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What Is Data Analytics Framework – A Comprehensive Guide

Data Analytics is an important function that is executed almost in every sector of the economy. Today business concerns, companies, corporations and industries are dealing with large volumes of datasets every day and trying to manage or optimize data analytics functions smoothly. This article is written to highlight the data analytics framework and what is the component of the data analytics framework. The article will also describe how each component of the data analytics framework is important to enhancing business operations. You are invited to explore a wide range of significant components of the data analytics framework and also educate yourself by understating the types of data analytics. 

Data Analytics Framework

Table of Content 

  • Overview of Data Analytics
  • Types of Data Analytics
  • Introduction to Data Analytics Framework
  • Components of Data Analytics Framework
  • Conclusion
  • Frequently Asked Questions

Overview of Data Analytics

Data Analytics is a dynamic and comprehensive process in which individual data analysts explore, gather, analyse and interpret large volumes of data in order to extract or obtain important information and insights to help business organisations in identifying market patterns, industry trends, customer behaviour and various other essential factors that contribute to informed decision making. Professionally expert data analysts leverage statistical or mathematical methods, data visualisation tools, probability techniques, predictive modelling and more to accurately analyse large volumes of data that are collected from various data sources or channels. These analysts also help many business organisations, companies and industries by enhancing, improving and streamlining their business function as well as operations along with determining associating risk factors and developing risk management strategies.

 

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Types of Data Analytics

Data Analytics is a broad concept or area of the field that encompasses various types of analysis to extract significant insights or information from the associated large data volumes. This section of the article is going to describe the major type of data analytics. Data analytics are classified into four types which are discussed below.

Descriptive Analytics

Descriptive analytics is considered the first and foundational level in the domain of data analytics. In descriptive analytics, data analysts are focused on answering the question of “What happened?” and “How many?”  or industry patterns to obtain a summary of what happened in the past. These pivotal informations are collected by expert data analyst and presented to management and stakeholder for informed decision-making.

Diagnostic Analytics

The next type of data analytics is diagnostic analytics which goes beyond descriptive analytics by answering the question of “Why did it happen” and “What are the major reasons for the happening?”  In diagnostic analytics, data analysts are responsible to determine or try to understand what the reason behind the occurrence of past events, patterns and trends is. They are also deeply involved in investigating or assessing the relationship in each dataset to ascertain the root cause of specific occurrences along with determining and evaluating the major reason or key drivers for occurrence. To conduct diagnostic analytics, data analysts enable or leverage multiple methods or techniques, including machine learning, data segmentation, data clustering or mining, correlation and regression analysis. 

Predictive Analytics

The next branch of business analytics is predictive analytics. This type of data analytics is considered one of the most significant kinds of analytics because it employs data analyst professionals to leverage historical data and statistical algorithms in forecasting or predicting future possibilities of business growth, possible risks, events and trends. Expert data analysts use modelling and machine-learning techniques to forecast future trends & possibilities. Predictive analytics serves as an important intangible asset for business organizations, companies and industries because it answers the question of “What is likely to happen next” & “What might be the outcome result of a specific scenario?”

Prescriptive Analytics

The last significant type of data analytics we are going to understand is prescriptive analytics. Ths branch of analytics holds immense significance because it is regarded as a high-level of data analytics that not only focuses on predicting future outcomes but also assists in suggesting the best possible way or course of action achieve specific business goals and objectives. Prescriptive analytics help numerous business concerns, corporations, industries and companies in optimizing their business strategies, streamline business operations and managing resources by implementing or leveraging multiple analytical tools and techniques. This type of analytics also contributes to an efficient decision-making process by combining predictive modelling with advanced optimization techniques or methodologies. Prescriptive analytics also answers the question of “What strategies should implemented to achieve the desired outcome?” and “What possible action or plan will help in obtaining the best result?”

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Introduction to Data Analytics Framework

The Data Analytics Framework is considered a structured or systematic approach that helps business organisations, companies, industries and corporations along with government agencies in processing, analysing, determining and extracting crucial and meaningful insights from large volumes of complicated datasets. The framework also serves as one of the important foundations for making informed data-driven decision-making while supporting concerning organisations and companies to make critical choices after identifying market trends or patterns, customer behaviours and more within intricate datasets. 

The Data Analytics Framework consists of several components or key stages to facilitate the overall data analysis process and in the next section of the article, we are going to explore each contributing key factor or component of the data analytics framework comprehensively. 

Effective implementation of the components of the data analytics framework plays a significant part in enhancing the operational efficiency of business concerns, optimizing business processes and identifying various growth opportunities available by harnessing the full potential of their data and enabling concerned parties to stay ahead in today’s competitive data-driven landscape.

