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:
This isn’t speculation, it’s fact borne from independent studies from the likes of Bloor Research and hundreds of interviews with practitioners.
What a lot of business leaders don’t realise however is the advantage of keeping the ball rolling with data quality after the migration terminates.
Why would we invest all that time, cost and energy into protecting such a critical asset only to watch it slide into disrepair post-migration?
Doesn’t make financial sense, operational sense or common sense. Agreed?
The purpose of this article is to outline 6 reasons why implementing data quality both during AND after your migration is the right choice and it all stems from a perfect opportunity that migration projects present.
Data migration - a unique meeting of minds (and technology)
Having spoken to hundreds of data quality leaders I’ve discovered there are 6 common elements to every successful data quality initiative:
If you remove any of those elements it can make it far more difficult to either launch or sustain your data quality initiative but fortunately your migration project offers the perfect environment for these elements to come together.
That’s the good news.
However, what do business leaders typically do after the migration project terminates?
That’s the bad news. Especially for users, customers and the balance sheet.
Remember why your organisation was introducing that new target system? It was probably for one (or more) of these reasons:
Data Quality Management has been proven time and time again to support each of those objectives above so to throw away the platform you’ve created is astonishingly short-sighted.
Remember how much effort it took to resolve data quality in your legacy environment because it had been neglected? That’s exactly what will happen if you ignore data quality in the new target environment. Over time the data will become more and more degraded, costing the organisation in stranded assets, customer churn, financial anomalies and compliance failure.
The problem is compounded by the fact that many new systems still have to share information with existing legacy systems, many of which still have poor quality data. So implementing a data quality strategy for the longer term by focusing on your target environment first but then building out capabilities to your entire legacy real-estate is definitely a smart move.
What data quality facets can be reused after the data migration project?
So you’re bought in on the concept but now you need to convince stakeholders of the value of retaining your data quality resources long term. To do this you can share the useful list below:
Skills and expertise
Data quality methodology
Data quality technology
What are your views on the importance of post-migration data quality?