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Data Cleaning to Produce Reliable and Error-Free Insights
Data Cleaning is a procedure that systematically refines Data to eliminate Inaccuracies, Duplicates, Noise, and Inconsistencies, and creates structured high-quality Data ready for Accurate Analysis, Decision Making and use.
Accurate and Reliable Insights from Enterprise (Data) Cleaning
Modern enterprises rely heavily on analytics and automation, so it is critical for these organizations to have clean, structured, and accurate data in order to generate accurate reports, insights that can be considered trustworthy, and support informed decisions across their organization.
These issues create challenges for organizations hoping to utilize data analytics. Organizations across all industries share these issues; duplicate records, inconsistent formatting, missing values, and obsolete information are among the most common.
All of these electronic roadblocks hurt the reliability of data, decrease the responsiveness of an organization, and deteriorate the quality of analytics and AI.
Statswork, through automation, deduplication, normalization and enrichment, refines raw data into a streamlined and cleansed form, destroying the duplicates, making sure that the normalization process has occurred, and ensuring that all missing values are populated.
As a result, Statswork’s Data Cleaning is designed to ensure an organization’s data is accurate, consistent, and prepared for analysis, enabling all organizations to operate at maximum efficiency and to make data-driven decisions with confidence.
Duplicate Detection & Removal
We offer automatic detection and removal of duplicates through our processes to help ensure consistent data quality and integrity.
Data Standardization & Formatting
We offer multi-source standardized and formatted data to allow analyzed data to be uniform, clean and ready for analysis.
Error Correction & Missing Value Processing
We provide intelligent methods for correcting incorrect entries and providing a systematic approach for handling missing values to achieve improved completeness of data.
Data Enrichment & Quality Enhancement
We provide techniques for enhancing and adding relevant, updated, and contextual data to enhance the overall value of the data.
Our industry
Statswork serves many different types of industries, including Finance, Healthcare, Retail, Market Research & Technology, by delivering high quality/accurate data that is ready for analysis.
- We have extensive industry insights: Our methodologies for removing dirty data items correspond to the way that businesses operate on an industry basis; therefore, we clean each industry’s data as they understand their data and operate with it.
- We utilize AI-enabled automation and rules-based cleaning methodologies: By leveraging automated and rule-based methods, we can find and remove erroneous, duplicated, or inconsistent records from your database with superior accuracy.
- Data is cleaned for the entire lifecycle: We perform a complete suite of activities for data cleaning that includes assessing the data and refining it to meet our clients’ final Data Enrichment and Validation standards.
- We ensure all cleaned records comply with regulations: Every record cleaned by our company has been done in accordance with any applicable regulations and following the Governance Policies of the client’s company.
- Most companies find it quick and easy to grow their business: Our data cleaning process has been designed to help us efficiently and quickly clean large, complex data sets.
- Better analytical insights lead to improved operational performance: Cleaned, organized, and credible data provides superior analytical insight reporting and the performance of AI applications.
1. Data Assessment
All data sources will be assessed for duplication, error avoidance, missing entries, and lack of formatting with information on where it needs to be cleaned.
2. Data Cleaning and Format Standardization
Automated rules will be established to fix any errors, remove any duplicate records, fill in any gaps left from none being filled in, and ensure every record has been placed in a consistent format for easy comparison.
3. Data Enrichment and Enhancement
Minerals and mines will be enriched and enhanced by being given current and relevant existing minerals and mines added to them to help increase completeness, worthiness, and usability of these minerals and mines for data analysis.
4. Validation and Quality Control
In addition to our data cleaning, we will conduct the final data quality control process on each dataset to validate that the data cleaned is correct and is compliant with established governance standards, quality in terms of compliance, and quality about meeting the analysis needs for all users.
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