Archive for the ‘Data Quality’ Category

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Managing online customer interaction

Published by Holger Wandt on 27-04-2011 in Data Quality. 0 Comments Tags: , , , .

 

In my previous post on the First Time Right-principle I explained that the starting point of customer lifetime value lies in making sure that the input of data is correct, valid, complete and standardized. This guiding principle is used in traditional data quality management, but it most definitely applies to “new” ways of dealing with customer data. New channels, online contact forms, self service portals, different customer behaviour – the changing environment adds new challlenges to the art of intelligent data management.

In traditional customer contact processes there is an interaction between people and people and/or systems. In online customer contact processes, the initial interaction is between the customer and the system. This calls for a data quality strategy in which the data will be captured and processed the First Time Right! Whether it is to prevent pollution in your existing database, to raise the customer’s confidence in your company or to guide your customer to the right product; the design of the online interaction process has a substantial impact on the quality of the customer data.

How do you “guide” your (potential) customer to enter the right data in a self-service portal or in a web contatct form (Graham Rhind and Winfried van Holland have been discussing this topic in Data Value talk as well …)? How do you make sure you that you check online loan applications correctly? How do you identify the returning customer? All these and many more questions add an extra dimension and another degree of complexity to the management of online customer interaction.

If you are interested to share discussions on this topic or learn more, you are invited to join our (Dutch) seminar on May 12th at the Human Inference headquarters in Arnhem. See for yourself how the intelligent management of your online , multi-channel customer interaction will have increasingly positive effect on the total data quality in your organization.

More than a name…..

Published by Jochem Poeder on 29-03-2011 in Data Quality. 0 Comments Tags: , , , , , , .

Everyone in this world has a name. When we hear a name however, it is really hard to precisely know what it consists of and how the consisting parts should be written. A name might, for example, contain salutation, one or more titles, given names, initials, one or more family names, and additions. Here’s an example of a name with different name parts:

Mr Peter M Smith PhD

  • Mr – Salutation, also called honorific, a polite way of addressing a person
  • Peter – Given name. The name given to a person at birth. You have male and female names, and sometimes a name can be carried by both. But, in general, it is possible to derive the gender of the person from his/her name(s).
  • M – Initial. An abbreviated form of a given name
  • Smith – Family name
  • PhD – Addition, in this case an academic title

This may appear easy, but due to all different naming conventions in the world, it is definitely not! At Human Inference, we have automated this process by creating a Firefox plugin that can help you interpret the various name parts and assign a gender to the name. It also finds the names which most closely resemble the ones you typed as input.

You can type the full name of a person, and that’s all you need to do. The plugin will make the most probable interpretation, based on the vast knowledge of names it has. It places the parts in the appropriate fields and displays the predicted gender. On top of that it will give you close alternatives for the names or for the way the input can be interpreted. These are shown as a list of suggestions when you right click on the input field. If you think that any of the other suggested interpretations is what you were looking for, you can click on it and it is displayed instead.

Summarizing, the plugin

  • can correct the mistakes that you made writing someone’s name.
  • can be used to segment the name correctly.
  • can provide you with closely resembling suggestions.
  • can predict the gender of the name.
  • is free of charge! Continue reading ‘More than a name…..’

First Time Right in Action

Published by Winfried van Holland on 21-03-2011 in Data Quality. 0 Comments Tags: , , , , , .

In previous blogs on the First Time Right (FTR)-principle, we’ve talked about preventing that your data becomes polluted. After reading the white paper on FTR you might want to see some actual examples. Yesterday, I have seen some demo’s and trials from our development group (special thanks to Kasper Sørensen and Ankit Kumar!) that I want to share. Look and play with it and give me feedback how to improve things. The demos are focussed only on guiding the user to provide correct names (so I’m aware that email, telephone, address, etc is not yet incorporated).

The first demo is a mockup for Microsoft CRM. You should go to the name fields (First name and Last name) and see how the entry form is helping you to guide you to correct names. I need to admit that the Microsoft CRM demo works better in Internet Explorer (I wonder why …. ;-).

