How To Design A Data Lifecycle Management Framework

Utopia's execs are attempting to change the way companies think about data by creating an enterprise data lifecycle management (EDLM) framework to manage data throughout its lifecycle, from creation and capture through management, maintenance, archiving and disposal.

The model is used to establish benchmarks, set future goals, and measure progress toward targets aimed at identifying duplicate customer records, managing data migration, matching criteria and processes, and more. It also helps marketers reach their target audience with the correct message to better understand demographics and segmentation by pulling in the data from across the enterprise.

The method designs a road map to determine how to manage the complete data supply chain -- what comes first and sequences the company must take to get there.

The first step means creating an enterprise architecture to align visions, goals and objectives; putting together a direction for the strategy; creating principals and models and framework; and designing a multi-generation plan.



The second means designing a data object architecture to align the business, standards, data governance, organization model, and management of change, according to Utopia.

The term lifecycle management originates with the electronics industry. Similar to the way electronics manufacturers manage the lifecycle of devices, Utopia's data framework manages the lifecycle of information from across the entire enterprise.

EDLM enables the data to funnel from the enterprise apps to supporting platforms like the ones serving ads, among other functions. Companies must have the foundational piece correct before they can successfully use the data to serve up ads. Otherwise there's no way to ensure the audience segments the 42-year-old male living in California driving a Cadillac -- even when doing an analysis to determine the customer base by industry and geography. Execs at Coca-Cola, Kimberly-Clark, Kellogg's, and Verizon are some of the clients who have had that talk with Utopia to improve the data quality across the respective companies. 

1 comment about "How To Design A Data Lifecycle Management Framework".
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  1. Pete Austin from Fresh Relevance, March 7, 2013 at 5:20 a.m.

    Obvious point, but companies do not *need* any of this stuff, e.g. they don't need to "have the foundational piece correct before they can successfully use the data to serve up ads". To succeed, you just need data good enough to do a bit better than the alternatives. After that, it's a question of opportunity cost (any delay is bad) and ROI.

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