Data understanding is a very important step in data analysis. It should be considered as a first step to do as soon as you have the data for visualization. Data Understanding helps to contextualize the data by identifying its source, schema, limitations, and the specific problem statement or question it aims to address. This contextual information is crucial for interpreting the data accurately and avoiding misrepresentations.
Data understanding involves identifying the relevant attributes for analysis. With an understanding of the data, analysts can determine which variables are important and which need further exploration. This ensures that the analysis focuses on the right aspects of the data and helps derive meaningful insights.
After identifying the different variables that are important for the analysis the next step is to identify the other dimensions or attributes that are not required in the analysis based on the problem statement. Data Cleaning and preparing the data are the next steps in data analysis. Understanding the data allows analysts to identify and address data quality issues such as missing values, outliers, or inconsistencies.
Data Understanding enables the identification of patterns, relationships, and trends within the data. It helps in detecting outliers, correlations, distributions, and other important characteristics that drive further analysis and inform decision-making processes. It also helps in mitigating biases and assumptions. By being aware of potential biases in the data, analysts can take steps to address and minimize their impact on the analysis, leading to more accurate and objective results.
So we can say that data understanding enhances the effective communication of findings. Analysts can present the results in a clear and meaningful manner, making it easier for stakeholders to comprehend and make better decisions.
We have covered the overall summary of why Data Understanding is important in data analysis, Lets start digging into the Data Understanding step by step so that a clear understanding of these steps can be developed. We will cover the major steps that are involved in the data understanding part
1. Identifying Relevant Variables and Attributes
Identifying relevant variables is a crucial step in data analysis as it directly impacts the accuracy and validity of the insights derived from the data. By understanding the data, analysts can determine which variables and attributes are essential for the analysis and which ones may not significantly be used in the final analysis. Here’s why this step is important:
a) Focus on Key Factors: Identifying relevant variables helps analysts focus their analysis on the key factors that are likely to have a significant impact on the problem statement or pain points of the client. By excluding irrelevant variables and attributes, analysts can streamline their efforts and allocate resources more effectively.
b) Reduce Noise and Complexity: Including unnecessary variables in the analysis can introduce noise and complexity, making it harder to identify meaningful patterns and relationships, hence more difficulty in data visualization. By identifying relevant variables, analysts can simplify the data analysis, reduce noise, and have more clarity on the outcome of the data.
c) Optimize Resource Allocation: Data analysis often involves limited resources such as time, computational power, storage, and budget. By identifying relevant variables and attributes, analysts can allocate their resources efficiently to focus on the most important aspects of the data. Identifying relevant resources is beneficial for the cloud storage and computation power of the available resources. This ensures that resources are not wasted on analyzing irrelevant or insignificant variables.
d) Avoid Biases and False Conclusions: Including irrelevant variables and attributes in the analysis can introduce biases and lead to false conclusions. Irrelevant attributes may have a weak or no relationship with the desired outcomes, and their inclusion can distort the analysis results. One single conclusion based on irrelevant variables or attributes can change the overall perspective of the stakeholders towards the findings from the data. By identifying relevant variables and attributes, analysts can ensure that their analysis is based on the most influential factors and reduce the risk of drawing incorrect conclusions.
e) Enhance Interpretability and Actionability: Analyzing relevant variables and attributes allows for a clearer interpretation and presentation of the results. It becomes easier to understand and explain the relationships and insights derived from the data when they are based on the most relevant variables. Moreover, focusing on relevant variables increases the chances of actionable insights that can be used to drive decision-making processes effectively.