The second demo shows the key possibilities of HIquality Name Worldwide in a Linkedin mockup.

I am enthusiastic about the ease to integrate the first time right mechanism in a web form (or any application with a web UI). Engineers showed me that it’s quite non-intrusive, they added five lines in the beginning of the page and it is working already.

Data Quality Analysis – It requires a bit of all worlds

Published by Kasper Sørensen on 17-02-2011 in Data Quality. 1 Comment Tags: , , , , , , , .

Doing a Data Quality Analysis (DQA) is a challenging task. You need to get your head into the domain of the business to understand what the data is all about. You need to talk to the users of the organization to understand how they work with data. And within hours you’ll most probably have a dozen of different data sources that you need to dig into.

Tools, tools, tools
The DQA is not trivial, actually the opposite, so often you’ll see that tool support is lacking. The analyst himself will have to use a toolset that is just as diverse and uncontrollable as the data he is trying to manage. The problem with such an approach is that it will eventually get in your way because you’re trying to get 2-3 independent tools to work nicely together, instead of just having these functions available where you need them.

I don’t mind combining tools at all, but we have to do so with care and acknowledge that combining tools also adds a lot of constraints to our working process. Let us for example say that you’re doing an analysis of string patterns in a set of Company names. You’re noticing a piece of the pattern that shows out to be legal forms like GmbH, Ltd, A/S and so on. You want to separate the legal form from the company name, but switching between tools that do the pattern finding (a profiling tool) and the separation (a transformation, perhaps even ETL, tool) means that you have to go back to step 1 in your workflow and re-do all the steps in your flow in different tools. If your chain of analysis steps is more than just a few steps long, then you’re out for a lot of waste. Continue reading ‘Data Quality Analysis – It requires a bit of all worlds’

We have 180 million names! Which one is right?

Published by Winfried van Holland on 14-02-2011 in Data Quality. 3 Comments Tags: , , , , , , , , , .

The internet is an ocean of wealthy content, but unfortunately, as in the real world, it’s heavily polluted.

As a company in business for 25 years, Human Inference absolutely sees the benefits of the internet. For our reasoning processes, based on natural language processing, we gather content and we classify this content on type, such as given names, family names, prefix, suffix, etc. (See also my blog post on the comparison of apples and oranges ….)

In the past this was done manually by, for example, investigating telephone books or manual research of census lists. But these were the ‘pioneer years’. What we see now is an enormous amount of content that can be gathered on the internet. It’s quite easy to find an internet page with 180 million records of person names. Great, so knowledge gathering is passé now? Continue reading ‘We have 180 million names! Which one is right?’

Do you really know what your customers are actually buying?

Published by Esther Labrie on 02-02-2011 in Data Quality. 0 Comments Tags: , , , , , , , .

 

We all would like to answer that question with a straightforward “Yes”. Yet the reality is that more than half of us must either be a liar, or not know the complete truth of their own data.

The new white paper “Do you really know what your customers are actually buying? – Data Quality and Business Intelligence” asks you that very same question-and then provides you with the means to answer with a full and truthfull “Yes”. The paper covers the problems managers/organizations might run into when working with Management Reports. The integration of many different data sources into an Enterprise Datawarehouse can cause problems that easily go unnoticed. Your data seem to be merged successfully with the ETL toolset combining different definitions for each dimension and relevant hierarchy. In other words: you think you know what you’re selling to whom, but in fact you’re dealing with a lot of undetected duplicates that mess up your Management Reports. Incorporating a Data Quality Firewall within your Datawarehouse will help you solve these problems – you can rely on your reports again.

The paper shows us the Before and After Data Quality Firewall situation. The After Data Quality Firewall situation leads to Management Reports that give true insight in the sales of an organization. The answer is the creation of the Golden Record. Being able to trust their data, your co-workers are happy and productive . But more importantly, based on your Golden Record you can now make adequate decisions for your business.