Gartner Research estimates that through 2005, “more than 50 percent of business intelligence and customer relationship management deployments will suffer limited acceptance, if not outright failure, due to lack of attention to data quality issues.”

Inaccurate, missing, duplicate, extraneous and inconsistent data hinders a company’s ability to understand its clients and business processes. This further leads to poor decisions, coupled with host of negative results – customer dissatisfaction, lost profits, inaccurate business forecasts, operational delays and so on.

Most companies have invested substantial sums in their relational database management systems (RDBMS) and corresponding business applications, each with a different focus, constituency and acronym: ERP, CRM, SCM. Yet, over time, these applications and their related stores of data may become unwieldy and riddled with errors. Bad or missing data with an enterprise creates lost opportunities, which can lead to lost opportunities to sell more and earn profits from their customer base.

In our experience, data quality is an achievable goal. We have found that an RDBMS is incomplete without a comprehensive Data Quality Management System (DQMS). A typical situation of ‘Garbage In, Garbage Out’ (GIGO) is created and that proves costly. With DQMS, your company can ensure that lack of good data and quality information often does not lead to lost opportunities.

How To Solve Information Quality Problems ?

ebusinessware can improve a company’s reference data by applying its state-of-the-art technology and business process excellence to your data feeds. Our end-to-end Workflow flows from importing / normalizing of client data and includes de-duplicating, cleansing, exception management and on-going testing. Our DQMS is the key to any organization’s quest for achieving Informational quality. As illustrated in the diagram below, our DQM System is flexible enough to except and import any non-proprietary information.

The imported data is stored in staging databases for extensive data grilling. With available tools and services, staging data goes through various stages of data cleansing process. Matched with series of algorithms, Matching process recommends a sequence of normalization, standardization, transformation and translations. This transformed, cleansed, integrated, enriched and high quality data is then sent back to the clients in customizable formats.

Our reference data professionals can help your teams to design a reference data management approach that is consistent and synchronized with industry’s best practices and that will work for your organization. We can assist in design, project management and ongoing support for your reference data management projects. ebusinessware’s reference data has been set-up to provide Reference Data Management (RDM) and Data Quality Management Systems (DQMS) as a turnkey solution or via a managed service model. To ensure that you have high quality information that can drive good and solid business processes, we offer a perfect blend of Data quality improvement and enhancement tools and services. (Refer figure below)

With the given set of tools and services, we develop a customized process for sourcing, cleansing, enrichment, sampling, testing and reconciliation of your reference data within your organization.

Conclusion

Corporate data is a key strategic asset, so ensuring its quality is imperative. However, due to the tremendous amount of data being gathered and given the variety of sources, data quality is often compromised. It is a common problem that many organizations are reluctant to admit and address. Spending money, time and resources to collect massive volumes of data without ensuring its quality is futile and only leads to disappointment.

With initiatives including total client profitability and quality of information becoming one of the premier issues with organizations today, at ebusinessware, we recognize, determine and share the severity of such quality issues with you. We work closely with your teams to succeed in getting total resolution.