Platt Perspective on Business and Technology

Building a startup for what you want it to become 25: moving past the initial startup phase 11

Posted in startups by Timothy Platt on July 31, 2017

This is my 25th 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 1-24.)

I focused in Part 24 on the terms “aggressive” and “conservative” as they are used in a business planning and execution context over time, and in the course of that discussion, used those terms in what at first glance might seem to be two different and even somewhat contradictory ways. My goal for this posting is to focus on making business analysis more effectively data-driven and I will primarily address that complex of issues here. But I wanted to begin this posting at least, by further considering what those two terms mean, and how they are used.

I stated in Part 24 that:

• Conservative in this does not necessarily mean building for reduced overall risk, and aggressive does not necessarily mean building from a more risk accepting and risk tolerant perspective, and it does not necessarily mean accepting more of it – even if that can be the case when making specific comparisons between specific businesses.
• The real distinction here can in fact simply be one of where the risk that is allowed for, is considered acceptable in the business’ overall operational systems.

And then I used these terms in a manner that might seem more consistent with an assumption that conservative does in fact mean less risk tolerant and aggressive means more risk tolerant, per se. When you consider overall risk and with both short and long term risk possibilities included, this alternative is not in general always valid. But when you focus on shorter term and even on mid-range risk and their management, conservative approaches do tend to be more risk aversive there, and aggressive tends to be more risk tolerant there. This understanding is consistent with my own experience and my own observations so it underlies how I use these terms as a practical manner. Most organizations and of all types tend to weigh shorter term risk potential more heavily than they do longer-term, and certainly given the perception of uncertainty and of plan-undermining change that longer carries. Overall, however and when all timeframe considerations are accounted for, conservative and aggressive can come to look more and more alike. And long-term strategy has to allow for that as well as explicitly considering the range of timeframes faced.

With that noted, I turn to consider data driven business analysis, and I do so by offering a basic empirically grounded assertion:

• Not all data is created equal – and the challenge of creating effectively useful knowledge out of raw data begins with effectively evaluating, organizing and prioritizing it.

And business analysis, of course, is a process of making useful actionable knowledge out of carefully selected and organized raw data. So this and the strategic and operational planning that come out of it have to be based on a finely tuned understanding of what data is being used, and of its value for this.

I begin addressing this by posing a basic starter set of questions that would apply to essentially any data or data sets that might come up for consideration:

• Is this data reliable, and if so for what? I parse that question, offered here as a general point of principle, into a set of more focused related questions.
• Where did it come from? And how reliable is its source from prior experience?
• Is it complete and unedited or has it been pre-filtered or re-represented in some way, by a stakeholder who might be bringing their own biases or agendas with them when offering it? Answers to this question would in most cases be more presumptive than conclusive but evidence of possible filtering or bias should raise red flags and should always be considered as a possibility. As an example of how pre-filtering can be carried out without any intent of adding bias into a data set but still end up adding that in, consider how data can be “cleaned up” before use by deleting from consideration, unexpected and seemingly out of pattern outliers and other “anomalies”, while removing second copies of duplicated records and the like. That happens and it should raise red flags.
• Is this data consistent with other data gathered and with expectations in place, or is it divergent from or contrarian to that? Note, new and different and unexpected should not rule out new data findings. But they should prompt closer and fuller examination and particularly if their inclusion would significantly shape conclusions drawn and actions taken.
• And of course, what would this data suggest and certainly when considered in the larger context of what is already known?
• And what are the consequences of that, and both if this data is correct and reliable and if it is not?

This type and depth of input analysis is almost certain to be carried out if a set of possible data under consideration is deemed a priori to that, to be actionably important and consequential. But this type of analysis is much less likely to take place and certainly with any thoroughness, at least a priori to using it in planning and execution, if it does not in some way stand out as potentially game changing. And in an increasingly emerging big data context that all businesses face, that means less and less of the data flow coming in faces even cursory review and quality control and can essentially become taken for granted.

I am writing here of a need to automate incoming data quality control, and as an increasingly vital risk management issue. And yes, big data is not just a possibility, or even a necessity for just big and established businesses. Small and new businesses can find themselves immersed in it too, and of fundamental necessity and as part of any realistic execution of their business plan.

I am going to discuss this set of issues in more detail in a next series installment where I will focus on specific types of raw data as business intelligence, and in the more specific context of an at least briefly sketched out working business example. 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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