The devil is in the detail global companies are

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: bal companies are taking drastic measures to deal with the downturn and remain profitable. However, these measures are often predicated on the assumption that decision-makers have access to a clean and comprehensive set of corporate data with a sufficiently granular level of detail. The devil is often in the detail. For example, many decisions taken to maximize revenues or profitability are based solely on financial information that does not necessarily reside in a single financial system. Companies should closely scrutinize decisions and pull together all the information about the products, customers, services, and geographies that are contributing most to the overall profit. However, many organizations struggle to bring together this information because it is strewn across separately managed systems with differing levels of control and quality. More data usually means silos and poorer quality Electronic data proliferation is economically neutral in that it grows exponentially in good times or bad. Databases have increased in number, size, and the richness of detail they contain. Attempts to tame this data growth in business applications such as ERP, CRM, supply chain management, and other enterprise business systems work on the assumption that data will remain in those siloed environments, but the reality is that companies often install multiple ERP and CRM packages for different units and divisions. Due to incompatible system design, moving data between environments is never a simple copy-and-paste task because each application has its own nuances and peculiarities regarding how disparate data is formatted, labeled, and stored. Common data quality issues, such as inconsistent data entry, duplication of records across database systems, incomplete and missing data elements, 6 S TRAIGHTTALK I T and confounded product hierarchies, also present obstacles to data consistency. No automatic wash cycle for data quality Treating data quality as something that can be solved by a piece of IT has also exacerbated the problem. Corporate data cannot simply be coll...
View Full Document

This document was uploaded on 12/31/2013.

Ask a homework question - tutors are online