Data cleansing and data preparation are not the same. When you are cleaning data, you are removing inaccuracies, invalidities, and junk from it. But when you perform data preparation, you are furnishing it to be used for an intended purpose. Spending time with data preparation gives you confidence in your data, the business intelligence process, and the validity of the insights derived from it.
Data preparation activities
Data cleaning is one of the activities involved in data preparation. Several other activities are included in data preparation, but usually only those are performed that are relevant to the purpose of the analysis process. Following are some common activities involved during data preparation:
Data integration involves loading data from multiple disparate sources such as local excel files, relational database servers, data stores at third party applications, and so on. It is important to have all these datasets together at one place so it can be analyzed for subsequent steps. Custom queries are usually formatted to import and integrate the required attributes of the datasets only. This helps in keeping the analysis process focused on the data that adds value to the resulting insights, and eliminate any noise that may be present in the datasets collected.
Data profiling allows you to identify potential problems with the current datasets. What are the issues that are creating roadblocks in your data quality, and hence must be fixed before moving on to insights extraction? Profiling your data will show you a complete picture of your dataset in terms of missing, misspelled, invalid, and duplicated values that your records contain. This will give a deeper view of your data values and highlight potential cleansing opportunities.
This is one of the most time-intensive activities involved in data preparation. Data cleaning includes tasks that ensure reliable data quality, such as identifying missing values and specifying accurate ones, removing garbage and invalid data, checking data accuracy and relevancy, and ensuring that data is up to date. As the process involves multiple datasets, the same data cleaning rules must be applied to ensure consistency in data quality.
Apart from data integration and cleaning, an important part of the preparation process is data transformation. This is not about changing the data, but transforming it to a state that is more useful for the analysis process. It can involve changing data types and formats such as changing date from MM/DD/YYYY to DD/MM/YYYY. Other than this, it also includes performing mathematical calculations on corresponding column values to identify a new attribute for the record, or parsing one column to identify multiple attributes.
Data matching and deduplication
When integrated from multiple sources, data tends to contain multiple records for the same entity. This step involves matching records based on custom designed match definitions, and identifying the ones that belong to the same entity. Sometimes it is just as easy as matching on a unique identifier, but you may have to use advanced matching algorithms and techniques such as phonetic, numeric, domain-specific, and fuzzy matching. Once matched, duplicate records are eliminated to ensure bias-free analysis results.
Data merge and enrichment
The duplicate records can be removed, or you can also merge multiple records representing same entity into one. Once all datasets are cleaned, transformed and deduplicated, the resulting datasets can be merged to represent a single, golden record. This dataset becomes the input for your analysis process.
Feature engineering and extraction
Oftentimes feature engineering and extraction is treated as part of the data preparation process as well. In this step, analysts study the final dataset and choose the attributes that can play an integral part in optimizing the analysis process. Feature extraction usually happens by reducing the number of data attributes. When different characteristics in a dataset are merged into one, each chosen attribute serves as a main “feature” for the business intelligence logic used to derive insights.
Data preparation solutions
Although data preparation activities can take up a lot of time, it is crucial for data analysts to invest this time in the process. This gives them confidence in the data and ensures the resulting insights are reliable and accurate. However, analysts should not get preoccupied with the tools used to prepare the data. This means that whatever tool or technique is being used to clean, integrate or transform the data, it should keep the process intuitive and simplistic.
There are three approaches to data preparation solutions:
For this approach, you must have some level of programming expertise. Once you have designed the custom logic for your data integration, cleaning, transformation, and deduplication steps, you can implement it in Python, R, or any other programming language. During this approach, you code behind-the-scenes process, rather than directly manipulate the frontend data. Although, it gives you the flexibility of developing your own custom solution that can be repeatedly applied on different datasets, it has challenges in terms of code expertise and maintainability.
In this approach, data visualizing tools or spreadsheets are used to directly manipulate the data from front-end. Although this approach is not repeatable and is very specific to the data, it is very intuitive and all changes are reflected as they are being made.
In this approach, processes are configured intuitively to prepare the data as needed. All data preparation activities, such as changing data types, validating patterns, designing match definitions, purging duplicate records, and creating golden record, can be configured in the process design. The process can further be used to clean and transform other datasets, so it is repeatable. An important thing to note is that a process-based approach gives you a centralized control of all activities, from beginning to end.
How can a self-service, process-oriented data preparation tool help?
According to a recent survey conducted by Anaconda, data scientists are spending 45% of their time on data preparation tasks, including loading and cleaning data. The data preparation phase is considered tedious and time-consuming for data analysts, not because they shouldn’t be doing it, but because it is difficult to perform all these diverse activities at a central place. And so, these activities consume most of their time.
As organizations are demanding quicker and more reliable business insights, self-service data preparation tools can play an important role in this process. They can help in reducing the time taken from data collection to insight extraction. As these tasks are mostly delegated to the IT team in an organization, a self-service data preparation tool can allow analysts to exercise better control and perform exploratory analysis.
A process-oriented approach in a self-service data preparation tool offers a central place that allows integrating, standardizing, transforming, deduplicating, and merging data from multiple sources, while keeping an eye on the data as it is being manipulated. Such tools put the data preparation process on the pedestal. Without going into the depths of code, you can focus on building a repeatable, configurable process.
DataMatch Enterprise (DME) is one such data preparation tool that allows you to configure your data preparation process. Starting from importing data from various sources, it guides you through data profiling, cleansing, standardization, deduplication, merge and survivorship. On top of that, its Address Verification module helps you to clean addresses with a few clicks.
Once your data is cleaned, parsed, and standardized, DME then allows you to define your custom match definitions or rules, based on which record matching can take place. When done, you now have your golden record ready from where you can start your analysis process.
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