Greg Taylor is one of the Marketing Managers at Experian Data Quality and has been with us for nearly 5 years. Outside of marketing and data quality, Greg is a keen sportsman and is enjoying his first taste of fatherhood.
Last year, Experian commissioned Dylan Jones and the team at Data Migration Pro to carry out a programme of research looking at the current data migration landscape.
The output was a fascinating, ‘in-the-trenches’, account of modern migration practices that identified some clear trends that can lead to project success. This blog will give a good overview of the key findings, so read on for my highlights.
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?
In my previous blog, I talked about Experian's brand new Data Quality Improvement Assessment, why we developed it and how we hope organisations will benefit from using it. The assessment gauges an organisation's data quality strategy maturity and plots the results on our data quality maturity curve as well as offering useful reading and advice.
In my last blog, I gave some background to Experian's brand new Data Quality Improvement Assessment around why we developed it and what we hope organisations will get out of it. The assessment plots an organisations data quality strategy on a graph, meaning they can benchmark themselves on how sophisticated their data quality strategy is.
One of the core themes discussed at our Data Quality Summit back in October was maturity, and in particular, strategy maturity around data management and data quality practices. This discussion is the result of a shift in both language and intent from the data custodians that we’ve engaged with over the last few years and highlights the importance organisations are placing on making their data fit for purpose.
With the dust settling once more on our annual data quality summit I just wanted to take a few minutes to reflect on what a thought provoking and insightful day it turned out to be.
Customer Relationship Management, better known in acronym form as CRM, is extremely important in these days. In a digital age, where organisations are trying to do more with less, it is vital that audiences are successfully engaged. CRM ensures that this is done to the highest and most effective standard.
A vast majority of organisations are vying for better quality data, as they understand the benefits attached to ensuring it is clean and valid.
However, many are still relying on short-term, tactical approaches to managing these initiatives rather than investing in governance - something that can infinitely increase the success and sustainability of data quality schemes.
Data governance provides organisations looking for better quality with the solution they need, rather than offering a quick fix.
There is a notable shift occurring in how organisations engage with data. As the technology and understanding of this asset changes, more and more firms are looking to experts to help them develop their strategies, so that they deliver a programme that is efficient, productive and cost-effective.
Marketers have revealed that data is going to fundamentally transform the way they work, according to research by the German market research institute GfK.