“Sacrificing data quality undermines marketing and business success”. January 2013
Article published in “Nex Media” Vol 1 Issue 1 - submitted by Daryl Joubert C.E.O. Intimate Data (Pty) Ltd
If the state of a company’s products, services and marketing matched the level of quality of the data in the company’s databases, would the company survive or go out of business?
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. The old maxim “garbage in, garbage out” still holds true for data quality
“Data quality is a critical yet neglected strategic business issue,” says Darryl Joubert, chairman of SA based specialist data quality software supplier and Direct Marketing service bureau Intimate Data. “Data quality problems hamper virtually every area of an organisation, from the mailroom to the executive office, and have a direct affect on the bottom line. 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.”
Research has shown that failure to examine the content, structure and quality of data is one of the root causes why IT projects and procedures 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.
In many companies, there is a lack of appreciation of the impact that poor data quality can inflict on the business – even at boardroom level. Research conducted by The Data Warehouse Institute shows that poor quality customer data costs US businesses a staggering $611 billion each year in postage, printing and staff overheads.
Data quality problems have always persisted in databases. Management tends to accept the cost of the status quo and the current level of low-quality data. But, says Joubert, this is simply because it knows no alternative. Most organisations consider the level of poor data to be normal, and a high level of redundancy is accepted as a cost of business. “Few companies take the issue of data quality seriously enough. Those that do, tend not to do so at a sufficiently senior level,” he says.
An international data management survey conducted by PriceWaterhouseCoopers found that 65% of traditional companies and 50% of all e-businesses only discuss data management at board level occasionally, if at all. Responsibility for data often lacks the senior management focus and control required to generate sufficient budget and priority.
One of the most frustrating issues associated with data quality improvement is not knowing how bad data really affects the organisation. Companies need to examine the size of their data quality problem, determine which angle 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 (ROI) 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 with lost opportunities. Joubert believes this model can be used to address both obstacles.
The costs of poor data quality can be divided into soft costs, which are clearly evident but difficult to measure, and hard impacts, whose effects can be estimated and measured.
Hard costs are those whose effects can be estimated and/or measured. These include:
- Customer attrition / Error detection / Error rework / Error prevention / Customer service / Fixing / Customer problems / Delays in processing / Delayed or cancelled projects / Lost interest from late payments / Lost revenue from lost payments / Wasted printing costs / Wasted envelopes / Wasted postage costs / Wasted man power / Incorrect call centre calls / etc
Soft costs are those that are evident, clearly have an effect on productivity, yet are difficult to measure. These include:
- Difficulty in decision making / Time delays in operation / Organizational mistrust / Lowered ability to effectively compete / Lost sales / Poor company image / Decrease in productivity / Data ownership conflicts / Lowered employee satisfaction
There is no doubt that the value associated with improved data quality can add to the company’s bottom line, either through optimisation in operational systems or by improving the value of knowledge generated through a business intelligence process.
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 and ensure the customer feels like a king.
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. Data quality problems have a direct affect on the bottom line.
Some examples of poor data causes are:
• Poor or non-existent data capture validation rules and systems,
• Online customers intentionally enter incorrect data,
• Call centre & Data capture operators enter abbreviated data to save time,
• Prospect and Third-party data contains errors,
• Customers input errors into front-office systems,
• Data from diverse systems conforms to disparate formats.
“Organisations that maintain the quality and integrity of their data will protect their business success and bottom line, while encouraging customer satisfaction and loyalty,” says Joubert. “Companies that invest time and resources into creating an effective data strategy are positioning themselves to secure maximum value for their business. At the same time they are building a solid foundation for any future business changes required to keep the business ahead of the game. An organisations poor quality data should not be their downfall and can be beaten.”
Darryl Joubert, Intimate Data, (021) 701-5152, 082 828 3017, email@example.com