There is a distinction between data wrangling and data engineering in data analytics. These two processes form the backbone of any data analytics pipeline, each serving a unique purpose. This article delves into these concepts, particularly in the context of Power BI. We will explore the intricacies of data wrangling and data engineering, the importance of data preprocessing, and the collaborative nature of data analytics.
What is Data Wrangling
Data Wrangling entails refining, organizing, and augmenting unprocessed data into a well-defined analytical dataset, which is efficient for decision-making. In addition, the process involves converting and correlating data from its initial state into a different format, making it easier to access and utilize. This is part of the larger conversation around the 8 Stages of Data Preparation for data projects.
In the context of Power BI, data wrangling can be done using Power Query. This tool allows you to connect to various data sources, clean and transform your data, and then load that data into a model for reporting.
What is Data Engineering
On the other hand, data engineering is a broader field that includes data wrangling but also involves designing and developing systems and processes for managing and storing data. It’s about building robust, scalable, and secure data infrastructure and pipelines.
In Power BI, data engineering tasks might include setting up data refresh schedules, managing data storage, and ensuring data security and compliance. This can consist of the overall solution architecture and design.
In essence, while data wrangling is more about dealing with individual datasets, data engineering is about building the systems and processes that make handling those datasets possible.
To learn how to develop Power BI solutions, start by reviewing the logical architecture of your data visualization projects. Power BI solutions have many moving parts, especially considering the various available data sources. These sources can range from on-premises to web-based and public to confidential, so it’s important to ensure data security. A clear and solid Power BI solution architecture is critical to achieving a good and secure solution.
Data Preprocessing is Key to Both
In the context of Power BI and data engineering, data preprocessing plays a pivotal role in analytics. Data Wrangling is the crucial first step in the data analytics pipeline, setting the stage for all subsequent analysis and visualization tasks. See Power BI data lineage experience for dataflows, Which helps show the data flow from the data source to its destination and helps you see the data transformation done along the way.
Data preprocessing involves cleaning, transforming, and normalizing raw data to prepare it for analysis. This can include dealing with missing or inconsistent data, removing duplicates, converting data types, and normalizing numerical data. In Power BI, tools like Power Query make it easy to perform these preprocessing tasks.
Why is data preprocessing so important? Simply put, the quality of your output is determined by the quality of your input. It’s the adage, “Garbage in – Garbage out.” If your data is messy or inconsistent, it can lead to inaccurate results and misleading insights. By investing time in data preprocessing, you can ensure that your data is clean, consistent, and ready for analysis.
You may also find the following article, which breaks this into eight stages; From Chaos to Clarity: The 8 Stages of Data Preparation.
Moreover, data preprocessing can also help to make your data more understandable and easier to work with. By transforming your data into a more suitable format, you can simplify the analysis process and make it easier to extract meaningful insights.
A good real-world example I had, involved a client that wanted to connect to Azure SQL Server using R-Scripts in their environment. I documented the example, Connecting R Scripts to SQL Azure for Data Discovery and Profiling.
In the Power BI and data engineering world, data preprocessing is not just a preliminary step – it’s a vital component of the analytics process. You can set the foundation for successful data analytics by understanding and effectively implementing data preprocessing.
Why is Data Wrangling Important?
Data Wrangling is all about putting together a working copy of the data. I feel it is the step that creates that clean set of data. The outcome of Data Wrangling is correcting inaccurate data or problems with the data feed. Always go back to the source. Could a data collection issue be fixed for future data feeds?
Here are some of the steps I feel are most important at this stage:
- Identifying and removing duplicates – To ensure accurate analysis, it is important to check for duplicate data points in the dataset and remove them. However, it is essential to carefully define what constitutes duplicate data to prevent the loss of valuable information. This may include instances where data is entered twice, ranges are missing, or there are differences in measurement scales.
- Handle missing data – When handling missing data, it is critical to consider variables such as the category and scope of the missing data. It is essential to consider how it may impact the analysis. Depending on these factors, one may select a suitable approach, such as assigning a value based on the available data or eliminating it.