2. Data Cleaning and Preparation
Data cleaning and preparation are crucial steps in the data analysis process. These steps involve identifying and addressing the issues related to data quality, transforming the data into a suitable format, and ensuring that it is accurate, complete, and reliable for analysis. Here’s why data cleaning and preparation are important:
a) Accuracy and Reliability: Data can be prone to errors, inconsistencies, missing values, datatype issues, and outliers. By performing data cleaning, analysts can identify and rectify these issues, ensuring that the data used for analysis is accurate and reliable. Clean data leads to more trustworthy and robust insights.
b) Consistency and Standardization: In many cases, data comes from multiple sources and in different formats. Data cleaning involves standardizing data elements, like formatting dates consistently, handling categorical variables, currency formats, and resolving discrepancies in units or measurement scales. This consistency enables accurate and meaningful comparisons and analysis across the dataset.
c) Missing Data Handling: Missing data is a common challenge in data analysis. Data cleaning includes strategies to handle missing values, such as imputation techniques or deciding on appropriate methods for dealing with null values. Addressing missing data ensures that the analysis is based on a complete dataset and reduces the risk of bias in the results.
d) Outlier Detection and Treatment: Outliers are extreme values that can significantly impact the analysis and distort the results. Data cleaning involves identifying and handling outliers appropriately, whether it’s removing them, transforming them, or treating them as special cases. By addressing outliers, analysts can ensure that their analysis is not unduly influenced by extreme values.
e) Data Transformation and Normalization: Data cleaning often includes transforming data into a suitable format for analysis. This may involve scaling variables, normalizing distributions, setting the decimal points for decimal numbers, normalizing the data types, or creating new derived variables that better represent the underlying relationships. Data transformation can improve the interpretability and effectiveness of the analysis.
f) Data Validation: Cleaning the data involves conducting checks and validation to ensure its integrity. This includes identifying duplicate records, blanks and nulls, missing values, cross-checking data against external sources or benchmarks, and validating data against predefined rules or constraints. Data validation helps maintain data quality and help with effective analysis.
g) Efficiency and Performance: Clean and well-prepared data enables more efficient and effective analysis. When the data is in a standardized and suitable format, analysts can focus their efforts on exploring insights and applying analytical techniques instead of spending excessive time on data cleaning during the analysis phase.
At Digifloat, we make sure that the Data Understanding part is properly done by our analysts and that all steps are taken care of so that more valuable insights into the data can be provided to our clients.
In conclusion, data understanding, identification of relevant variables, and data cleaning and preparation are essential aspects of data analysis. These processes lay the foundation for accurate and reliable insights, effective decision-making, and impactful data-driven outcomes.
Data understanding allows analysts to summarize the data, grasp its nuances, and interpret it accurately. It helps in identifying the data source, methodology, limitations, and the specific problem or question at hand. This understanding sets the stage for meaningful analysis and prevents misinterpretations.
Identifying relevant variables ensures that the analysis focuses on the key factors that are most influential in addressing the problem or question. By excluding irrelevant variables, analysts can streamline their efforts, reduce noise and complexity, and allocate resources efficiently. This process helps derive accurate insights and enhances the interpretability and actionability of the analysis results.
Data cleaning and preparation play a crucial role in ensuring data accuracy, reliability, and consistency. By addressing data quality issues, such as errors, inconsistencies, missing values, and outliers, analysts can trust the integrity of the data. Standardizing formats, handling missing data, and treating outliers appropriately contribute to robust analysis results and reduce bias.
Together, these processes pave the way for effective data analysis and interpretation. Accurate insights derived from clean and relevant data empower stakeholders to make informed decisions, identify patterns and trends, and uncover meaningful relationships. Furthermore, they enhance the communication of findings, leading to increased understanding and engagement among stakeholders.
Ultimately, data understanding, identification of relevant variables, and data cleaning and preparation are integral steps that ensure the reliability, accuracy, and interpretability of data analysis. By adhering to these practices, analysts can unlock the full potential of data, harness its power, and drive data-driven success in various domains and industries.
Gulfam Pervaiz is a data analytics consultant at Digifloat with 4+ years of industry experience. With expertise in Data Visualization, BI Reporting, he is dedicated to helping his clients by delivering the insightful visualizations for data-driven decision-making.