Whitepapers
An Introduction to Data Quality
What causes poor data quality, what effects can this have, and what can be done about it?
Data quality as part of a data management strategy. A company’s data management strategy needs to focus on answering three simple questions.
- How do I give employees within the organisation access to the data they need to do their day-to-day work?
- How do I assure the data they are working on is of the highest quality possible, to ensure it is fit for the purpose they want to use it for?
- How do I secure the data so that only my company benefits from it, and that it can’t be damaged in a malicious attack?
Most organisations have put a huge amount of effort into solving problems 1 and 3. Whether with an Access database, or an advanced CRM application sitting above an Oracle database, time, money and resource will have been spent ensuring that the data staff need to carry out their jobs is available for them to access. Similarly the IT or security team will have installed firewalls, virus protection and system login controls to ensure only the intended, trusted users can benefit from this information.
But how much effort goes into addressing the issues raised by question 2, and ensuring the data being used is of a good enough quality?
In almost all cases the answer is “simply not enough“.
There is a large discrepancy between how data quality is considered compared to the other two aspects of data management. It is inconceivable that an organisation would spend time and money creating a database full of high quality information, and then leave it unsecured for anyone to access. Similarly, no organisation would hide very high quality data away behind the most secure of defences, but then not let anyone in the company access it.
So why do many organisations ensure data is both secure and accessible to those who need to have access to it, without also ensuring it’s in a fit state to be used?
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Measuring Data Quality ROI
How do you calculate an ROI for a data quality solution?
Justifying a data quality initiative with tangible numbers can be a daunting exercise, but to sell an organisation on the benefits of an effective data quality strategy must include hard facts for the business to listen and responsibly initiate a solution. Senior management require a clear statement of costs savings and revenue impact for any investment. Supplementing ROI calculations with business scenarios that clearly show the impact of poor data enable the business to understand the real consequences.
Most business managers today accept that unreliable data has an impact on the business. With recent initiatives in enterprise applications such as Customer Relationship Management (CRM), expensive software is streamlining organisations to be responsive to customers and derive greater monetary value per customer relationship. These applications are all underpinned with data that represents real business information. Maximising the quality of this information increases the ROI of the application investment and can be a substantial part of the ROI. Having a slick application integrating departments to provide a business services such as customer support services with poor data can undermine the whole CRM initiative.
Often, data quality is a secondary thought. The task of deploying a major application outweighs any thoughts about incorporating a data quality strategy. Having the system up and running is the primary thought. Incorporating a clear data quality strategy at an early stage can have massive impact on user acceptance and the financials of the business. Sales staff, customer service agents and knowledge workers can effectively use the new system with confidence. Poor data can harm the reputation of the roll-out and dent the confidence of the business.
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