SA direct mailers losing millions through incorrect postal codes.
(Issued by: FHC Strategic Communications, in www.Biz-Community.co.za)
South African businesses - in particular direct mailers - are haemorrhaging vast sums of revenue on millions of pieces of incorrectly addressed mail, with up to 15% of business-related correspondence never reaching its intended destination.
The consequences of poor address quality is costly: millions of rand are wasted annually on undelivered mail and returns, resulting in delayed payments, re-mailing costs and poor cash flow.
"Poor address quality has a greater effect on companies than they realise," says Darryl Joubert, chairman of specialist data quality software supplier and service bureau Intimate Data. "These costs tend not only to be ignored but absorbed into the overall cost of doing business. And yet these are real costs and they add up. The bottom line is that data quality problems hurt the bottom line."
There are distinct bottom-line rewards to be had from attention paid to clean address data. For instance, a leading retail bank improved the quality of its customer addresses by one percent and saw the reduction of incorrectly addressed statements, brochures and other correspondence slice R1 million from its postage bill.
People are quick to blame the South African Post Office (SAPO) for the non-delivery of mail, says Joubert. But the issue is endemic, and it is not unique to South Africa. "It's a widespread, global issue, occurring in countries where postal or zip codes are in use." For instance, the US Postal Service reports that 26% of all addresses on US mail are incorrect.
Returned mail has a huge impact on the bottom line. A large company may send over a million pieces of direct mail annually. An average 15% undeliverable rate would mean 150 000 undelivered pieces of mail.
When industrial giant 3M identified poor data quality as its weak point in 2000, it implemented data cleaning software that improved the rate of valid addresses to around 90%, saving the company the equivalent of about R850 000 a year, plus a further R850 000 in reduced surcharges from shipping and courier companies. Apart from improved address accuracy, the company's data warehouse now delivers accurate customer, product, sales, inventory and financial data direct to the desktops of 3M workers and partners who can access the information via the Internet.
Locally, Daimler Chrysler found it had 800 000 customer names on its database; when it had applied the appropriate data cleansing disciplines to the software, it was able to reduce this number to 260 000, with major savings and improvements in business processes.
The implementation of effective, reliable address cleaning software delivers direct savings to the bottom line. SAPO offers substantial bulk postal rebates to companies qualifying for postal address management service supplier (PAMSS) certification, which not only means savings on postage but results in improved cash flow by ensuring mail items are correctly addressed to reach their destination.
Address data quality needs to become a way of life for business, identified at board level as a core focus requiring ongoing attention. Data is a business's most valuable asset, so setting data quality standards and achieving accurate data needs to become one of its highest priorities, adds Joubert. [03 Jul 2003 16:32].
“When it comes to achieving true business intelligence (BI).
(Article by Annemarie Cronje, ITWeb, 9 November 2004)
“When it comes to achieving true business intelligence (BI), the rather un-sexy truth of it all is that data, or the quality thereof, largely determines whether a company will be successful.
More and more South African companies are turning to business intelligence (BI) to address issues around legislation compliance, good governance or to cut costs. However, if BI solutions are to deliver vital and accurate information, they need quality, accurate data that is drawn not only from all the right sources, but also integrated to provide a clear picture of the business landscape.
Raw data in itself is meaningless but it forms the vital foundation for obtaining real business intelligence on which strategic business decisions can be based. The problem is that as long as there has been data, there have been errors. Bad quality raw data quite obviously will lead to distorted information.
To compound this, data is far more complex today than it has ever been. Back in the 1980s, there was much less information to be managed, with the main source of customer data probably being a delivery address and a billing address.
Nowadays, data pours in from numerous sources and consists of all kinds of information. In addition to keeping track of vendors, suppliers, inventory and financial records, companies are also tracking customer buying habits, preferences and much more.
This explosion of data and the myriad of different sources make BI very difficult to manage. This is because the quality of data that a company has will inevitably determine the reliability of the information its executives have at their disposal to lead with confidence.
The consequences of bad data quality can be catastrophic. Inaccurate or incorrect data can call into question the production of performance indicators, reduce the credibility of the information system and even lead to significant financial losses.
In fact, dirty data can damage every aspect of a business. On the customer-side, outdated information or an incorrect credit score could mean a failed marketing campaign or an angry customer. In the supply chain, poor product data can cause production bottlenecks and slow down delivery orders to retailers.
The benefits of having squeaky-clean data are endless. Clean data provides a solid information asset that can help companies identify and reduce inefficiencies, create tighter customer relationships and quickly respond to changing market conditions.
So what is dirty data and where does it originate?
Quite simply, bad data is information that is incomplete, erroneous, duplicated or obsolete - a misspelled name, an incomplete field or an out-of-date business address.
This bad data can come from a number of sources in a company's management processes and systems. For instance, there may be a lack of up-stream control on information that is captured into the management system; there could be a glitch in the transfer of data in the information collection process or delays in the updating of information.
Manual spring-cleaning of data has become almost impossible as data volumes increase. Fortunately, technology allows companies to effectively deal with their dirty data.
