Garry Moroney

Garry Moroney Before founding Clavis Technology, Garry Moroney was founder and CEO of Similarity Systems another innovative and highly successful data quality solution provider. Similarity Systems was acquired by Informatica Corporation in 2006. Following the acquisition Garry served as General Manager of Informatica’s Data Quality Division. Garry has also worked as a consultant with McKinsey and Company

Author Archive

Is your data ready for Global Data Synchronization?

Tuesday, November 15th, 2011

We all know the global data synchronization (GDS) train is coming down the track, but what most suppliers and retailers don’t know is whether their data is ready for GDS or not.


I say most don’t know; but to be honest if they were to guess they’d probably say it’s nowhere near ready. Anecdotal evidence of lack of preparedness for GDS in terms of data is backed up by industry studies. In the UK the Data Crunch Project carried out by GS1 in conjunction with some of the country’s largest supermarkets and consumer goods suppliers revealed data being used in the supply chain is inconsistent in well over 80 per cent of instances. (more…)

Retailers: Three steps to ensure you can trust data get from your Suppliers

Tuesday, November 8th, 2011

With the growing importance of data to the Retail supply chain and Supplier collaboration, Retailers need to know that they can trust the quality of product data they get from Suppliers.


The fastest way to get your Suppliers to rally round your product data goals is to make data quality one of their key supplier performance indicators. Just as Retailers monitor their Suppliers’ performance against key performance indicators (KPIs) such as On-time Delivery, Order Fulfilment, Packaging and Price Competitiveness they can also measure, monitor and Scorecard them on data quality. (more…)

You might not know it but your job is data quality

Thursday, September 2nd, 2010

The number of people involved in data quality these days goes way beyond those of us who actually have data quality in our job titles. I could go as far as to say that anyone who touches corporate data should take some responsibility for data quality – even if it’s only that we should at the very least tell someone who can do something about it when we see data that is incorrect, invalid or otherwise compromised.


So don’t be ashamed, sing out loud, sing out proud – “my job is data quality.”

Does bad product data stunt your growth?

Tuesday, August 3rd, 2010

You bet it does: bad product data can lead to lost sales due to out of stocks, delays in new product introductions, and lost opportunity due to money and effort being spent on workarounds. That’s got to impact growth.


If the data is inaccurate re-supply can go wrong or shelf placement can be thrown out of whack by faulty dimension data leading to physical products not fitting with the store planograms produced by merchandisers. Although some stock-outs will see consumers purchasing alternative brands, a study by industry analyst firm AMR Research (now part of Gartner) point to nearly half of out-of-stock instances resulting in a lost sale for the retailer. And while this might not always result in a lost sale for the supplier, as the customer can pop across the road to another store, AMR says that in 37 per cent of cases they don’t, so everyone loses.


(more…)

Data Quality Control in the Internet Data Services Economy

Monday, May 10th, 2010

I recently read a fascinating article by Jim Ericson of Information Management Magazine which explores the growth of the “web data service economy”.

You can read the full article here: Net Expectations – What a Web data service economy implies for business. The main thrust of his narrative describes the explosive growth in the availability of web-based data services in recent years, whether they are based on APIs, Representational State Transfer (REST) architecture or other paradigms.

According to Ericson the internet is becoming a giant integration platform with ever-increasing amounts of data available as easily accessible services – and with increasing numbers of organizations both providing and accessing these services over the web. Examples he gives range from the US retail giant BestBuy making its entire product catalogue and pricing available via APIs, to lookup credit report services provided by TransUnion.

But while opportunities abound for data services the article makes the very valid point that one of the big challenges in this environment is “data governance and quality control”.
(more…)

Data Quality: Is Right First Time the Right Way to go?

Monday, January 25th, 2010

The new KPI (key performance indicator) for data quality is “Percent Right First Time”. Organizations have recognized that strategic data quality initiatives should focus on avoiding data errors rather than fixing them.

Right First Time is the core philosophy underlying Clavis Data Quality Steward and so it’s something that we talk about on a daily basis – but the other day an experienced data quality practitioner challenged me on whether Right First Time was  really a realistic objective. He questioned it on two levels: Firstly, can data be defined as ‘right ’ or ‘wrong’. After all isn’t it true that data can be right for one use case, but wrong for another. For example a product length expressed in millimetres to two decimal places may be right for use in a European sales application, but wrong for use in a US engineering application where inches may be more appropriate. Isn’t the proper measure of data quality “fit for purpose,” he asked. (more…)

The Cost of Bad Data

Wednesday, November 11th, 2009

“What is the cost of bad data to our business?” – As all data quality professionals know, this question is not easy to answer. And inability to provide a concrete answer frequently impedes strategic, enterprise-wide investment in data error prevention or correction initiatives.

It’s a bit like asking “what’s the cost of our under performing employees?” (more…)

Preventative Data Quality: Vaccinating against the virus

Tuesday, November 3rd, 2009

All over the world governments are hurriedly stockpiling quantities of vaccine against the H1N1 Virus. They are doing this because the medical profession understands that when it comes to stopping viruses, prevention is by far the best strategy. The benefits of disease prevention – over cure – are many:

  • There is no recovery time for people who are vaccinated so the economic cost for a country in terms of lost work days and medical treatment is largely eliminated.
  • The risk of serious illness or death from the H1N1 virus is very real while there is negligible risk associated with the vaccine.
  • If the vaccine strategy is executed effectively, i.e. at the earliest time, then the spread of the disease will be contained in smaller areas and the number of people affected in any way will be minimized.

    • There are very strong parallels in the world of data and data quality:

  • The economic cost of “recovery time” for people who contract a virus is equivalent to the cost of rework or fixing bad data once it has entered a business’ IT system. These costs can be huge.
  • Ok it would be very rare or, though not unknown, for people to die from bad data, but the risk of business failure is high. While catastrophic failure of a business due to data quality is thankfully also fairly rare, almost all companies today suffer from multiple ongoing, day-to-day problems and inefficiencies caused by low quality. It’s a bit like a persistent cold which stops your business performing at its best.
  • The parallels between the spread of a virus through a community and the spread of bad data through a company’s IT systems are also very strong. One of the big challenges with data is a single data error in a single system can quickly become multiple data errors in multiple systems. And as more systems are connected and integrated for real time data sharing this problem grows exponentially.

    • View bad data as millions of potential viruses threatening your business – and work on your prevention strategy.

    When it comes to Data Quality Prevention is better than Cure

    Monday, October 5th, 2009

    Welcome to the new Clavis Technology Data Quality and Data Governance Blog. We hope to use this forum to share some of our experience of the world of data quality and data governance in the coming months and years. At Clavis we have some of the foremost experts in the area, with many years experience in delivering data quality and data governance solutions to large organizations.

    For my part I intend to focus on the importance of “preventative data quality” and the greater value that can be achieved by getting your data “right first time”. (more…)

    Clavis Data Quality Blog Arriving Soon

    Friday, October 2nd, 2009

    The Clavis Data Quality Blog will be coming to this space very soon – We look forward to sharing our thoughts and experience on the world of data quality, data governance and master data management.