Data analysis, like any scientific discipline, follows a structured, methodical process. Each stage requires specific expertise and skills. However, to derive meaningful insights, it’s essential to understand how all the stages fit together as part of a unified framework. This approach ensures that the results are reliable and robust.
This blog explores the key stages of the Tableau-driven data analysis process.
Phase 1: Data Discovery and Formation
Every successful journey begins with a purpose. This phase aims to define your desired data objectives and determine the best approach using the data analytics life cycle. This initial stage involves evaluations and assessments to develop a foundational hypothesis for solving business challenges or issues.
The first step involves assessing the data for its potential uses and identifying key questions such as its origin, the insights it provides, and how it aligns with your business objectives. It is necessary to evaluate internal infrastructure, resources, time, and technology to ensure they align with the data.
After completing these evaluations, the team devises hypotheses to be tested later. This stage sets the foundation for the subsequent phases of data analytics life cycle. Collaborating with a Tableau consulting company also helps organizations streamline this process and gain clarity on how best to leverage Tableau tools for data analysis.
Key Takeaways
- Explore and understand the challenge
- Establish context and insights
- Identify the data sources that are required and available for the project
- Develop preliminary hypotheses that can be validated through data, with support from a Tableau consultancy
Phase 2: Data Preparation and Processing
Data preparation and processing are crucial for making the collected data suitable for analysis with Tableau tools. This phase involves gathering, sorting, processing, and cleaning the data to ensure it is ready for further assessment. One critical element of this phase is ensuring that all necessary information is accessible before moving ahead.
Various methods employed during this phase for data acquisition are:
Data Collection: Gathering information from external sources
Data Entry: Creating new data points within an organization, either through manual input or digital technology
Signal Reception: Collecting data from IoT devices and control systems
An analytical sandbox, supported by Tableau professional services, is a key tool during data preparation. It provides a secure platform for data analysts to process and tweak datasets before analysis. This stage of the data analytics life cycle may not follow a specific sequence and may require repetition as needed. Working with Tableau consultancy also streamlines data preparation by ensuring the right tools and processes are in place to handle data efficiently.
Key Takeaways
- Ensure data readiness by gathering, sorting, processing, and cleaning data
- Employ data acquisition methods based on project needs
- Leverage analytical sandbox to enhance the efficiency of the preparation phase
- Avail Tableau implementation services to ensure proper tools and processes for data handling
Phase 3: Design a Model
After defining business goals and gathering substantial amounts of data (in various formats), the next step is to design a model that uses the data to achieve those objectives. This stage, known as model planning, involves selecting methods for loading and analyzing the data.
Several approaches for integrating data into the system include:
ETL (Extract, Transform, and Load): Data is transformed before being loaded into the system according to set business rules.
ELT (Extract, Load, and Transform): Data is first loaded into the system and then transformed.
ETLT (Extract, Transform, Load, Transform): A hybrid approach that combines transformation and loading processes.
This phase also involves collaboration to identify the methods, techniques, and workflows to develop the model in the next phase. A Tableau consulting partner provides insights into which Tableau tools best suit your model design and help optimize data flows to achieve business goals.
Key Takeaways
- Establish the methods and techniques for model integration
- Introduce ETL, ELT, and ETLT as approaches for data transformation and loading
- Collaboration ensures that tools and workflows align with business goals
- Optimize the model design process with the help of Tableau consultancy
Phase 4: Model Building
In this stage, datasets are created for testing, training, and production. Data analytics professionals develop and run the model they designed in the previous stage. Tools and techniques, such as decision trees, regression, and neural networks, are used to construct and test the model.
Testing the model helps determine if the existing tools and systems support its execution or if a more robust environment is crucial. Tableau implementation partners ensure your model integrates seamlessly with Tableau’s advanced data visualization capabilities, enhancing model testing and evaluation.
Key Takeaways
- Prepare datasets for testing, training, and production
- Evaluate the robustness of current tools and determine if a more robust system is necessary
- Use open-source tools like R, Octave, and WEKA for model-building
- Opt for Tableau professional services to enhance model deployment and visualization capabilities
Phase 5: Result Communication and Publication
It’s time to evaluate if the objectives in Phase 1 were met. This phase involves collaborating with stakeholders to assess whether the project’s outcomes align with the initial goals. The team identifies key findings from the analysis, quantifying the business value of the outcomes, and crafting a narrative to communicate these results to stakeholders. It helps translate these findings into visually interesting dashboards, making it easier to communicate results to stakeholders and drive informed decision-making.
Key Takeaways
- Evaluate whether the objectives from Phase 1 were achieved
- Identify key findings and quantify the business value of outcomes
- Create compelling narratives using Tableau dashboards to share insights
Phase 6: Measuring Effectiveness
As the data analytics life cycle concludes, the final stage involves presenting stakeholders with a comprehensive report, including results, code, briefings, and technical documentation. To assess the effectiveness of the analysis, transfer the data from the sandbox to a live environment and verify if the results align with the intended business objectives.
If the outcomes meet expectations, finalize the reports and findings. If the results do not align with the goals defined in Phase 1, the team may revisit earlier stages in the data analytics life cycle to adjust inputs and refine the approach. Tableau implementation services are invaluable during this phase. They ensure effective communication of results and quick integration of necessary adjustments into the system.
Key Takeaways
- Present stakeholders with a comprehensive report and documentation
- Transfer data from the sandbox to a live environment for validation
- Revisit earlier stages if results do not align with initial objectives
Conclusion
Tableau is a powerful data visualization platform. However, the trick lies in implementing it in the right way to get insights from day one. Tableau consulting partners help businesses maximize the value of their data analytics initiatives. The consulting partners also ensure the tools and processes are aligned with their strategic goals. Such two-fold benefits make engaging Tableau experts a powerful investment, helping businesses achieve their goals faster.
The post Breaking Down the 6 Phases of Tableau-Driven Data Analytics appeared first on Datafloq.
