In an environment that is increasingly based on information, companies need to understand that the value of their business relies on the successful management of their data. Data quality problems hamper virtually every area of an organisation, from the call centre to the executive office. Every hour spent searching for missing data, correcting inaccurate information, working around data problems and resolving data-related customer complaints is an hour of direct cost to the bottom line.

Some examples of poor data causes are:

• Poor or non-existent data capture validation rules and systems,

• Poorly-trained call centre & data capture operators,

• High incidence of illiteracy given multiple, diverse languages of customers,

• Customers input errors into front-office systems,

• Prospect and Third-party data contains errors, and

• Data from diverse systems conforms to disparate formats.

Research has shown repeatedly that failure to examine the content, structure and quality of data is one of the root causes why IT projects often cost more than two-thirds of their original budgets, or even collapse completely. Huge emphasis is placed on systems integration and infrastructure, while inadequate attention is paid to the lifeblood of any IT system: the actual data fed into it and, more importantly, the quality of that data. The old maxim “garbage in, garbage out” has never been more applicable.

One of the challenges associated with data quality improvement is not knowing the extent to which bad data affects the organisation. Companies need to examine the size of their data quality problem, determine which part of the problem is most business-critical, and then determine the initial steps that need to be taken to address the problem. A data quality return on investment assessment provides a set of metrics to highlight the more critical data quality issues, and tie those issues to actual business problems, which can either be related to increased costs or to lost opportunities.

Customer data quality is the foundation of the organisation’s Customer Relationship Management (CRM) strategy. The concept of CRM is simple and logical – put in place appropriate means to make the customer’s experience with the company a more personal and intimate one. Poor data quality compromises the organisation’s entire CRM investment and undermines the company’s relationships with its customers. Reliable data quality is the foundation of every CRM initiative. The importance of assessing data quality on an ongoing basis cannot be overemphasised.

Organisations that invest time and resources into creating an effective data strategy are positioning themselves to secure maximum value for their business, while encouraging customer satisfaction and loyalty.

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