Data Processing
“Spotless Data, Spot-On Solutions.”
At Varaisys, we understand the critical role of data cleaning and processing in achieving data-driven success. Our iterative methodology ensures meticulous refinement and enhancement of client data. By establishing a foundation of high-quality, clean data, we empower machine learning and data science initiatives to flourish.
Varaisys leverages both its proprietary system and industry algorithms and models to automate data cleaning, transformations, validation, and data transfer. Our methodical approach guarantees that every task is meticulously monitored until completion, ensuring consistent success.
Data Cleansing
At Varaisys, we specialize in data cleansing, which involves identifying and rectifying corrupt or inaccurate records within a dataset. Our process includes detecting incomplete, incorrect, or irrelevant data and then replacing, modifying, or removing the problematic entries. Data cleansing is crucial for maintaining high-quality data, as it ensures consistency and reliability in decision-making processes across various domains, from government policy analysis to business operations.
Data Mining
At Varaisys, we specialize in data mining using our home grown BI tool that extracts valuable insights from vast datasets. Our data mining expertise transforms raw data into actionable insights. We help you make informed decisions, reduce risks, retain customers, and boost sales.
Our Methodology
- Remove Irrelevant Data:
Begin by identifying and eliminating any data that is irrelevant to your analysis. Consider what specific analyses you’ll be running and what downstream needs you have. Removing irrelevant data ensures that you’re working with a focused dataset.
- Deduplicate Your Data:
Duplicate records can skew results and lead to incorrect conclusions. Identify and remove duplicate entries from your dataset. This step ensures that each observation is unique and contributes meaningfully to your analysis.
- Fix Structural Errors:
Structural errors include inconsistencies in data format, naming conventions, or units. Standardize these elements to ensure uniformity. For example, correct misspelled column names or unify date formats.
- Deal with Missing Data:
Missing data can impact the quality of your analysis. Decide how to handle missing values: either impute them (replace with estimated values) or remove rows with missing data. Techniques include mean imputation, forward/backward filling, or using machine learning models to predict missing values.
- Filter Out Data Outliers:
Outliers are extreme values that can distort statistical measures. Detect and handle outliers appropriately. You can use techniques like the IQR (Interquartile Range) method or Z-score to identify and address outliers.
- Validate Your Data:
Finally, validate the cleaned dataset to ensure its integrity. Cross-check data against external sources, perform sanity checks, and verify that it aligns with your expectations. Validated data is essential for accurate modelling and reliable insights.
