During a recent Data Migration Roundtable, a delegate confided with me that they were feeling overwhelmed at the thought of having to coordinate and oversee a large data migration project.
Coping with all the moving parts of a complex data migration is a common challenge, so I wanted to share some practical ways that I’ve approached this in the past.
“Unity is strength when there is teamwork and collaboration, wonderful things can be achieved” Mattie Stepanek
At Experian we firmly believe that we can make a difference to society and our communities by helping people to make the most of their data to ‘create a better tomorrow’. For us being committed to this vision also means playing a role in celebrating and supporting diversity – and that’s just as important inside our business as out.
Traditionally, organisations have tackled their SCV requirement through the deployment of an MDM platform. And yet, as Philip discusses in his paper, ‘MDM has always been complex, costly and time-consuming to implement’ and so not necessarily, therefore, in tune with modern business requirements. Layer in an increase in regulation and we have a perfect storm of reasons for organisations to seek an alternative route.
So, what options are there for organisations looking to keep costs to a minimum or take a more agile approach to developing an SCV?
It was June, and Europe was turning to summer. Excited, I was walking around the office, when my colleagues caught my good mood. "What are you doing this weekend?" I answered "Cycling from London to Paris!" I have ridden cycling races and sportives, but wasn’t sure what to expect from my body, my mind and my companions. After 3 days, 36c heat, 20 energy gels, 2 punctures and 270 miles in the saddle - I learnt these three things.
We recently hosted a packed-out Data Migration Roundtable event that focused on some research that we commissioned from Data Migration Pro to explore ‘the state of the nation’ regarding modern data migration projects. In this post, I want to cover off some of the questions that we simply didn’t get a chance to cover fully on the day.
The impact of the GDPR (or the General Data Protection Regulation) on data strategies is rapidly coming into focus ahead of the 25th May 2018 deadline, the date on which it comes into force.
With this in mind, I thought it would be useful to take a look at one particular element of the GDPR that is going to present both opportunities and challenges for businesses. That element is the right to data portability.
If you saw my recent Global Data Management Research highlights post you’ll be aware that there are some pressing issues that organisations are up against in 2017. I was delighted therefore to have the opportunity to explore some of these issues further in a feature article on IT Pro Portal. You can see this below or visit the site to read it along with a wealth of other data-related market perspectives.
At Experian I’m always looking for ways to improve our customer experience and give our clients what they need to exceed their data ambitions. With this in mind I was delighted to be part of the recent launch of our brand new Customer Support Hub. It’s the product of some really in-depth analysis around exactly how our customers want to interact with us and access support for our tools.
During a recent interview with Rebecca Hennessey, I also explored some of the key findings and recommendations from the research.
In this post, I want to expand on those earlier insights by introducing the ‘Data Migration Butterfly Effect’.
This phenomenon occurs when a few simple oversights, typically at the beginning of a data migration project, can cause a chain reaction leading to widespread chaos and delays as the project develops!
Goldfish have a six second memory. The Great Wall of China can be seen from space. We only use 10% of our brain. Myths have an annoying habit of becoming accepted as truth if enough people hear them and enough time passes – and the Data Quality industry isn’t immune to this.
So, I have taken it upon myself to ‘debunk’ some Data Quality myths and provide some great resources to help you succeed in your data quality initiatives.
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