Today we live in a world where data is the foundation on which any profitable organisation is built. However without processes in place to keep data clean, insights and value cannot be extracted and fully utilised. Clean data is a must have for organisations today and data cleansing tools are available to ensure you get the most out of your data.
Today is a proud day for everyone at Experian Data Quality. Gartner’s 2014 Magic Quadrant for Data Quality Tools has just been released listing Experian Data Quality for the first time.
Gartner has positioned Experian Data Quality as a ‘Challenger.’ I believe this reflects our success in providing excellent data profiling and discovery solutions that help organisations better utilise and understand their data.
The exponential growth of data has proved a growing challenge for businesses across all sectors, but also an increasing opportunity. New research shows that 90% of those surveyed believe data has changed the way organisations are doing business.
UK businesses are realising the strategic value of data as a competitive differentiator, a customer experience enhancer as well as a direct influencer of revenue. In order to make this a reality businesses are under increasing pressure to provide reliable and consistent data that is accessible corporate wide. Often by default rather than design, this responsibility has fallen on the shoulders of Chief Information Officers (CIOs).
So you are looking to migrate data from one system to another and the business thinks it’s as easy as copy and pasting from one spread sheet to another and is often an afterthought at the end of a CRM migration. I simplify perhaps, but that’s what it feels like some management and non-technical people think. Trying to explain why data migration can be complicated and fraught with peril is like trying to bash a square peg into a round hole. Sometimes only failure can open people’s eyes.
This post and webinar covers a novel approach for delivering a complete end-to-end migration framework using a single platform.
Including the early phase extraction, profiling and discovery activities all the way through to data quality rules, data mapping, prototyping, transformation and finally archival.
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.
You know by now that a data quality strategy is vital for your data migration project and by means of a quick recap, the reason why data migration projects overrun so often is partly due to:
I was recently asked:
“What would you expect to see included in a data quality issues log?”.
This is a great question because although some modern data quality tools will allow you to track data quality issues, a lot of data quality defects are first reported by everyday business users. We therefore need a method of tracking from both a business and technical perspective.
There were a vast amount of questions that were asked before, during and after our webinar ‘Squaring the Circle: Using a data governance framework to support Data Quality’ on the 4th September and unfortunately we ran out of time to answer them all. So, Janani and I thought we would incorporate your questions and turn this blog post into a virtual Q&A session.
A friend of mine recently moved into a new build flat earlier this year. The move went quiet smooth in terms of shifting and lifting his belongings. What didn’t go well was him having to do what all responsible adults do; take out insurance on his flat, car and gadgets.
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