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The Data Quality Issue
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One of the biggest challenges in a data migration project is typically the poor quality of legacy data. Incomplete, inaccurate,
unstructured or duplicate data can derail an entire implementation.
Using tools and libraries of templates, our content specialists can handle the most difficult data cleansing projects as part
of the end-to-end migration. Our specialists can profile, standardize, correct, enrich, de-duplicate and classify various types
of enterprise data with great accuracy and consistency.
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Data Monitoring & Governance
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Data governance refers to the overall management of the availability, usability, integrity, and security of the data employed in
an enterprise. A sound data governance program includes a governance team, a defined set of procedures, and a platform to enforce
those procedures.
Our solutions and technical platform extend beyond data migration and data quality into data monitoring and governance by
checking and controlling the quality of new data creation across the enterprise, as well as providing templates for consistency
across locations and users.
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What is the Data Health Check Methodology?
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The Data Health Check is a process conducted to objectively assess the quality and characteristics of any data source. It is
the logical first step to establish overall health (the what and where) before any data transformation can take place. In
doing so we not only uncover what needs fixing today, but also establish processes to keep your data clean on an
ongoing basis.
- While it can be conducted any time, changes to systems, new implementations or corporate events such as M&A are typical trigger points
- Carried out on a representative sample of data to determine the quality and characteristics of an organization's data
- Conducted by Content Engineers who also have domain expertise in the related industry
- Requires comprehensive testing, analysis and comparison with standards
- Findings are delivered on easy to follow drill-down views
- Report concludes with recommendations on data transformation and potential process improvements
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