Data wrangling, in essence, is about making data more accessible and meaningful for businesses. It’s a critical step in turning raw data into actionable insights that drive growth and efficiency. By understanding and effectively implementing these aspects of data wrangling, businesses, especially small to medium-sized ones, can unlock the full potential of their data assets.

Data Extraction

Data extraction is the initial step in the data wrangling process. It involves gathering raw data from various sources, which could be internal databases, customer feedback forms, online platforms, or any other relevant repositories. For a small business, this might mean pulling sales figures from accounting software, customer data from CRMs, or online reviews from social media. The goal here is to collect a diverse set of data, which is often unstructured and scattered, in preparation for further processing and analysis.

Data Cleansing

Once data is extracted, the next critical step is data cleansing. This involves identifying and correcting errors, inconsistencies, and inaccuracies in the data. For a typical business, this might include rectifying misspelled customer names, standardizing date formats, or removing duplicate entries. Data cleansing ensures the reliability and accuracy of data, which is vital for making informed business decisions. It’s like tidying up your workspace; you remove what’s unnecessary and organize what’s important for better productivity.

Data Transformation

Data transformation is the process of converting data into a format or structure that’s more suitable for analysis. It involves tasks like normalizing data scales, aggregating sales figures, or categorizing customer feedback. For instance, a retail store might transform its daily sales data to observe weekly or monthly trends. This step is crucial in turning raw data into actionable insights that can inform business strategies.

Data Integration

Data integration is about combining data from different sources to provide a unified view. For a small business, this might involve merging customer information from sales, marketing, and customer service departments to gain a comprehensive view of customer interactions and preferences. This holistic approach enables businesses to make more informed decisions, as it provides a complete picture rather than isolated data snippets.

ETL Processes

ETL stands for Extract, Transform, Load. It’s a process that combines the first three steps of data wrangling — extraction, transformation, and loading of the transformed data into a new database or data warehouse. For a business, ETL can automate the movement of data through different stages, making it ready for analysis and reporting. It’s like an assembly line in a factory, systematically preparing raw materials (data) into a finished product (useful insights).

Data Migration

Data migration involves moving data from one system to another, which might be necessary during system upgrades or when adopting new technologies. For example, a business might migrate data from an old customer relationship management system to a new one. This process requires careful planning and execution to ensure that data remains intact and usable in the new environment. It’s akin to relocating to a new office; the goal is to move everything important without losing or damaging anything.

Data Wrangling: Business case

Many business management software packages these days will have ready made integrations with data visualisation applications such as PowerBI. However for many legacy applications this may not be the case. In order to be able to draw from all your business data, even your older business data, the information contained within those legacy systems will need to be extracted, cleansed, transformed and then integrated with the new data. This means standardisation of naming conventions, addresses, phone numbers, transaction details, invoice information, dates and currency. In many cases data would need to be extracted and integrated from multiple sources to ensure validity and accuracy, for example using a google maps API to ensure that contact information is still current.

In some business cases, different business units may utilize different management applications, such that even if they can both be integrated with your visualisation package, the data would need to be standardized so that the most accurate insights can be generated.