Most companies are unaware they have a data problem, do not understand the consequences or simply do not know how to resolve it.
Data is becoming increasingly complex; gone are the days of the early '80s when the main source of customer data was shipping and billing addresses. Companies today collect and retain a mountain of data, including all customer, financial, partner, vendor, supplier, employee, product, warehouse and other data.
More often than not, this is fed into the marketing machine to drive sales, bring new customers aboard and retain existing customers. Poor data means poor results in this effort. But regulation is another current and impending issue companies face. Basel II and Sarbanes-Oxley, facets of which appear set to be replicated in South Africa, motivate businesses to reassess their approach to data quality.
According to Gartner, three quarters of financial services companies still make decisions based on poor data. The financial services sector is not alone in this regard. But some businesses are apparently unaware of the problem, while others are incapable of addressing it. The Data Warehouse Institute, for instance, estimates that companies lose more than $600 billion every year due to poor data.
PricewaterhouseCoopers in a recent research project found that of 600 US, UK and Australian executives, more than three quarters said they had suffered significant problems, costs or losses due to poor quality data.
Besides its many other impacts on the business, flawed data corrupts and undermines almost every IT project in the business. Gartner says that more than 25% of critical data used in large corporations is flawed. "Most enterprises do not fathom the magnitude of the impact that data quality problems can have," says Ted Friedman, principal analyst for Gartner. "These problems cause wasted labour and lost productivity that directly affect profitability."
Nobody disputes any longer that corporate data is a key strategic asset and the goal of data quality management is to provide the foundation for creating information, knowledge, and through applying insight to achieve wisdom.
The benefits of this good quality data are:
* Cost savings.
* Improved chance of marketing success.
* New opportunities to cross- and up-sell.
* Improved forecasting accuracy.
* Regulatory compliance.
* Improved decision-making.
* Better customer service.
* Smoother operations.
* Slicker supply chain management.
But try building a customer relationship management (CRM) or BI application on dirty data and the expected return on investment (ROI) vanishes faster than Windows NT support. The majority of large organisations continue to reach for ineffective technology solutions, even after they identify data quality problems. Gartner says these ineffective solutions often include priority spending programmes for advanced BI and CRM capabilities.
New business applications can only deliver the promised benefits if the data they rely on is of good quality. Ensuring good quality data is not a once-off project, but an iterative company-wide programme aimed at delivering accurate, consistent data.
Executives must remember that data quality is a business issue; according to Gartner, IT-driven data quality projects often fail. IT should select the tools to facilitate addressing poor quality data from source to business division, ensuring these fit into existing IT architectures.
Many businesses believe that technology will solve their data quality problems, but throwing technology at the problem will not yield long-term positive results. Gartner suggests that companies should examine their approaches and methodologies to improve data quality and engage the data users and business employees, not simply the technical staff. The greatest success in achieving good quality data comes from engaging both business users and IT.
Paul van Aswegen, GM of Informatica South Africa
For more information contact Paul van Aswegen, Informatica South Africa, 011 462 9676, firstname.lastname@example.org