Data Management is an automated process of extracting semi-structured data from various source systems, verifying quality, standardization, and adherence to updated specifications, cleansing, transforming, and mining content to fill gaps. The deliverables are transparent reports on data quality, changes applied, and a prepared data set, that can be used to load into newer information systems, or used for Analytics, Visualizations or Machine Learning. For any IT system deployment, data management is very crucial as it makes the data within the system accurate, available, sortable, searchable, and greatly more usable. In Rihal, Data Management process is seamlessly managed and implemented using our in-house tool “Rihal Data Engine (RDE)”. RDE has built-in automation capabilities where it can perform various Data Management activities such as Data Scraping, Data Transformation, and Data Verification with just a click of a button making any migration project effortless, and fully transparent.
What We Do
Data Management is run by a skilled team of data engineers that design and run solutions for data issues causing pain to any business. Data engineers are also qualified in handling complex Migrations, Reporting, and Visualization tools to cover niche business needs.
Data Scraping and Classification
Data scraping is a process of extracting data from various unstructured sources including MS Office files, pdfs (including scanned pdfs), images, AutoCAD drawings, handwritten scans and more. Scraping data can aid in scenarios including extracting all tag numbers from engineering drawings, extracting employee numbers from HR documents, and extracting revision and expiry dates from controlled documents. Data Classification uses a combination of scraped content and machine learning models to categorize, catalogue, group, and rename files. This is valuable when attempting to organize a large library of legacy files.
Data verification is a configurable automated process of checking the data for any anomalies and non-adherences to standards set by the business. It automatically flags and groups clusters of issues with data sets and presents them in a transparent way to aid in resolving them. Data verification also provides automatic suggestions to correct the data.
Data transformation is the process of changing the data format, structure, or values from its raw form to form that is compatible with the target system. Transformation of data is the crucial intermediate step of any ETL process (Extract, Transform and Load). Rihal Rule Builder, a component of Rihal Data Engine, is used to build trackable transformation rules to ensure that only desirable changes to data are applied for each ETL cycle. A detailed report of all changes is generated in every ETL run to ensure that no undesirable changes were applied to the data.
Data mining is a feature of RDE that can help fill gaps in data by looking at values filled in cells on the same row of data, or in similar rows.
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