Loqate Blog

The Hidden Returns On Your Data Quality Investment

Posted on Tuesday September 4, 2012 2:01 am by Graham Rhind

Though improvements in data quality will always benefit an organisation in one way or another, the culture and structures of most businesses fail to recognise this, or to treat data quality as an integral part of the business. Employees in many organisations still spend time and effort trying to persuade executives of the importance of data quality in their efforts to do their jobs; and there is often a structural disconnect between the team members who are faced with data quality issues every day, such as call-centre workers, and those in positions to make changes and improvements. Those who make business decisions based on data need to understand the impact poor data quality will have on their financial bottom line.

Increasing revenue

Data quality is not the same as a data quality project. Data and its quality needs to be in the DNA of any organisation. One off projects may be ill-conceived, overly complex and tackle the results but not the causes of data quality issues, so that the status quo of poor data quality is rapidly reached again after a project’s conclusion.

Traditionally, data quality improvements provide ROI and impact an organisation by increasing revenue (through business growth) and reducing risk by allowing compliance to laws and regulations and allowing business decision to be made on the sturdy foundations of accurate data.

The negative effects of poor data quality

The negative effects of poor data quality become apparent downstream from data capture and low quality data is injected into systems throughout an organisation. By the same token, good data quality results in improvements throughout a company. A rapid addressing system which reduces the number of keystrokes required to enter a customer’s address in the call centre has clear financial benefits at that point – customers are handled more quickly, fewer operators are required, efficiency increases. The ROI can be felt, though, also in other, less measurable ways. The data collected is better so that downstream business decisions have a better foundation. Items sent to the customer are returned less often, requiring less administration. Customers are able to contact the call centre more quickly, have their queries handled more promptly, and receive ordered goods on time. These ripple effects of data quality improvement cannot be underestimated.

Prevention is better than cure

Data quality improvements are often achieved with a low initial financial outlay. The structure of most organisations is geared towards resolution rather than prevention. There has to be a fire to fight before money is laid on the table. This is evident in the data world as most companies are happy to lay out large amounts to cleanse data within their systems which is proving unfit for purpose. This results in less than optimal data quality, as cleansing after collection can never be as effective as cleansing during collection. For lower outlays the companies can prevent the data pollution at source, such as in their web forms and data entry systems, and whilst the ROI of these actions are less easy to measure (as there is often no before and after scenario from which to take measurements), the beneficial effect on the company is clear. Duplication rates are lower, marketing and sales are more effective, business can make decisions on the basis of better data, and so on.

Many of the returns of data quality improvement are hidden, but their effects on the health of any organisation are great. Data quality should be built into their very fabric of every company, and woven into their structure and processes, emphasizing prevention over fire-fighting. No data quality improvement is ever wasted. Organisations which work with incorrect and poor data quality will, ultimately, fail.

From Graham Rhind - GRC Data Intelligence

Graham Rhind

About Graham Rhind

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