Perfect data. That is the term used when a system supplies information that is both fully accurate and lacks nothing essential for resolving a customer’s requirements. Anything less, either in accuracy or completeness, represents a flaw or failure to achieve the system’s intended purpose. Quality data maximizes an organization’s ability to make mission critical and operational business decisions.

Attempting to engineer data quality through out-dated, brute force approaches requires an order of magnitude and more resources than the same effort using modern data analysis technologies. While Data Blueprint uses such technologies in all its practice areas, experience has shown that the return on investment is greatest when applied to data quality engineering. We combine rigorous data analysis with automated tools to help customers improve the quality of their data. Our associates understand the proper application of these technologies to organizational data quality issues and can help to develop the business case for their deployment. 

Enterprise data is more useful when its quality is known and can be taken into account by relevant business users. We have created and proven several techniques for designing and implementing data quality methodologies that can identify, capture and cleanse data, and more importantly, prevent future data degradation.  Our data quality engineering practice areas have received independent validation and represent the “next generation” in these techniques. Data Blueprint offers the following data consulting and data engineering services in the area of data quality engineering:

DMPA® (Data Management Practices Assessment)

• A formal process developed by leading thinkers in data management, that allows an organization to gauge the maturity of their data management practices and to see how their practices compare against similar organizations. A DMPA® assessment also offers specific actionable steps for cost-effectively reaching and sustaining higher data quality standards – standards that have significant real-world payoffs. DMPA ® is derived from the well established SEI capability maturity model and integrates unique data gathered from hundreds of organizations.

Data Quality Engineering Effectiveness Assessment

• Virtually all organizations are investing in some sort of data quality engineering tasks.  The relevant question here is: How effectively are you managing data quality?  If your investmetns in data quality do not seem to be paying off, or your data quality investment plan is non-existent or more than three years old, or if you have not yet invested specifically in data quality - then you should consider our Data Quality Engineering Effectiveness Assessment.

Data Audit Solutions

• Discover the state of your data and its impact on the organization’s performance and profits

Data Cleansing Programs

• ​Fix data inconsistencies that are costing your organization time and money

Sustainable Error Prevention

• Insure the quality of your data assets

Text Extraction

• Unlock and retrieve inaccessible data assets