- Address outliers – Identify outliers that differ markedly from other data points, and decide whether to keep or modify them. Consider whether the outliers correspond to meaningful or erroneous data requiring further examination.
In this Data Cleaning step, you mainly remove errors, duplicates, and irrelevant data to ensure accuracy and consistency based on the exploration phase results. This can be one of the most challenging yet essential steps if you are evaluating the data for the first time.
Note: Be sure to make notes and detail what you are doing, as you always need to show your work and back up any decisions you make at this stage. Decisions here could affect the results of the analytics, and you always need to explain your work!!
Data Analytics is a Team Sport
Indeed, data analytics is a team sport. It requires collaboration between different roles, including data engineers, data scientists, data analysts, and business stakeholders. Each role brings a unique set of skills and perspectives to the table.
Data engineers build the infrastructure and tools needed for data generation, collection, and storage. They also ensure that data is clean, reliable, and readily accessible.
On the other hand, data scientists and analysts use this data to build models and algorithms that can extract insights from the data. Therefore, they need a strong foundation in mathematics and statistics and expertise in programming languages like Python or R.
Data analysts use the outputs of these models to generate reports and visualizations that can inform business decisions. Therefore, they need strong analytical skills and a good understanding of the business context.
Finally, business stakeholders use these insights to make informed decisions that drive the organization’s strategy and growth.
In Power BI, these roles come together in a shared environment. The platform allows data engineers to manage data, data scientists to build models, and data analysts to create reports and dashboards. This collaborative approach ensures that everyone has access to the data and insights they need when they need them.
What Organization Roles are Involved?
The following table is a portion of the article, Winning Project Management For Power BI Projects. Ensuring data quality, relevance, and iterative, agile, step-by-step approaches are essential elements that play a significant role in achieving Power BI project success. Your data project today has a wide range of deliverables. It is a data-gathering, processing, governance, security, and visualization project all rolled into one.
| Stakeholder | Role | Contribution to the Data Wrangling & Data Engineering |
|---|---|---|
| Data Analyst | Analyzing and interpreting data | Data manipulation, statistical analysis, insights generation |
| Data Engineer | Managing data infrastructure | Data collection, storage, integration, and preprocessing |
| Data Scientist | Developing advanced analytics models | Machine learning, AI, predictive analytics |
| Data Steward | Responsible for maintaining data quality, governance, and consistency | Owner of a portion of the data. Creating standard definitions and ensuring trust in data resources. |
| Data Wrangler | Data Processing and physical handling of data | Data Processing, preparation, and cleansing |
| Domain Expert | Providing industry-specific knowledge | Business context, domain-specific insights, best practices |
| IT Specialist | Ensuring technical support | System maintenance, security, network administration |
| Quality Assurance | Ensuring product quality | Testing, validation, defect identification, and resolution |
| Senior Management | Setting strategic direction | Decision-making, resource allocation, project prioritization |
| Database Analyst | Optimizing database performance | Database design, query optimization, performance analysis |
| Data Modeler | Creating data models | Logical and physical data modeling, schema design, data normalization |
| Data Visualization Designer | Crafting visual representations | Data visualization design, storytelling, interactive visuals |
Data Wrangling and Data Engineering for Power BI isn’t a solo endeavor. Instead, it’s a team sport that requires collaboration and communication between different roles. By understanding and appreciating the contributions of each role, you can create a more effective and efficient data science team.
Conclusion
In conclusion, data analytics is not a solitary pursuit but a team sport that necessitates collaboration and communication among various roles. Power BI is a shared platform that allows data engineers, data scientists, and data analysts to work together effectively. The Data Wrangler has an integral role to play.
By understanding and appreciating the contributions of each role, you can create a more effective and efficient data science team. This understanding and knowledge of data wrangling and data engineering set the foundation for successful data analytics with Power BI.
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