Platt Perspective on Business and Technology

Mining and repurposing of raw data into new types of knowledge – 3

Posted in business and convergent technologies, macroeconomics by Timothy Platt on March 3, 2012

This is my third installment in a series on repurposing accumulated stores of raw data and of processed knowledge, and of the potential – and potential pitfalls of commoditizing and marketing this as information product (see Part 1 and Part 2.) So far I have discussed this topic from the perspective of the data usage buying and selling businesses, only considering the original data sources from the perspective of due diligence and risk remediation as they would apply to those businesses as they sell and buy data access. What of the original data sources and owners that the businesses that hold data accumulations initially collected it from? I turn to that important set of considerations here.

As a first point in this discussion I note the fact that data is frequently accumulated, organized, processed and owned as entries in information repositories, in stages. And each of these accumulation and processing steps combines accessed raw data and newly created raw data, as well as processed knowledge and new organizing metadata.

• For much of the accumulated business intelligence data in storage and use, this traces back to information collected from individual customers – individual people and their families and original data source client businesses.
• This might be accumulated directly by a business or other organization that provides those end users with products and/or services.
• This might be shared with and aggregated by a partner business of the product or service provider that the end users deal with – businesses that the initially data collecting business works with as a business to business supply chain partner. In this case legally specified due diligence and risk remediation requirements might limit precisely what information could be so shared from the original business source, and what of that, the acquiring partner business could in turn share with others and under what circumstances. In either case, data transfer might be allowed but with explicitly limited access within the acquiring business except, for where this data is rendered anonymous as to original client or customer source, with only demographics level data transfer permitted for more wide-spread use.
• Either of these business categories, identified here according to the directness and indirectness of contact with original source, might seek to commoditize areas of its data stores as marketable product.
• The next step in this process of distancing data accumulator and processor from original source might be where a third party business acts as a broker, working with other businesses in helping them to more effectively commoditize their data, and with both higher returns and reduced risk.
• Each step removed from the original source permits aggregating data from wider and wider ranges of sources, making progressively more sophisticated data processing and processed knowledge development possible. Think of this as assembling puzzles with at least opportunity for starting with more pieces of those puzzles, and assembling larger, more complex puzzles.

And at every stage of this distancing and of this more wide-ranging accumulating and combining, due diligence and privacy protection law has to be followed. And the more wide-reaching the sourcing of this data with its pools of raw data and any processed knowledge also shared, the wider the range of state and national venues and legal jurisdictions have to be satisfied. The same types of data coming from the same types of sources might have to meet one set of legal guidelines for privacy protection in one legal jurisdiction but a completely different set of them and perhaps even a contradictory set of requirements for another.

• Data source legal venue identification has to be included with data accumulated as a crucial due diligence mandated meta-tagging requirement.
• And it is important and even required in most cases to include this type of metadata as access monitoring and authorization tags for every step in the process of accumulating and sharing data, that one next step removed from the original raw data source.
• The one point you have to assume every legal jurisdiction will agree on is that any data that passed through their venue will have to be safeguarded according to their privacy and confidentiality laws, and in accordance with their case law precedents and interpretations of those laws.

And with this, I have argued the legal and organizational/operational complexities and challenges of accumulating, managing and processing, and sharing data that initially comes from multiple and even globally distributed sources. I will add that as a final step in the accountability chain, the end-user data access purchaser needs to know and understand the law as to privacy and confidentiality in their own legal jurisdictions too, as they cannot safely break them either. The points that I have been raising here apply to every link and step in the data transfer chain from original source through to and including end-users.

I am going to explicitly turn to the issues of data monetization in my next series installment. Meanwhile, you can find this posting and series at Ubiquitous Computing and Communications – everywhere all the time and at Macroeconomics and Business. I specifically point out as directly applicable background reading Business Intelligence as a Qualitative Distinction – a requirement for effective rules of monetization and my nine part series: Intelligence as a Quantitative Distinction (see Macroeconomics and Business, postings 21 and 23-30.)

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