Components of Data Analytics Framework

As we understand now what Data Analytics Framework is and how it is helping multiple organizations irrespective of their business size along with effectively or efficiently handling as well as managing their overall data analysis processes. It also provides a set of guidelines, techniques, methodologies, tools, and best practices for collecting, processing, analysing, and interpreting large data volumes to derive meaningful insights and support the decision-making process. This section of the article enunciates the key components of the Data Analytics Framework in a comprehensive manner.

Business Understanding

Business understanding is one of the important or first and foundational steps in the data analytics framework under any data analytics project. In this step expert data analysts or related professionals who are working in business organisations, companies and industries are focused on entirely identifying the goals, objectives and the requirement of the projects. This process helps data analysts to determine and understand the requirement of business concerns along with ascertaining a thorough comprehension of the multiple business obstacles that essentially need to be addressed and managed by competent or expert data analysts. In this step, data analysts have to work in collaboration with business domain experts in order to gather relevant information associated with business concerns. 

Data Collection

The second step within the data analytics framework is the process of data collection. Data analyst professionals are responsible for the identification and collection of huge volumes of data from multiple channels of sources, including internal databases, external APIs, surveys, customer feedback and opinion polls for contributing to an in-depth analysis process. During the data collection process, data analysts have to make sure of the quality, accuracy and completeness of collected data. They also have to verify the major source or channel through which data are transmitted and collected while making sure that data is error-free.

Data Preparation

The next key step of the data analytics framework we are going to discuss is data preparation. This is the most significant step after data collection because this step enables data analysts or related professional workers to clean the gathered data and organise or transform it in an efficient manner in order to make collected data suitable for the analysis process. This phase also ensures that the transmission or collection of data is in a consistent and usable format that is ready to be analysed and processed through various analytical techniques. During data preparation companies and industries employ professional data analyst experts to address multiple issues associated with data such as missing values and inconsistencies in large volumes of dataset. With careful preparation of data, the analysts diminish and mitigate the errors, handle missing values, maintain consistency and improve accuracy for further data analysis.

Data Exploration

The data exploration step is also known as exploratory data analysis (EDA). It is one of the crucial steps within the data analytics framework. In this step, data analysts or associating analysts are involved investigation or analysis of the gathered and prepared data to obtain or extract initial insights and relevant information such as market or industry trends, patterns, customer preferences and more. During the process of data exploration, data analysts leverage various types of statistical and mathematical techniques to understand the structure and characteristics of data along with the relationship in each dataset. Data analysts help organisations and companies by exploring data thoroughly to validate assumptions, unravelling potential data threats and other issues as well as formulating hypotheses for further data analysis.

Data Analysis 

This step is regarded as the core step of the data analytics framework because in this step data analysts process the collected or prepared data through multiple methods and techniques including statistical analysis, mathematical or machine learning approaches, analytical techniques and more to extract meaningful insights to obtain significant conclusions. This stage or phase also enables data analysts to determine and draw out valuable information from large datasets in order to address or ascertain various types of business problems related to data and answer each issue systematically. Data analysts during the data analysis process are focused on applying appropriate algorithms, methodologies, techniques and tools to explore the relationship between data, identify patterns, make precise predictions and classify data based on predefined criteria or format. The insights and valuable information extracted from the analysation of the data help business concerns, organisations, companies and industries for identifying favourable business opportunities, determining and mitigating business risks and streamlining business operations along with contributing to inform the decision-making process.

Data Visualisation

This significant step of the data analytics framework comes after the execution of data analysis. Data visualisation is an important process which enables data analysts on related professionals to present data along with its analytical findings in visual format or structure such as bars, charts, tables, diagrams, maps, graphs and dashboards. This is a very supportive process because it helps organisations, companies and industries to understand the intricate or complex structure of large datasets. The step also makes data effortlessly accessible to a wide range of audiences including technical and non-technical. 

By efficiently representation of data and information with the assistance of data visualisation, patterns, trends, and relationships within each dataset become more apparent and comprehensible which enables data analysts and organisations to promote accuracy in a more informed decision-making process. Multiple varieties of data visualization techniques including bar charts, line graphs, pie charts, heatmaps, and scatter plots, are leveraged to befit different data types and analysis goals.

Interpretation & Insight

After the representation of datasets in visualisation format the next step under the data analytics framework is the interpretation of insights from the represented data. In this phase, data analysts are responsible to examine the result derived from complex data analysis and later representing the analytical result in the visualised format in order to obtain significant as well as meaningful conclusions in respect of business concerns and research objectives. During this pivotal process, data analysts interpret trends, patterns, factors affecting, the relationship between datasets, correlation and more to establish a favourable connection between business operations and data volumes. Insights obtained from data should be accurate, precise, clear, error-free, relevant and practical in order to make informed decisions related to business concerns. The process is performed by expert data analyst professionals to help concerned parties in receiving crucial information that assists them in identifying opportunities for business growth. 

