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Enterprise data quality tools: Key factors to seek during evaluation

The ability to conduct analysis, make decisions, and craft strategies depends on having the right information at the appropriate time. In order to perform analysis and make decisions, maintaining data quality is imperative. Accessing and extracting information is easier when standardized data quality processes are clearly defined. Comprehensive data management capabilities, together with enterprise data quality tools, can help organizations to meet these goals.

While many enterprise data quality tools are currently available, proving their return on investment can be a challenge. However, once there’s buy-in for establishing a data quality framework, it’s critical to assess the impact of a possible deployment. The question to ask at this point is: ‘What will be the impact of this technology implementation and how will it help my business to grow?’ Once the business value is determined, the focus should shift to evaluating various enterprise data quality tools in the market.

The key dimensions to address in a data management strategy are accuracy, integrity, consistency, completeness, validity, timeliness, accessibility, cleanliness, relevancy and profiling. For example, let us take the scenario of a manufacturing organization which has grown significantly through acquisitions and has to address these dimensions. As a result of this growth, there are issues with systems, data reliability, data complexities, product integration, and synchronization of various formats along with policies across transactional systems. These dimensions should be addressed through data management capabilities with help from enterprise data quality tools. The capabilities that should be present in the enterprise data quality tool are as follows.

Data governance: Maintaining clean and consistent data includes defining measurable quality, quality management, and the overall confidence of the organization in the information provided to business users. Governance in this environment should include content definition, reference data, calculation engine, databases, and reporting tools and technologies. Individual business units typically implement their own solutions. By contrast, a common enterprise data quality solution creates a uniform platform and a source for reporting across groups.

Data architecture: It’s important to engage users throughout the entire development cycle, as well as build trust in the architecture and its implementers. It’s also essential that they participate in developing reports which are categorized to be low in complexity. Organizations should also adopt an approach which permits a relationship manager to establish the link between information management and the business entity to validate that the concerned members are aligned.

Some key features and functions to look for in the enterprise data quality tool to this end are:

• Support for the framework selected by the organization.

• The ability to represent models in a manner that allows non-technology stakeholders to relate.

• Support for meta-models.

• Requirements traceability.

• Support for enterprise use. For example, keep in mind features like multi-user collaboration support.

Data retention and archiving: Business analytic solutions are seldom point solutions. So specific archival policies need to be developed (independent of ERP systems) to support regulatory requirements and business requests to perform trend analysis.

Data quality management: The key elements to look out from enterprise data quality tools on this front are:

• Defining profiling rules

• Evaluating profiling routines

• Defining cleansing rules

• Auditing cleansed rules

• Incorporating data standards specific to the organization

• Correcting source systems by maintaining these rules through the lifecycle of your data management process.

Master data management (MDM): Complexities arise due to several instances of ERP implementations across the organization, custom solutions, and functional groups maintaining dimensional data separately and referring to the same data element differently. Functional groups should agree about key data assets for an organization to improve overall effectiveness in the decision-making process. MDM systems must maintain data hierarchies, versioning, auditing, single copy, multiple copies and continuous merge. The selected enterprise data quality management tool must be able to accommodate these requirements.

Metadata management: It’s essential that enterprise data quality tools address metadata management from business and technical perspectives. Business metadata allows the end user to interpret reported data elements in business terms. Technical metadata should attempt to provide data lineage end-to-end, assist the support staff with impact analysis, implement enhancements and change requests. 

The key features that should be supported by enterprise data quality tools are the abilities to:

• Extract data from different layers (extract and reporting).

• Specify mappings and reports that are impacted as a result of change in database structures.

• Provide standard reports that will assist the data architect in maintaining complex data warehouse systems.

These capabilities should be leveraged by enterprise data quality tools in a holistic manner to achieve desired data management outcomes. Note that the capital cost of acquiring and supporting enterprise data quality solutions could be significant; therefore organizations should validate the use of these tools and consider a licensing model which focuses on usage [such as a software‑as-a-service (SaaS) solution]. In order for organizations to meet their data management goals, they must determine their business priorities and then marry data management capabilities with enterprise data quality tools in order to be most effective.

About the author: Chandu Mukkavalli is the Director at Deloitte Consulting LLP. He has more than 13 years of IT experience with a primary focus on information management, BI and data warehousing. He has developed and led the BI/data warehousing practice at large consulting organizations.

Disclaimer: This presentation contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this presentation, rendering business, financial, investment or other professional advice or services. As used in this document, ‘Deloitte’ means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries.

 

This was first published in October 2010

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