Product Data Management is considered as the most challenging field within Data Quality Management. There are more variables and attributes in Product Data Management, than there is in Customer Data Management.

An example:

Who would think this the same product?  They use different descriptions, measurement and standards.  The examples from e-Commerce are thankfully a little simpler, and would be easier to solve.

If you want to save money in Data Quality Management, Product Data is the area with most potential.  Just by standardize, not cleansing, you have a cost saving potential of 2,5%.

Product Data Management impacts e-Commerce.

Impact on Search Engine Optimazition (SEO)
SEO experts I have talked to tell me that duplicate products and duplicate URL’s are quite common in Webshops.  Duplicate records and URL’s are bad for different reasons:

-          It has negative impact on search rankings

-          It waters out the results in the index

-          If others link to your products, it will be split between the products and the
effect will not be as strong.

Cleansing you Data and URL, will be an effective way to improve your SEO.

Recommendation Engine.
2010 could be the year for the recommendation engine. Amazon has used this for years, and there is a lot of buzz in the market for this feature now.  The increase in Conversion Rate and Page Per Visit is quite amazing.

You will get the best result of you only list the product once; otherwise the recommendations will be spread across several identical products and not be as strong.

Which is most likely to get highest conversion rate?  3 identical products/URL’s with a few recommendations or 1 product with several?

Usability and Image.
How will customers react if they find several entries of the same product?  Maybe the go to the next store, where they only find one entry, to be sure they purchase the right product.

It gives a better impression of your shop if your data is in order. It is a sign that the rest of your business is in order also.

These are just a few examples of impact bad Product Data can have on your web shop, some of the challenges described above can be solved without too much effort, whereas others will be more demanding.  Why don’t you run a test of your data?

Ohh – is it 53 weeks in 2009?

A little Data Quality Glitch story from Norway again.  Skagerak Elektro which provides the street lights in the Municipality of Tønsberg had not taken into the account that 2010 has 53 weeks – not only 52.  So when the clock passed midnight on Sunday, the streetlights blacked out.  They had forgotten to fill in week 53 into the operating system.

Not a big story, but maybe some Data Quality Processes should be in place??

Link to story in Norwegian:

Link to story with using Google Translate

Calling Information for 17 seconds costs 100 Euro

Daylight saving time enden on oct 25, and the time was turned back from 0300 to 0200.  A Norwegian Telecom provider could not cope with this.

One person who called the information for a number to the Taxi service, called 17 seconds at 02:14:28, but the bill said 1 hour and 17 seconds, and run up to about 100 Euro.

The story in Norwegian here:

The story using Google Translate here.

I just read an article in the Norwegian paper Dagbladet.

In Norway there is a State Church. It means it is sponsored by the State and a vast majority (about 85%) of the citizens is member of it.

In the last weeks the Norwegian Church as sent out 3,12 mill election cards to Norwegians over 15 years of age.  The problem is that many members have elected to leave the church and select not to be a member of become members of other churches or organizations.  There seem to be a problem with the registrations of the ones who have left.

This has severe financial implications since you get support for each member. The other churches can miss this income. It might not sound as much – but when there is an estimated that more than 100.000 Norwegians is wrongly enrolled in the state church. For the organization Human Etisk Forbund – it is a loss of about 3,4 Mill NOK

It has also created hard feelings since this is an important issue for a lot of people.

There is no explanation of this Data Quality Failure, if it is Data Entry problems, cleansing problems or just pure sloppiness.    I am just waiting for them to ask for the heavenly powers the posess to solve the problem.

Sales people are the ones who complain most about poor data quality and at the same time probably the ones who create most of the dirty data. 76%  of the dirty data is created in the data entry phase. Why not make it easier by introducing some error tolerance in their CRM/ERP Search, Data Quality Firewall, Online Registration and in the data cleansing procedures?

Why is dirty data created?

There can be multiple correct spellings of a name
Let’s say your customer Christopher Quist calls you? I have gone through the name statistics in the Nordics. There are 10 ways that Christopher is spelled and 7 ways Quist is spelled.  This means there are 70 possible correct ways to write his name!

How big chance is it that the customer care or sales representative hits the correct form? It can be unprofessional to ask Christopher many times, it is time consuming and irritating. With an error tolerant search – the representative would find it immediately.

People hear differently.
I used to work at the Nordic call center for Dell in Denmark. I would hear and spell a name differently than the Danes. The most common way to write Christopher Quist in Norway would be Kristoffer Kvist and in Denmark it would be Christoffer Qvist. In the Nordic Call Centers it is not uncommon to answer telephones from another country, and therefore the chances of “listening” mistakes grow.

People do typos.
In the entering process it is easy to skip a letter, do double lettering, reverse letters, skip spaces, miss the key and hit the one beside it, or insert the key beside the one you hit. If we do all these plausible typos with the most Common way to write Christoffer Qvist in Danish – it would generate 314 ways of entering the name! The Norwegian version of Kristoffer Kvist would generate 293 plausible typos!

