Data Quality : an ignored topic

Ever since there have been applications and databases, there have been Data Quality problems.But given the continued existence of Data Quality issues in each and every organization, it is clear that this is often seen as the icing on the cake.

How can companies ensure the trustworthiness of information (Quoted statistics, BI & DW projects, Financial reporting, etc.) if Data Quality is not a “way of life”? The value of data is clear but often hard to quantify precisely. Nobody can deny that inaccurate, inconsistent, incoherent data has a direct impact on the bottom line.

The annual cost of “dirty” data for U.S. businesses exceeds $600 billion. Almost as much as US Defense Spending Budget for 2013. Business is driven by velocity. Asking yourself if your data are trustable and accurate will simply have negative impact on your day to day business.

Data Quality is a fundamental prerequisite for the successful implementation of enterprise applications, such as CRM, SCM, BI/DW and ERP. It must be of sufficient quality to meet companies’ business needs. It is what we could call Phase 0 of any data driven project.

Poor data quality can lead to big problems. Organizations consolidating different sources of data without any data quality approach will generate a set of conflicting information and likely bring erroneous results leading to wrong decisions and other negative consequences.

So, fit-for-purpose data is essential for organizations to achieve maximum value from their business-critical applications and identify new opportunities, improve operational efficiency, and comply with industry, national or international compliance or regulatory issues.

Furthermore, digital transformation, M&A, existing and new regulations, greater use of information and Big Data pose new challenges. Data Quality being one of them.

This is why having Data Quality initiatives must be mandatory for any companies and data must be treated as a strategic corporate resource.

Don’t underestimate the size of these initiatives. It will (should) be a Top Down approach and will require executive sponsorship.

But let’s be clear, Data Quality is mostly a business issue, not an IT issue. Business units managers and other data stewards will own it and collaborate in addressing / improving enterprise Data Quality.

They will be able to understand what data quality means in the context of a particular LoB, to bring good data governance and Data Quality perspective into their respective business processes.

Data quality will be the means to a specific analysis or operational goal. For example, increasing customer experience, data mining, marketing campaign, data migration, reporting & dash boarding, replacing multiple ‘best of breed’ applications with single ERP or creating a “single view” from multiple sources, etc.

Data Quality should be a fascinating journey instead of being an ignored topic.