Organizations continue to be challenged by data warehousing and integration implementations.  Given the need to merge single source applications and garner the benefits of a “single” view of the truth, data warehouses serve to consolidate and streamline information flows that impact multiple constituents and offer data integration among disparate sources of data.  Some organizations have turned to data marts as a prescribed way to reduce costs and challenges associated with larger scale data warehousing implementations.

Data marts are said to be a subject-oriented and organized to enable decision support for a given business unit or department.  With each department (e.g., accounting, sales/marketing, production) creating, maintaining and potentially owning its own data mart, continual islands of isolation can proliferate.  Such proliferation can result in myriad differences in overall organizational objectives as well architectures and designs.

Withstanding these issues, a unified truth is desirable, and this objective rests on the degree to which data quality is integrated into organizational techniques, processes and practices.  Data quality requires organizational buy-in, data management practices and continual monitoring which is most effective and efficient when utilized prior to the extract, transfer and load process into the data warehouse.  Data detection, completion, auditing, enhancement and standardization are but a few of the processes used by leading data quality vendors.

Data quality coupled with data integration enables organizations to engage in more informed decision-making which impacts organizational key performance indicators (KPIs).  Rigorous organizational practices would include data quality assessments to capture best practices, areas that warrant process reengineering and where continuous improvement can be executed.

Organizations, such as nonprofits, significantly reply on members and volunteers for both revenue flows and service delivery.  Financial quandaries can stem from employees spending hours working with member databases that contain inaccurate, incomplete and duplicate member data.  Often users export data to user-friendly spreadsheet applications which is a typical practice not limited to nonprofit firms.  The extracted data, however, requires modification and formatting to create needed reports for board of directors and executive directors.

The final reports may not be accurate or complete, and the decisions made from these reports can be right decisions (in terms of alignment with organizational mission) based on the erroneous data.  These decisions, in turn, can impact membership services, target marketing programs and financial outcomes of any organization.

Ultimately, an initial assessment of data quality practices and processes is critical prior to technology adoption.  The assessment should gather the views of multiple constituents to foster users and management buy-in and align with organizational strategy and mission.  Such an assessment can drive centricity of data and stimulate user trust of the organizational data.