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Learn about data cleaning, enrichment, and transformation techniques to prepare your data for accurate and reliable analysis.
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Data transformation is a complex process of converting raw data into a suitable format for analysis. This involves several steps, including data cleaning, data enrichment, and data transformation. But why? And do we? need to transform data in the first place? Data in its raw form is complex and confusing, but once transformed, it plays a major role in the analysis as it helps in identifying patterns, trends, and insights that might not be apparently visible in raw data. However, once the data is transformed, it may serve as the most important asset to any organization.
IBM research says that the yearly cost of poor data quality in the United States is $3.1 trillion. And, unless the data is analyzed, it is difficult to identify the error, and by then, the cost per client rises due to the lower conversion rate.
In today’s blog, let us see how to carry out this task of transforming the data at hand for better analysis that directly impacts revenue.
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It is critical to clean any impurities from the data before placing it on the worksheet. Data cleaning is an essential step in the data transformation process. It is about finding and correcting errors, inconsistencies, and inaccuracies in raw data to ensure that the data is reliable and accurate for analysis. So how can you clean the data that can help you achieve more accurate results? Let's see:
Data profiling is analyzing and reviewing data to gain a better understanding of its quality and structure. It helps to identify issues in the data, such as missing values, inconsistent data types, and duplicates. Data profiling can be done using various tools such as Excel or Python. For example, if you have a dataset with client information, data profiling can help you identify if there are any missing phone numbers or email addresses.
Data auditing is verifying and reviewing the accuracy and completeness of the data. It helps to identify data that is incorrect, incomplete, or inconsistent. Auditing can be done manually or using automated tools. For instance, you can audit a dataset of sales transactions to ensure that all transactions have been recorded accurately.
Data documentation is creating a record of the data and its characteristics which includes information such as the source of the data, the way it was collected, and any transformations or cleaning that was done. Documentation ensures that the data is consistent and can be easily understood by others who are analyzing the data. Let’s say you create a dataset of customer feedback that includes the date of collection, survey questions, and response options. This is nothing but documentation.
Data validation is checking the accuracy and completeness of the data against predefined rules. Validation helps to ensure that the data is consistent and that it meets the required standards. Data validation can be done using automated tools or manually. For instance, you can validate a dataset of employee records by checking that all employees have a valid email address and id number.
Data monitoring is continually checking the data for any changes or errors. This helps to ensure that the data remains accurate and updated. Data monitoring can be done manually or using automated tools. For instance, you can monitor a dataset of social media traffic to ensure that the data is being recorded correctly and that there are no sudden spikes or drops in traffic.
Data cleaning ensures that your data is trustworthy and correct for analysis which can help to improve the quality of your analysis and ensure that your results are meaningful and actionable.
Data enrichment is like adding that additional ingredient to amp up the dish. It is the process in which additional information is added to your dataset to make it more informative and valuable for analysis. The objective is to provide a richer and more comprehensive view of the data so that it can reveal deeper insights and patterns. Some techniques for data enrichment are:
Data augmentation involves adding new data to your dataset that is related to the existing data. This dataset can include adding new variables, such as demographic information or observations. Let's say you have a dataset of client transactions; you can augment this data by adding customer demographic information such as age, gender, and location.
Data integration combines data from multiple sources to create a more comprehensive dataset. This integration can be done manually or using automated tools. For example, if you have a dataset of customer transactions from a point-of-sale system, you can integrate it with data from a CRM system to get a complete view of the customer.
Data normalization involves organizing data into a consistent format to make it easier to analyze. This normalization can include standardizing data formats, converting units of measurement, or removing inconsistencies. Let’s say you have a dataset of sales revenue from different countries that have different currencies; you can normalize the data by converting all the values to a common currency.
Data categorization involves grouping data into categories based on common characteristics. Categorization can simplify complex datasets and make them easier to analyze. For example, if you have a dataset of customer feedback, you can categorize the feedback into different topics such as product quality, customer service, and delivery.
These enriching techniques can help you to uncover insights that were not visible before and make better decisions based on your analysis.
Data transformation is converting your raw data into a format that is suitable for analysis. The goal of data transformation is to make data more organized, consistent, and manageable to be easily analyzed and interpreted. Here are some techniques for data transformation:
Data filtering involves removing unnecessary data from your dataset to focus on the relevant information. Filtering can include removing outliers, duplicates, or incomplete records. For example, if you have a dataset of client reviews, you can filter out irrelevant or duplicate records to focus on the most important reviews.
Data aggregation involves summarizing your data by grouping it into categories or applying mathematical functions. Aggregation helps to simplify complex datasets and make them easier to analyze. For example, if you have a dataset of daily sales transactions, you can average the data into monthly or quarterly sales figures to identify trends and patterns.
Data normalization involves standardizing your data into a consistent format to make it easier to analyze. This can include converting units of measurement or scaling data to a common range. For instance, if you have data on client ratings on a scale of 0 to 5, you can normalize this data by converting the ratings to a scale of 0 to 1.
Data transformation with machine learning involves using algorithms to transform your data automatically. This can include techniques such as dimensionality reduction, feature selection, or feature engineering. Let’s say you have a dataset of client sales transactions; you can use machine learning algorithms to identify the most important variables or reasons that influence their behavior. Thus transforming your data using these techniques, you can convert your raw data into a format suitable for analysis which can help you extract insights and improve the performance of your machine learning models.
Data transformation focuses on improving the quality and usability of data, while data enrichment focuses on expanding the breadth and depth of data. Both processes are important in preparing data for analysis and can complement each other to achieve more accurate and meaningful insights.
As data continue to grow, so are tools and technologies for data transformation, ranging from simple spreadsheets to advanced programming languages and platforms. Let us take a glance through some common tools and technologies for data transformation:
Spreadsheets such as Microsoft Excel or Google Sheets are the simplest and most commonly used for data transformation tasks such as filtering, sorting, and aggregating data. These tools provide a user-friendly interface for manipulating data and can handle smaller datasets.
Data integration software such as Talend or Informatica can merge data from different sources, transform the data, and load it into a target system. These tools are often used for complex data integration tasks involving large data volumes.
Programming languages such as Python or R are popular for data transformation because they provide many libraries and functions for manipulating data. These languages allow for more complex transformations and can be used to automate data transformation tasks.
ETL (Extract, Transform, Load) tools such as Apache NiFi, Airbyte, Hevodata or Microsoft SQL Server Integration Services can automate data transformation processes, especially for large and complex datasets. These tools provide a visual interface for designing and executing data transformation workflows.
Cloud-based platforms such as AWS Glue or Google Cloud Dataprep can be used for data transformation in a distributed and scalable environment. These platforms provide built-in tools for data cleaning, normalization, enrichment, and integrations with other data processing and analytics tools.
These tools and technologies can automate and pave your data transformation processes, reduce manual errors, and improve the efficiency and accuracy of your data analysis. The choice as we saw, depends on the size and complexity of your data, as well as your particular needs and budget.
Data Transformation can change the way you look at data and it is essential for anyone who works with data, Be it a data analyst, a data scientist, or a researcher. It allows you to turn raw data into valuable data assets, make informed decisions, and gain a competitive edge in your industry.
Therefore, next time rather than asking, is your data reliable? Ask, Is your data transformed? And you will get your answer. Once your data is transformed, its time to deploy data observability - Signup for 30 days free trial with Decube
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