Sloppiness
Sometimes people believe it is easier or safer to just enter the data again.

Other mistakes error tolerance covers

• Information written in the wrong field (contact name in the  company field)
• Information is left out (Miller Furniture vs Millers House of Furniture)
• Abbreviations (Chr. Andersen vs Christian Andersen)
• Switch the order of the words (Energiselskabet Buskerud –Buskerud Energiselskab)
• Word Mutations (Miller Direct Marketing – Müller Direct &
  Dialogue Marketing)

What will the result be for you if you have error tolerance?

• Cost reduction – if you have a call center of 100 persons and they would save 20 seconds for each call. They could start immediately serving the customer, instead of making the customer spell their names.

• Happy customers – it is annoying to always have to spell out the information to a sales representative if you want to buy something.

• Happy workers – it is annoying trying to find a customer you know is in the system – but cannot find!  You spoil valuable selling time

Introduce true error tolerance today!

Trends from the Nordics are confirmed in England. Retailers with physical stores are the fastest growing in the online shopping sector.

They have been a little slow to roll out, because of challenges like cannibalization, logistics and branding. I know that several chains are on the move to launch large worldwide shopping sites. In my opinion they will be highly successful and pass the early movers on the net. My basis for this is that they take the challenge of Data Quality seriously – maybe not by choice but out of pure necessity.

The retailers have multiple points of sales, and multiple point of customer data storage.  Point of sales can be Telephone/Customer Service, Customer Clubs, Physical Stores and customer data storage can be in CRM, ERP and logistics system.  In addition they will have several brands where also the customer data is stored in.  When they then try to do multichannel marketing, it is impossible with the structure they have today.

Multichannel seems a little challenging in this picture……

The solution the chains choose is to make a Master Database, with one customer ID – with link to each brand. When you have the Master Data, you can start analyzing for cross selling opportunities. If you are a customer of Brand one, you are also a likely customer of brand 5.  In addition you have tools to filter the information from the multiple points of sales and data. When customer data is entered in one of the point it is checked for duplicates, matched to the right record, checked for fraud. In addition you can set up error tolerant CRM search for the point of sales and enrich the data with Reference data.

Now you are ready for Multichannel Marketing!

The first movers, who often were pure online players, have not had the need for such a rigid Data Quality setup. Where retailers now use professional tools, the pure players still trust their homemade. They will wake up one day and wonder what happened?

What would we say if an African or South American government sent out election card that:

• Says you will have to cast your vote in a non existing election location
• Gives you the wrong address to the election location
• Tells you to vote at two different location, but you should only vote at onel
• Tells you to vote at two different locations, but you should actually vote at a third location?

With our prejudices, I think we would cry “voter fraud”. The above mentioned is not in Africa, but is happening in the Danish EU election. The newspapers bring new stories every day.

It is of course not voter fraud, but just plain poor Data Quality.  Is it much better that poor Data Quality influences the results of the democratic process? In Denmark the EU is already charged with having a democratic deficit, so this is the way to go!

The sad thing is that all this problems could be solved easily with the right process and tools.

You can read more about this in Danish on Politiken.dk

The Information Difference Ltd – a British Analyst Firm has made a research about the vendors on the Data Quality Market. You can read the full report here.

Here is the Diagram that Information Difference has made of the DQ Vendors.

The major vendors in the data quality market are described using the Landscape diagram that follows (see later for more on how this is derived; note that due to a modification in the methodology this time around—this version includes customer satisfaction feedback—like-for-like comparisons between this chart and previous versions should be treated with caution).

Another interesting conclusion in the report is that:

The highest customer feedback scores in this research were for Melissa Data, Datactics and Omikron.

These vendors are focused on solving the DQ challenges at hand. Sometimes so focused so the larger vendors call them Niche vendors. Maybe providing fast and correct result can be as important as delivering largs suites?

I have just attended MDM Summit Europe 2009 – very interesting!

This was about Master Data Management – but single most theme that was discussed was Data Quality.

Statements like: “Data Quality is MDM”,” MDM has to have the best Data Quality” was repeated throughout the Summit.

Can we hope that the issue of Data Quality is finally getting the traction it needs?

We need more of these Data Quality Summits!

It is estimated that 76% of poor data is created in the data entry phase.  Mostly this is due to the fact that the operators simply cannot find the customer, supplier or product.  If you enable the operators to find the record, you will instantly get better data quality.

I have made a checklist of what efficient search for CRM/ERP should be able to handle. You can find the checklist here.

You can also read more about CRM and Data Quality here.

Are you situated in Norway?  My company is running a quiz about Data Quality until the 15th of may, with a new question every week.  You can win a Washing Machine (since this is what data cleansing, data scrubbing is all about)  or a gift card from Elkjøp of 10.000 NOK.

We do this to put the focus on Data Quality, with capital DQ.

You can access the quiz here: