Fight Global Warming with Data Quality - Part 2

In Fight Global Warming With Data Quality we showed how increased focus on Data Quality could help the environment, especially in the Direct Mail industry.

TNT Post has taken the lead in this field and introduces the “eco-mark” for the brands using their services. Data Quality is one of the key factors to obtain the “eco-mark”

Data cleansing has been flagged as one of the best ways a business can improve its marketing efficiency and score well on TNT’s carbon calculator, as well as save money in both the short and long term.

You can read more about the initiative on MarketScan

New MDM reasearch shows All Best-In-Class companies focused on Data Quality

In a new research by Aberdeen Group called “Winning Master Data Management (MDM) Strategies for 2008-2009″ showed that 100% of businesses suceeding with MDM, focused on Data Quality.

All Best-in-Class companies focused on customer data quality surveyed by Aberdeen Group (100%) invest in data collection, cleansing, and analysis tools, while 98% invest in data process management.

Is there any doubt that Data Quality is one of the key sucessfactors of MDM? You can read more about the research on CNNMoney.com

DM Review: Building a Foundation for Data Quality Success

In the latest newsletter DM Review has a good article by Bob Haganau about Data Quality Success.  I encourage everybody to read it.

I have focused a lot on the GIGO - Garbage In - Garbage Out problem, which is a initial cleansing issue.  In this artice Mr. Haganau also focus on the ongoing prevention:

just when one marketing database is cleaned up, a new campaign brings in all-new leads. As soon as one system migration is completed, another company is acquired – and so on. To be successful in this environment, companies must build a strong foundation of people, process and technology to ensure data quality”

In addition Mr.Hagenau brings some interesting examples and successfactors.  Read and enjoy!

Put a Data Quality Backbone in your CRM system.

It has become widely accepted that Data Quality is the main reason for CRM failure:

“Without clean data, there is no CRM. Poor data quality can lead to serious business problems.” - is quoted from an Article called “Avoid the pitfalls of poor data quality” in cxoamerica.com

“If there is no standard of quality for data, a CRM system becomes useless” - is quoted fromblog called Poor Data Quality = CRM Failure based on this article in BTOBonline

“Dirty Data can jeopardize your CRM” – an article in itworld.com

“most executives are oblivious to the data quality lacerations that are slowly bleeding their companies to death,” – is quoted from an article called Data quality: ‘The cornerstone of CRM‘ in Computer world

“The simplest systems can deliver amazing results if they operate on clean data.”  - is quoted from the article “The Bane of CRM – Data Quality

Gartner estimates that 50% of CRM implementations fail.

Here is an efficient way of securing your Data Quality Backbone:

Omikron Data Quality Server has through several implementations proved to be the backbone of several successful CRM systems. With it’s 2 core modules it secures that your operatives find their customers, and that the data stays clean and up to date.

Module 1: Find your customers easy, fast and intelligent with FACT-Finder Address-Search

Most duplicates are created in the data entering phase, because the operators don’t find the existing customers. Most systems have search/duplicate checks, but they are not very efficient and return few hits. If you are able to find your customers, you have solved a major part of the problem.

FACT-Finder is Europe’s Market leading search- and navigation engine for online shops. We use the same technology in our FACT-Finder Address Search to search in your CRM system. Fact Finder Address-Search is:

• Error tolerant
• Quick
• Language independent
• Easy to navigate in the results
• Easy to integrate

The illustration shows how the DQS works and communicate with CRM and ERP systems.

Module 2: Always a clean database with DQS Duplicate Check

DQS Duplicate Check will ensure that your database(s) are up to date. It can

• cleanse your data for duplicates and can enrich them with reference data every day
• match and consolidate the data across several databases and systems
• monitor the data entry phase to detect where poor data quality is initiated

Additional Modules for Data Quality Server
Data Quality Server is module based. Here are some of the other modules:

• Sanction List Comparison
• E-mail verification
• Telephone Number Plan verification
• Gender Assignement
• Postal Correction
• Data Enrichement

You can read more about Omikron Data Quality Server in this electronic brochure or on the webpage.

Fight Global Warming with Data Quality

The Direct Mail industry is urged to go green.  Experian is referring to surveys that show if Direct Mail companies wants to reduce their waste and be more cost-efficient, they have to make better use of Data Quality Systems. This will ensure their records are as accurate as possible.

I haven’t found this survey myself, and cannot link to it.

But I believe the survey.  Let’s do the math with an imagined example:

A telephone company sends out 1.000.000 letters in a DM campaign.
They have 5% duplicates/bad records which is 50.000 wasted letters
Each letter weigh 20 grams, so the waste is 1.000 kg of paper
This equals to 24 trees. They do 10 campaigns a year, and waste 240 trees.

What would the economic incentive be for this company to save 240 trees?
50.000 wasted letter ten times a year at a cost of 80 cents?  400.000 Euros in direct costs. In addition you avoid soft costs like lower customer attrition, employee satisfaction, error rework and the list goes on.  Not so bad in these hardening economic times?

From CW: Dirty Data Blights the Bottom Line

I stumbled over this article in ComputerWorld, and just saw the relevance to my post on the sexiness of Data Quality:

Data quality isn’t a glamorous topic, but Companies ignore it — especially for internal systems- at their financial peril.

I advice you to read the article since it includes a good case study, and some very valuable hints on how to go about asucessful DW project.

I feel like I am repeating myself, but hopefully I will wake someone up about Data Quality!

Survey shows that Sweden has the best and Norway the worst Data Quality in the Nordics.

A survey* Omikron Data Quality has done in the Nordics shows that Swedish Companies has the best and Norwegian Companies the worst Data Quality of their customer databases. By using our Data Quality Software we have tested Nordic companies, regardless of size and business segments, on how many duplicates they have in their customer databases.

The results shows that Sweden had the best Data Qualty, with an average of 14,9% of duplicates, followed by Denmark with 16,7% and in the bottom Norway with 19,7%. The Nordic average is 17,1%

An interesting observation is that whereas in Sweden, all the results are close to the average, there are great discrepancies in the Norwegian numbers. They are either very good or very bad.

A possible reason why Norway scores in the bottom is that there is a little overrepresentation of companies that want to consolidate and merge several databases. Probably because of company mergers or they want to convert their data to one system. It is critical to start the new system with clean data. It is actually positive for Norway that it is an overrepresentation of these consolidations, because it is a sign that the problem is taken seriously.

The reason why Sweden has the top score is because the companies possibilities to register personal data is more liberal in Sweden than the rest of the Nordics. The official register for companies in Norway is not 100% accurate either, which affects the companies data.

Why are duplicates the single biggest problem in customer databases?

There are high indirect and direct cost related to duplicates and bad data quality. Frustrated employees that contacts the customers double, or use a lot of time for error-rework, lost customers, lost customer attrition and wrong foundation for decisions are just a few points. We have in an earlier posting showed how some estimates the cost of poor data to 10-20% of your revenue. In the bottom of this post you will find an example of how much bad data quality costs, and what Swedish companies win in comparison to Norwegian companies.

What is the cost of bad Data Quality

Method : 1 in 10 rule by Thomas C. Redman

In this theory Thomas Redman explains how a perfect customer records costs 1 USD, a bad will cost 10 USD. We use the results of the survey.

An interesting observation is that a Norwegian company with 500.000 customer records with the average Data Quality will have nearly a quarter million in more costs than a comperative Swedish company.

How is your Data Quality?

If you want to test your customer data, to see if you make decisions based on correct data, you can contact me on jei@omikron.net

*the survey is not scientific because of too few testdata to get a 100% representative results. But the survey gives us an indication of the situation.

Is Data Quality as Sexy as Intestinal Medicine?

This weekend I was to a dinner with the Queens Heart Surgeon.  We talked about the hierarchy of surgeons, where he is at the top, Brain Surgeons is at 2nd and Intestinal Surgeons are at the bottom.  He is at the top because he literally holds life and death in his hands, whereas Intestinal Surgeons just has a shitty job.  Even though it’s unpleasant for the patients, they can learn to live with it, and it’s seldom lethal. 

After being to some Conferences and Meetings about BI and MDM, I ask myself:  Isn’t this how it is presumed to be in the IT world?  The big BI and DW vendors and integrators struts their charts and reports and show how important they are to the businesses and imposes as Heart Surgeons.  They claim that Data Quality is something you can take on the fly and want to project it as Intestinal Medicine.

Shouldn’t it be the other way around?  Gartner predicts that 50% of DW and CRM projects will fail, and the single most important reason is poor Data Quality.   It seems like it is the Data Quality Operators that are holding the life or death of these projects in their hands!

So from now on Data Quality Operators are compared to Heart Surgeons, and OK, I’ll give it to the big BI and DW vendors and integrators; they can be Brain Surgeons, a good 2nd in the hierarchy. :-) 

Data Quality moves into the boardrooms

Since 2005 the number of businesses where Data Quality and Integrity has risen to boardroom level has risen with 16 per cent. This according to the QAS report, “Contact Data: Neglected Asset Seeks Owner”.

This is very good news and shows the rising importance of taking Data Quality seriously. There is a way to go stil though for only 46 per cent have a documented Data Quality strategy. 34 per cent does not validate any of ther customer or prospect data.

The number of employees that work directly with Data Quality has also risen by 5% in the same period

Data Quality in BI “as we go along”

In the last year I have been to several conferences and network meetings about DW and BI. I have heard several customer stories.  They all tell about the fantastic economic reporting system, and how easy it has become for the managers to access information.

When they are asked about customer data quality issues, they often say ”we take that as
we go along” or ”we will cross that bridge later”.  Their main reason is that they need to
get the financial reporting in place, and this takes all their human and financial resources.

Do they really think their reports will be correct if they base their calculation on bad data?  How do they “take it” down the road?

We know that professional data quality tools finds another 1-5 per cent new duplicates then the cleansing tool included in the BI-package. Let’s assume that it is 2 per cent on average. A couple of customer cases i have listened to have 2 million customer records. 2 per cent accounts to 40.000 customers! I would think this would influence those economic reports. Is it your best customers maybe?

Lack of trustworthy data can pull the rug under many BI and DW projects. Why is it these problems are not taken seriously.

Here are some previous postings on the topic:
Ten Mistakes to Avoid in Data Quality Management 
Survey shows BI Key Benefits are Intangible
“Without proper data, BI are meaningless”
Laissez-Faire: the biggest obstacle to improved Data Quality