Modelling: In addition, Various statistical models like machine learning algorithms and regression were taken into the account to handle the supply chain management and it is effective step to determine the predictive analysis of future patterns and trends and also figuring out the favourable outcomes.
• Model Training and Evaluation: In order to achieve accurate predictions, the real-time monitoring process must be train using arithmetical methods of machine learning algorithms. Furthermore, this training helps to predict classified outcomes and determine the training and testing factors of the data provided to regression. Ultimately, the goal of the training process is to detect the predicted values of favorable outcomes. Moreover, by integrating these techniques, the system can effectively analyze and forecast data in real-time.
• Deployment: The process use to deploy the statistical methods into real-time monitor tracking models and automatically determine the numerous activities like lead sales, trend parameters and increase efficiency
• Continuous Monitoring and Refinement: Continuous monitoring helps to figure out the warnings and attentions while analyzing the values and constant monitoring might identify the economic performance of a firm before optimizing the forecasting values.

Advantages

            The predictive frameworks predict the future analysis of small, medium and big organizations and also determine supply chain management risk complexity and providing seamless opportunities by improving the adaptability. The framework use to spot the industrial risks and manages the dynamic changes in the business environment (Aljohani, 2023).

3. Prescriptive Analytics Framework

            Prescriptive is a mature analytics framework over other two frameworks. After predicting the favorable outcomes of the decision, the team implements the prescriptive analytics framework process. In order to avoid risks, the team suggests safeguard measurements in the prescriptive analytics framework.” Furthermore, the prescriptive framework undergoes various processes such as digital and network processing, pattern and speech recognition, etc., to identify the veracity of managing the decision.(Khoshbakht, Shiranzaei, & Quadri, 2021).

• Predictive Modelling: In a predictive analytics framework, different model are considerable. They are probabilistic models, statistical models, and logic-based models used to forecast future outcomes.
• Decision Analysis: Three levels are applicable in decision-making: evaluating decisions at the initiative level, division level, and specific level (Vater, Harscheidt, & Knoll, 2019).
• Optimization Techniques: Optimization techniques describe actions. Probabilistic techniques and bootstrap robust optimization techniques drive information and handle real-world complex conditions. Optimization and replications help in making good choices in a prescriptive framework (Lakshmanan, Sornam, & Flores, 2020).
• Decision Support Systems: Furthermore, Markov decision support systems and decision automations are the two stages in this framework that are very important to enhance the effectiveness of the predictions made by humans. In addition, formatted and unformatted information capture the impact of the decision, while simulation supports the rule-based systems to activate decision making in prescriptive analysis.
• Implementation and Monitoring: implementation of decisions systems generates and estimate the potential outcomes. Monitoring continuous outcomes helps to track real-time events (Lepenioti, Bousdekis, Apostolou, & Mentzas, 2019).

Advantages

            Improves the systematic approaches, searching the possibility of outcomes and enhance the sustainability performance and used to optimize the economic outcomes.

4. Real-Time Analytics Framework

            The framework is responsible for real-time data and real time views and having two different architectures processing the data techniques with multiple layers called batch, speed and serving layers used to store the data for future analytics  

• Data Streaming: In data ingestion circumstances, data directly enters with specific speed and time creates unrelated data streams. To reduce this, streaming or ingestion algorithms are crucial to accomplish the necessities
• Complex Event Processing (CEP): The objective of an event processors is to create emergency events for incoming data streams. If a new data arrive into event processor, non- vocabulary words will be clean as it use using classifier
• In-Memory Computing: The feature enables the computed data streams much faster than another analytical tool and extract the insights of quality of the streamed data.
• Alerts and Notifications: Real-time data analytical frame work is essential to provide the emergency notifications to maintain advanced security to make decisions. If a data stream enter in the CEP process, then after completion of the process a notification and alert mechanism will be insert in the framework taking suitable action if any fire incidents are happens.
• Interactive Dashboards: In real time analytical engine dashboards monitor real time sources in order to attain restored logical outcomes.

Advantages

            The frameworks in real-time applications will detect the crime incidents, improves accuracy and flexibility. It make accurate decisions to achieve analytical outcomes. It provides emergency notifications to the firms as well as people who using this framework (Manjunath & Annappa, 2020).

5. Cognitive Analytics Framework

            Another important Data Analytics Frameworks. Advanced analytics introduced new framework with variety of applications to determine the tactic knowledge about the different AI training methods and data visualization of human activities for structured and unstructured data. Cognitive framework implements various artificial intelligence techniques to detect the fraud in machinery industries.

• Natural Language Processing (NLP): Cognitive frameworks implemented with machine learning technique. It is used to enables the human language in depth of unformatted and formatted data.  
• Machine Learning Algorithms: Many online training methods drive the knowledge to recognize the patterns. Such patterns have ability to maintain accurate predicted results and tarin the AI models.

• Pattern Recognition: In cognitive framework, many visualizations models map the data anomalies every minute and recognize the behaviour of data.  
• Contextual Awareness: Contextual analysis examines the data from various resources to a document. It also create awareness to recognize the spelling the document.
• Advanced Analytics: Cognitive platforms detect the faults in the combined information using advanced analytics methods. Then to evaluate the capabilities of framework (Rousopoulou, et al., 2022).

Advantages

            It explores the deep learning techniques through human understanding languages, enables the capability to capture the social media trends. Also supports the various advanced tools to centralize the memory (Ducharme & Angelelli, 2014)

Conclusion

            Large volume of data is realizes everyday in structures and unstructures format with tremendous measurements to develop the technologies. The capability to gather that information is little complex process, have chance to get fraud data in huge amount. In order to reduce the problem, the big data analytics are introduces above mentioned frameworks to make the process simple. It is also easier with less complexities. Many organizations utilize different types of Data Analytics Frameworks based on the economic conditions. The frameworks came up with various algorithmic methodologies to determine and to predict the analytical outcomes. They depends on the requirement of the organization (Khoshbakht, Shiranzaei, & Quadri, 2021).

Adil, B., & Abdelhadi, F. (2022, January 15). A Big data Analytics Framework for Competitive Intelligence Systems. Journal of Theoretical and Applied Information Technology, 100(1), 149-169.

Aljohani, A. (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability, 15(20), 1-26.