Organizations worldwide often struggle with a data paradox. They often feel ill-informed about their customers, prospects, inventory, suppliers and products. They are pressed every day to react to changing market conditions, a more mobile customer base and an increasingly dynamic trading network. Some global companies are even unable to determine exactly how many employees they have.

However, even as companies struggle with an incomplete view of business-critical functions, companies are receiving more and more information via every conceivable venue. A 2008 IDC study found that by 2011, there will be 1,800 exabytes of electronic data in the world – about 1.8 zettabytes or 1.8 billion gigabytes.1 Even as this avalanche of data reaches the enterprise, the reality for most of these organizations is simple: “more is less.” More data is arriving at an organization, but they are less prepared to turn it into meaningful information.

Over the past 15-20 years, companies have attempted to manage this deluge of data and create a more efficient culture by implementing enterprise applications to manage different aspects of the business. The two most common – customer relationship management (CRM) and enterprise resource planning (ERP) – are increasingly relied on to manage customer data and supply chain data, respectively.

In a vacuum, these applications will work “as advertised” by helping maintain and control a segment of the businesses. Where CRM, ERP and other enterprise systems fail, however, is that companies do not operate in a pristine, unaffected environment. They install multiple CRM or ERP packages for different business units and divisions.

Organizations acquire new organizations – and all of the legacy data from these new business units. Data entry is sometimes hurried, and there is little, if any, training for employees charged with collecting information. The result is a chaotic, disparate and disjointed view of the enterprise. The answer for most organizations is to consolidate, migrate and modernize, expecting that a more coherent view of the enterprise will emerge if the data is centralized onto few applications. But while moving to a smaller number of applications will have incredible benefits, it also poses incredible risks. Companies who have already done major data migration or consolidation work often struggle with:

• Incompatible system designs and technologies – Moving data is never as simple as a copy-paste endeavor. Each system has peculiarities on how data is stored, how it is labeled, etc.
• Limited knowledge of what data exists, where it came from, and what it represents – Companies may have data that predates anyone in the organization. Without a cursory knowledge of what’s in the old systems, how can you make intelligent decisions on what to migrate and what to archive.
• Lack of standards on what constitutes “good” or “valuable” data – When modernizing a system, do you bring all data to the new system or just that which is most current? Is there a standard method for evaluating data characteristics like timeliness, consistency or validity? Without a business framework to govern these decisions, good data could get left out – or inconsequential data could populate the new systems.

All of these issues have the same root cause: inconsistent, unreliable and inaccurate data in the source systems that is capable of polluting the new application. This white paper will examine how data management technology can solve these issues through a three phase process:

• Analyze – Use data discovery and data profiling techniques to identify problematic data, verify data structures, evaluate data complexity and uncover any data characteristics that can affect the data migration process.
• Improve – Use data quality and data integration technology to match, merge and standardize data from across sources into a single, unified data structure within the target application.
• Control – Monitor data on an ongoing basis to understand when new data arrives that violates business rules or does not meet standards for data quality.