Platt Perspective on Business and Technology

Building a startup for what you want it to become 30: moving past the initial startup phase 16

Posted in startups by Timothy Platt on February 20, 2018

This is my 30th installment to a series on building a business that can become an effective and even a leading participant in its industry and its business sector, and for its targeted marketplaces (see Startups and Early Stage Businesses and its Page 2 continuation, postings 186 and loosely following for Parts 1-29.)

I focused in Part 28 and again in Part 29 of this, on the increasing importance of big data and of its effective use, and for what is rapidly coming to be essentially all businesses: large and small. And one of the core threads running through that narrative was an at least briefly stated rationale as to why this point of conclusion is increasingly valid already, in our still just emerging 21st century context.

That two part discussion, in fact addressed the first of a list of topics points that I offered in Part 28, as material to be delved into in subsequent series installments from there. The second of those points as initially offered for future consideration was:

• In-house generated, and outside-sourced business intelligence as marketable commodities, and for how they would be used in business planning and for how this type of resource might be selectively commoditized and sold.

And my goal for this posting is to at least begin to discuss and analyze that point. I note in anticipation of doing so that the range of issues that this briefly stated bullet point brings up is vast and complex. And many of the details of that are going to prove pivotally crucial to both individual businesses and to networked groups of them such as supply chain systems, and to the markets and the consumers that these businesses serve too.

I begin this posting and its core line of discussion with a single two word expression as drawn from that bullet pointed topics note: “marketable commodities.” And I begin addressing that by citing the obvious. As soon as I began writing of businesses acquiring outside sourced business intelligence, I at least implicitly began addressing the issues of information commoditization, and its marketability and sale.

The devil, as they say is in the details for that, and in ways that only begin with the challenges of information security in the face of confidentiality concerns, where the most valuable information in this can and often does carry security and confidentiality issues and challenges with it; information not so encumbered tends to be freely and even widely available anyway, and as such has at best just minimal marketable value as such.

And with that noted, I raise the first real issue that of necessity arises here, assuming only that a given body of raw data, processed knowledge developed from it, or some combination thereof, can be organized in such as way as to meet security concerns and requirements:

• What is this information worth, and how would that be determined?

I have in fact been addressing that challenge for quite a while now in this blog, so to keep from repeating earlier lines of discussion and analysis, I begin re-addressing this set of issues here, by offering links to my earlier discussions of this:

Business Intelligence as a Qualitative Distinction – a requirement for effective rules of monetization,
• My series: Business Intelligence as a Quantitative Distinction, as can be found at Macroeconomics and Business as postings 21 and following for its Parts 1-9, and
Depreciation of Value in Non-Rivalrous Goods and the Business Intelligence Life Cycle and its Part 2 continuation.

These postings all go back to 2010 for their writing, so the issues that I would address here have been on my mind for a while now. And the newsworthy events that prompted me to write them then, which still hold topical value now, involve blind spots in how businesses are reviewed and analyzed for their overall market values, as for example in merger and acquisition contexts.

It is easy to determine a replicable and reliable valuation for the rivalrous assets that a business holds, and according to largely consistently determined accounting-based amortization revaluations as those items age. This means everything from physical space and building structures owned, to the furniture and other physical assets held and used within them, and more. It is much more difficult to arrive at a consistent and reliable valuation of business intelligence held by a business, and certainly where that means trade secret or other proprietary data or processed knowledge. So in practice, its valuation often becomes one of seeking to establish a more fluid “what the market would bear” as to viable price offered, if it were to be put on the market for sale.

• But these less tangible assets clearly hold value, and particularly now and for essentially any type of business intelligence that might be held, in an increasingly big data-driven, competitive business context.
• So as we more and more fully enter into a true information-based economy, it becomes more and more important that we be able to set consistently based valuations as to what crucial business intelligence that is held, is actually worth.

Consider the scenario of buying and selling a business with its information assets included, as an example context that happens to bring these issues to a head and in ways that cannot be evaded or overlooked. But these issues are at least as important when a business would buy or sell specific, select information resources too, as part of its ongoing business activities.

I began reconsidering the issues of information valuation here, by mentioning information security in passing. But I have to acknowledge that business risk is in fact a crucially important source of business cost here, and one that can generally be at least probabilistically determined, where overall cost of a possible event or circumstance arising, that would have to be taken into account here at least nominally for its possible impact, can be determined as the product of its likely cost if it does arise in fact, and the likelihood of that happening and with that value expressed as a proportion. So for example, and to keep this simple, if an adverse event would either fully occur or not occur at all (with no partial or limited occurrences possible), and with a maximum penalty due from that or none at all, and its happening would cost $10,000 and it has a .01 likelihood of happening (1%), then its nominal cost that has to be calculated in, would be (0.01 X $10,000) = $100.

This type of nominal cost determination makes the most sense when a business has to arrive at overall ongoing costs accrued over longer periods of time, for a wide range of possible simultaneous risk factors and possible incidents. And when the array of them, each with its own probability of occurring and each with its own predictable costs if it does, becomes complex enough so as to collectively constitute a statistical universe, then overall cost determinations as arrived at over time from this can become on average, quite reliable and predictable. This model as described here is in fact part of the basic foundation of the core business model pursued by insurance companies. But the basic principles outlined here apply to any business that would buy or sell business intelligence and particularly where they seek out or sell information of significant marketable value, where such disclosure might carry risk as well as possible profitable value.

I am going to continue this discussion in a next series installment, where I will at least briefly delve into the issues of how information would be selected and organized for use, and for sale as marketable commodities. In anticipation of that, I note here that anonymizing data and aggregating individually sourced data into general demographic form are important here, but they only address part of this story. I will discuss that basic approach but go beyond it too. Yes, this is in part a matter of managing information security risk, but it is also a matter of establishing the types of information-based value that would be offered in a marketplace, and sold to other businesses, where that at least potentially includes competitors – and particularly if that data is sold to and then resold by a third party business that pursues an information aggregator and clearinghouse business model. After completing that phase of this overall discussion, I will continue on to address the remaining issues as listed at the end of Part 28 for inclusion here.

Meanwhile, you can find this and related material at my Startups and Early Stage Businesses directory and at its Page 2 continuation.

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