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The quality of data is determined by how well the information fits an intended use. For example, it really doesn’t matter how complete, accurate, or timely the data is if it isn’t presented at the proper level of granularity to provide the insight an executive needs to support decisions. Several aspects that impact realized data quality include:

  • The inherent data quality …characteristics such as accuracy, completeness, consistency, and freshness of the data.
  • Pragmatic data quality; or how well it suites a particular purpose. Characteristics include form, precision, level of aggregation, and availability.
  • The level of integration, such as multiple customer numbers for the same customer or multiple product IDs for the same product.
  • Inconsistent definitions of the people, organizations, locations, assets, and events across different systems and business units.

All of these issues can make it difficult to obtain a clear view of the business and reduce the confidence a knowledge worker has in the data. Ultimately, it doesn’t matter how much time, effort, and budget are expended on a new analytic platform if the users don’t trust the data.


SKML can help you understand your current issues with data quality, plan for and implement improvements, and implement a program to ensure continuous monitoring and improvement. The impetus for data quality improvement may be a Master Data Management (MDM) initiative, an ERP implementation, an enterprise data warehouse, regulatory compliance, or a host of other business drivers. The key is to build a sustainable program for data quality so that you aren’t repeating this process a few years from now. We will leverage our EIM Framework with templates and examples to provide guidance, accelerate the process, and align with one of the enterprise initiatives mentioned above; and other, interconnected aspects of EIM, such as Data Governance and Stewardship, Metadata Management, and Data Architecture.

SKML’s data quality service offerings include:

  • Data Quality Strategy and Discovery
  • Data Quality Technology Selection
  • Data Quality Assessment
  • Data Quality Improvement
    • Data Cleansing
    • Data Standardization
    • Data Matching (Householding), Survivorship, and Enrichment
  • Master Data Management strategy, preparation, and implementation
  • Data Quality planning and preparation for ERP implementation
  • Data Governance & Stewardship Consulting
  • Metadata Management support for Data Quality
  • EIM Program Definition and Alignment

 

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