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Top 5 big data migration triggers

By Nadhan Easwaran, HP Distinguished Technologist


In this Information Week article, Jeff Bertolucci outlines 10 big data migration mistakes -- 5 pitfalls and 5 risks. Reading through these risks and pitfalls made me wonder about the scenarios that trigger data migration in the first place. Even though big data introduces additional complexities to these scenarios, the fundamental principles of data migration still apply.


trigger image.jpgHere are my top 5 scenarios that trigger the migration of data. Wherever applicable, the nuances introduced by Big Data are in italics:


1.  Rationalization. When there is a proliferation of data repositories across the enterprise, some with duplicate information, a key step is to rationalize them down to a well-defined set of manageable repositories. Such rationalization requires migration of data from source repositories into the predefined target repositories in the rationalized state. Big data has resulted in the exponential growth of the volume of data generated and consumed requiring continuous monitoring of the landscape of enterprise repositories, as well as the analysis of the data within.


2.  Consolidation. Then there are those scenarios where the same application may have multiple instances enabled by dedicated backend repositories. The ease with which data can be generated and uploaded has resulted in the proliferation of various forms of unstructured data across the enterprise. Localization requirements have fueled the growth of regional repositories from which enterprise-wide data needs to be migrated into a consolidated instance.
3.  Operationalization. Frequently accessed data is derived and organized into consumable chunks in Operational Data Stores (ODS) for more frequent access with low latency. Historical data is held for longer periods in backend source data repositories. Social attention drives the demand for data most pertinent to current events. The need to access the latest videos about the Olympics or the political convention of the week wanes over time. The time periods for which such data has to be consumed and processed into dashboards drive the amount of data is migrated to the ODS.
4.  Compliance. Federal mandates require enterprises to refine the organization and layout of their data. A good example is the Ring-Fencing scenario which requires banks to take steps that would insulate high-street banking businesses from their riskier investment banking arms. This requirement has significant implications on the data resident in or out of the ring-fence which triggers migration of structured and unstructured data (e.g. images of paper-based financial transactions) across the ring-fence.
5.  Informationalization. As I outline in this post on  Informationalizing your data while rationalizing your applications , Harvard Business Review blogger Thomas C Redman introduces the term -- "Informationalization" -- the act of making products and services more valuable to your customers by building in more data and information. Checkout Information by Synthesis. The continuous availability of unstructured data gives new meaning to this term. Enterprises could look to the usage patterns of informationalization scenarios to consider creating canned stores of such information. This, in turn, could require data migrations to be performed.


Data Migration must have been required the moment the second database was stood up in IT. Keeping in mind the risks and pitfalls Bertolucci outlines, the goal for all Data Migrations should be to ensure that the enterprise is operating more efficiently after the migration is performed. Having some context to the specific scenario that requires such migration allows us to be more accommodative of the nuances introduced by big data.


How about you? Have you taken a look at the TDWI Checklist Report that provides insight into the systematic adoption of innovative data management and utilization techniques? A review of this checklist is likely to highlight scenarios within your enterprise that trigger data migrations while retaining a line of sight to your corporate mission and corresponding business objectives.  What types of business benefits you have realized by migrating data? Please let me know.


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