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I had a customer meeting (large insurance company) last week. The customer wanted to know how technology could help deliver trusted data across the enterprise. They know that their data has big problems. They know that the solution involves new processes and responsible roles to deliver fit-for-purpose data. They know they need a single source of the truth – a hub. A most compelling comment was “you can’t govern something until you have something to govern – that why we need a hub”. This statement started a debate on what comes first – data quality / data governance processes or the hub.
Informatica’s annual sales kickoff took place in mid-January in Las Vegas. In attendance were members of over 39 partner companies, a 50% increase from 2008. I’d like to thank the attending partners, their presence and enthusiasm helped make this the most successful kickoff in Informatica history! We also recognized our top alliances partners with our annual Informatica Alliances Partner Awards. Congratulations to Accenture, Cognizant, HP and Teradata, our 2009 award winners.
Global Partner of the Year – Accenture
If you haven’t been following along, in my previous posting I reviewed the Data Quality Positioning Gap as a non-traditional challenge to achieving data quality success. In this post, I will discuss the Perception Gap.
The Data Quality Perception Gap
Assuming we have properly met the challenges associated with customer expectations and solution positioning, chances are that our customer’s are still not “buying” because of the fifth non-traditional challenge…….data quality solutions are perceived as theoretical or impractical. Often times, data quality solutions appear to boil the ocean and our customers become overwhelmed with the scope and complexity or rightfully dubious of the likelihood of success. While this may not be readily apparent from the customer’s objections or from their rationale for why not to proceed, it is a leading reason why data quality solutions never see the light of day. In order to win our customers’ confidence and their business, we need to be viewed as a data quality expert. Proposing solutions that strain credulity calls this expertise into question. (more…)
At the beginning of an Information Lifecycle Management (ILM) project for my client’s Application and Data warehouse databases, my dialog begins with Records Management and the executive team to assess their ILM and Data Governance maturity. These questions were briefly mentioned in my previous blog. Here is some background on why the answers can dictate an ILM project’s success.
Are data retention schedules defined and are they assigned to a business owner?
Data targeted for ILM needs a business owner who is accountable and responsible for the data lifecycle – including defining when the data can be archived or deleted. If data retention schedules do not exist or aren’t enforced, data volumes grow uncontrollably causing problems in the data center. IT then owns the problem but isn’t able to address the solution unless business tells them what data can go where. If data needs to be retained for longer periods of time, the business needs to provide IT with access requirements so they can properly design a database archive.