Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 13: open source and crowdsourced business intelligence

Posted in macroeconomics by Timothy Platt on February 16, 2014

This is my thirteenth installment to a series in which I discuss and analyze the valuation of information in a business context and from a due diligence and risk remediation perspective, and in terms of what in a physical systems context would be called quantum indeterminacy (see Macroeconomics and Business, postings 137 and following for Parts 1-12.)

I focused in Part 12 on the interactive impact of competing sources of business intelligence as they influence and shape each other’s valuation and achievable price points. But implicit in this discussion was an assumption that the value in business intelligence is entirely in its data per se. When data sources are narrow and limited, and what data that is available is focused and precise, that can be true. But when this data flow widens out for its sourcing and volume into an essentially open ended flood, that ceases to be true. And that brings me to the issues and challenges of open sourced and crowd sourced data as business intelligence, and to a reconsideration and refinement of understanding as to where value is created from it. This installment is fundamentally about business use of big data, and of its data’s valuation in a business intelligence context.

• Big data is not, ultimately, about its data as much as it is about how its open ended flood of raw data is selected from, filtered and evaluated, kept for further analysis and use or set aside, and converted through that into actionable pattern recognition-based insight.
• Ultimately, if you are presented with all possible data but as an indiscriminately unselected and unorganized jumble, it in and of itself cannot offer you any value as a source of business intelligence.
• Finding value from selectively searching out and assembling a meaningful pattern and actionable insight from the flood of data that everyone is looking at … but that few really see, is where a business intelligence provider can set itself apart as offering competitively distinguishing value.
• And this becomes particularly pertinent with crowd and similarly sourced data where challenges of completeness, accuracy, timeliness, bias and other potentially confounding and compromising complications for use, are by definition unknown, due to the open-endedness and lack of due diligence accountability of its wide-ranging sourcing.

This, I add, expands out the range of potential unknowns when collecting, evaluating and assembling data as predictive business intelligence and organizing it so as to convert what begins as raw data into actionable knowledge. And the expression caveat emptor – let the buyer beware comes immediately to mind in this context. Even when the business intelligence developer and provider seeks to pick up the burden of resolving uncertainty here, through selective and knowledgeable filtering and assembly of data as gathered from the flood, into reliable and effectively, productively useful products, the buyer still has to select which business intelligence providers to trust and buy from.

• Track records and history of performance are one obvious source of provider selecting insight here, but in an age of open and crowdsourced reviews where individual reviews might or might not be valid and unbiased, selecting a potential information product provider still calls for a level of sophistication and judgment.
• And this only begins with careful selection of review sources to turn to and rely on. Review derived insight in and of itself can become primarily a source of questions that would be asked in seeking further insight, from which a prospective buyer would make their purchasing decisions – and certainly when sourcing business intelligence from new providers that the buyer has no direct experience with.

Let’s return to the question of predicting flu outbreaks. How does this discussion apply there?

• Bias and even systematic bias in crowd and other open sourced data need not stem from anything like intent.
• It can come from differences in perception and understanding, and in how data offered from original sources can become distorted by the collection and organizing process that it is provided through as it is brought into database systems.
• Even systematic, subtle and difficult to discern bias and skew can be added in when raw data is provided through database-specifying data types and data entry restrictions, where standardization as to how data is organized is essential if that data is to be subject to database inclusion, query-based use and analysis.
• What are the biases of the database system itself? Open source data can and does come from multiple database sources, each with its own limitations and its own data collection and organizing biases and filtering.

Ultimately, effective collection and development of actionable business intelligence, and effective discerning purchase of such information can be at least as much about the metadata of how this business intelligence data was gathered and organized and from where and by whom and for what initial purpose, as it is about the business intelligence data itself.

And this brings me back to the issues and questions of business intelligence as a black box challenge, and the role and utility of taking a quantum indeterminacy approach in business intelligence as initially discussed in Part 1 of this series. I am going to in effect turn pack to the beginning of this series in my next installment to reconsider my initial discussion there, on the basis of all that has followed it. Meanwhile, you can find this and related postings at Macroeconomics and Business.

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