In selecting a data quality solution, companies need to look for sophisticated matching and standardisation capabilities that enable users to analyse, clean and standardise data across all platforms.
There is no doubt that companies should not be cutting any corners when it comes quality-checking processes. If they aren't keeping a check on quality - they are throwing money to the wind and probably won't achieve true BI, which is a company's most strategic weapon”.
The costs of inaccurate data can be enormous – and not only in monetary terms. The responsibility for squeaky clean data must therefore lie with everyone within the organisation, from the receptionist to the chairman.
For South African financial services companies, data quality is a particularly major issue. In light of increasing fraud threats and new regulatory requirements, banks are placing major focus on the quality of their information.
“We see information quality as a cross-Group process. Clean data is dependent on the contribution of all the role players,” says Cornie Victor, general manager of the Business Enablement department in ABSA’s Information Management (IM) Division.
Recognising that the consequences of bad data quality can be catastrophic, ABSA’s data quality initiatives, which are underpinned by SAS business intelligence technology, started in 1997.
Inaccurate or incorrect data can call into question the production of performance indicators, reduce the credibility of the information system and even lead to significant financial losses.
“In fact, dirty data can damage every aspect of a business,” says Annemarie Cronje, Solutions Architect at SAS Institute SA. “On the customer-side, outdated information or an incorrect credit score, could mean a failed marketing campaign or an angry customer. In the supply chain, poor product data can cause production bottlenecks and slow down delivery orders to retailers.”
Accurate, up-to-date information on customers not only improves customer service, but reduces the risk of an expensive marketing campaign failing – not to mention opening up new opportunities to cross and up-sell.
Victor says that if incorrect address information led to only two percent of ABSA’s annual customer mailings being returned, this would cost the bank close to R3million each year.
“One of the major benefits of excellent information quality is a higher response rate to marketing campaigns,” says Victor.
“Others include improved customer loyalty and service, improved shareholder value and opportunities for new product development.”
Victor believes that data quality is a journey: “Organisations need to create awareness of quality on an ongoing basis, measure constantly and benchmark against latest trends.”
Often maintenance systems don’t reap the benefits that they promise through no fault of their own. How can you expect a system to improve underlying data? The answer is that you can’t. What you need is to have good data in the system so that it can be accessed, processed and used to provide practical information for the organization.
Let me illustrate the cost of not having good data with an example. A multi-site manufacturer has four locations, three of which are in fairly close proximity to each other. Each site has its own autonomous storeroom with inventory parts. At each site, there is a part time catalog manager responsible for all database activity. Because the plant is unionized and positions often change, the catalog manager may be replaced every few months.
The resulting inventory catalogs reflect this: inconsistent manufacturer naming; missing manufacturer part numbers; inconsistent use of symbols/abbreviations; spelling mistakes; incomplete descriptions and; duplicate items. System word searches are next to impossible and finding a part is a frustrating, challenging, usually unsuccessful experience.
Maintenance workers at all locations had long lost faith in stores; each kept a stash of parts hidden somewhere for his own use. To plan for a repair job, they would attempt to find parts through the system, but if unable to locate what they needed, they would abandon the search and just order the part directly; in the case of an emergency, they might call another location to request the loan of a part. Inventory value across the company topped $80 million.
Recognizing that something had to be done, the company attempted to undertake the data cleaning themselves. They established a team of nineteen internal people comprised of maintenance workers (Electrical, Mechanical, Instrumentation & Pipe Fitters) from all four sites as well as two support people and one Inventory Specialist.
After more than a year of effort, and with only half the database cleaned, they decided to engage outside data cleaning experts to revitalize the effort. Systematically, the data from each site was cleaned. In conjunction with maintenance workers from all sites, a common layout for item descriptions with acceptable noun/modifier pairs was developed; the order of attributes was negotiated to satisfy all locations; terminology, symbols, abbreviations and industry nomenclature were agreed upon. It took six months to rework the entire database.
Having good data brings with it measurable rewards. Duplicates within sites were revealed to be in the 10% range. Common items across sites were identified in the 25% range. Merging the three regional stores into a central warehouse reduced overall stocking levels and allowed sites to share common critical spares. It also freed up millions in cash savings.
Item searches successfully revealed part information that maintenance workers could count on. As confidence in the central stores grew, additional stock from private caches was repatriated, further adding to the savings realized. Overall, across the company, inventory was reduced by more than 20%.
The data cleansing effort clearly paid for itself several times over. It also became the impetus for other corporate initiatives. The company went on to improve its item-equipment links to further enhance the maintenance system. In addition, it consolidated items along product lines and reduced its supplier base for volume discounts.
Clearly good data yields good results.
Mary Cenedese currently heads up Marketing & Sales for I.M.A. Ltd., a private company specializing in Data Scrubbing, Catalog Management, Inventory Analysis and Inventory Exchange services for MRO items. Mary has a Bachelor of Mathematics (University of Waterloo) and an MBA (University of Western Ontario) and has more than 18 years business experience in Supply Chain Management and Information Technology.
