The goal of the Enhance/Enrich model is to spare organisations the trouble of preprocessing data on their own. Unlike filtering, however, the strategy here is to add, not subtract or normalize. A unique value proposition can be created by joining two datasets, or even deriving some (computationally intensive) results out of a single dataset. The enhanced data then can be sold to other parties, or kept within the organisation to improve their own business.
4. Simplify Access
Sometimes companies do not want to deal with raw, bulk data downloads, especially when the data arrives in spreadsheets, fixed-width files, PDFs, or other forms not readily amenable to analysis. That means there is an opportunity to provide just the data customers need in a format they can handle.
This is a logical extension to an existing Collect/Supply business, with a bit of Filter/Refine thrown on the back end. Given several customers who buy the same raw data and who perform the same post-processing, data managers can do it for them at some reasonable cost.
To take it further, data managers can also put the data behind an application programming interface (API) such that customers can programmatically fetch the subsets they are after, and in a machine-readable format. Consider cases in which the raw data is both bulky and comprises a large superset of what the customer really wants. Customers can either download all the data and write their own routines to extract the portions of interest, or they can pay data managers to subset and extract on demand.
The last strategy, Consult/Advise, is the most open ended. Consulting is certainly not unique to the data arena, but it does come with its own playbook. Here, consultants trade money for access to their experience and expertise. For example, people with strong economics or finance experience can add a perspective beyond pure data analysis. They would readily see that airline frequent-flyer programs and retail points-for-participation services represent virtual currencies, with the airlines and retail firms acting as central banks.
Conclusion: Ultimately, no one strategy is a perfect moneymaker, nor is any particular idea categorically superior to any other. What constitutes a strategy's benefits and drawbacks relies very much on every organisation's abilities and resources. Considerations that cross-cut the strategies, such as domain knowledge, technical skills, usage rights, and general business concerns need to be factored in when deciding on the best model for every business. With trends such as Internet of Things, mobility and social media dominating our lives today, these models will help businesses manage and maximize the potential of data in their hands.
 IDC, Asia/Pacific Big Data Technology and Services 2013-2017 Analysis and Forecast: The Journey to Tech+ Transformation Continues, Next Stop Is Innovation, Oct 2013
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