Communication of Data

The communication of data is considered an essential step within the structure of the data analytics framework. In this step, data analyst professionals after evaluating interpretation and insights along with gathering all relevant information associated with large volumes of datasets are accountable for presenting the findings, significant interpretations and valuable insights in a clear and understandable or comprehensible manner to senior members, management, board of directors and stakeholders of the concerning business organisations, companies, industries and corporations. Efficient communication of data helps to maintain authenticity or transparency among data analysts and concerned parties. Communication also bridges the gap between decision-making and technical analysis along with contributing to making data-driven insights and information accessible to non-technical audiences. In order to establish effective communication data analysts are advised to select appropriate and easy-to-understand visualisation techniques which accurately and precisely represent the findings.

Implementation

The second last step or component of the data analytics framework is the implementation of insights, findings and recommendations which were extracted through an intricate data analysis process into practical actions. This process starts after the representation and communication of the analytical conclusion and receiving approval for implementation by data analysts in collaboration with cross-functional business teams and stakeholders in order to integrate the data-driven results into existing processes, strategies along with decision-making frameworks. During the whole implementation process, data analysts have to work closely with concerned business teams, management and stakeholders to address any challenges, obstacles and affecting factors that may arise. They are industry professionals who are responsible for providing support and guidance to ensure a smooth integration of data-driven insights into practical applications. Successful implementation of data-driven insights helps organisations, businesses, companies and industries to achieve multiple benefits including optimization of business operations, increasing efficiency and enhanced business performance along with improved decision-making process.

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Monitoring & Evaluation 

The last and most important component of the data analytics framework is monitoring and thorough evaluation of the consistently ongoing process of tracking the success, performance and effectiveness of the implemented data-driven applications. This step of monitoring and execution is performed under the supervision of expert data analysts who aims to measure the precise impact of the changes that occurred in businesses based on data insights. During the monitoring process, data analysts continuously gather relevant and crucial data and metrics to gauge the progress of the implemented solutions by comparing the actual results with the expected or desired outcomes and identifying any deviations or discrepancies that need attention. Consequently, an evaluation process involves data analysts conducting a comprehensive in-depth analysis of the data volumes in order to ascertain and determine the success ratio of data-driven initiatives. Evaluation helps in identifying or accessing whether implemented data have achieved its intended objective or it might need some essential improvements. Consistently monitoring and evaluating the impact of data-driven decisions aids organisations, companies, industries and corporations in reviewing their strategies, making improvements and enhancing business operations along with enabling them to adapt to changing business conditions. 

Conclusion

Data Analytics is a broad concept in which every sector of the economy is deeply involved because large amounts of datasets are generated consistently. It also has a major and significant contribution to business growth along with profitable success. By leveraging data analytics organisations, companies, industries and corporations unveil various types of trends, patterns and correlations in respect of data volumes. The article aims to provide understating of the core concept of data analytics and which is the data analytics framework by describing each component of the framework comprehensively. The article also highlights work done by data analysts and related professionals within each data analytics framework.  I hope that my article will be able to provide sufficient and precise information regarding data analytics framework efficiently. 

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Frequently Asked Questions

Q. What are the factors affecting the data analytics framework?

Several crucial factors can impact the data analytics framework such as sources of data, quality of data, technological advancement, complexity of data, expertise of data analysts, market or industry trends, regulatory changes, ethical considerations, integrity of data, availability of resources, analytical tools or techniques and evolving business requirements. Addressing these significant factors ensures a robust and effective data analytics framework that provides valuable insights and also continues to support data-driven decision-making.

Q. What skills are required by data analysts to execute and maintain the data analytics framework?

There are a variety of skills that are needed along with qualifications to execute and streamline the components of the data analytics framework efficiently. Below mentioned skills are required by every data analyst professional.

  • Technical Skills
  • Analytical Skills
  • Programming Languages Skills, including R, Python, SQL and more.
  • Data visualisation tools & techniques
  • Strong Problem-Solving Ability
  • Mathematical and Statistical Skills
  • Knowledge of Data Manipulation
  • Machine Learning Skills
  • Presentation and Reporting Skills
  • Communication Skills

Q. What are important tools that are required for the implementation of a data analytics framework?

For efficient implementation of structured or systematic data analytics framework there are several important tools required. The tools include data visualisation tools such as Tableau, PowerBI, Google data studio, Microsft Excel, Plotly, Infogram and more. Statistical software tools, including SPSS or SAS and databases tools like SQL for data allocation, storage and retrieval. 

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