Platt Perspective on Business and Technology

Building a startup for what you want it to become 28: moving past the initial startup phase 14

Posted in startups by Timothy Platt on November 30, 2017

This is my 28th 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 1-27.)

I have been writing this blog as a whole, with an emerging 21st century orientation in mind, and with a goal of developing and presenting business approaches and understandings that would offer value for an organization as it faces a flow of oncoming change and disruptive change that is already taking shape around us all and that will only continue and for all. I focused in this regard in Part 27, on the emerging need for, and commoditization of big data capabilities for planning and managing a business, and both in its immediate here and now and as it builds for its future. And I wrote there of how this capability is becoming a necessity for smaller businesses, and just as much as it is for big data’s more traditional big corporate adherents.

Big data, and certainly as it has emerged as an essentially ubiquitous innovation that is capable of offering insight to any desired fineness of granularity, is just one of a growing range of disruptive changes that are redefining the world around us and for all of us, and not just in a business and economic context. So in a real sense that particular disruptive change and source of it just happens to be the one that I am focusing on here, and it is one where overall impact has already proven to be profound. The question that I would raise in this context and here in this series, is one of how a given business can better tap into and benefit from the positives of this change, while more effectively managing and limiting any downsides that would accompany them.

And with that noted, I offer a point of acknowledgement. Simply stating that big data is becoming a necessity and for more and more businesses, as begun in Part 27 and repeated here, offers very little value beyond my stating two obvious points:

• Big data with its use in business planning and execution is more than just a passing fad – it is rapidly becoming a long-term essential, and what we have seen of it up to now is just an early and even still-embryonic stage in development for what is to come.
• And it represents a capability that is evolving and very rapidly to be more and more cost-effective and for more and more types of businesses and for more and more types of use within them. I particularly stress expanding range of use here.

That leads me to the fundamental question:

• How can a business more effectively tap into and benefit from all of this?
• Any valid answer to that has to start within the business itself. Simply looking for a “best” source of hardware and in-house software, and cloud-based resources for this that would reside outside of the organization,
• For collecting and organizing a business’ own data and applying in-house and outside-based resources to it,
• Cannot suffice.

In fact that type of a tools first approach is almost certain to lead you to arrive at a poor-for-you implementation and no matter how diligently and thoroughly you research your tools options. Begin by looking within your business itself and at your needs and opportunities there. Then look for the resources that would best help you specifically meet them, and for both the more replicably generic areas of your business model and plan and their execution, and for the details that set your business apart as unique too.

Begin that by asking questions, and more specifically and importantly by thinking through the key questions that you need to ask and find answers to. And the most important and salient of those questions will in all likelihood have at least a few essential qualities in common:

• Being able to more effectively answer them would better help you achieve and maintain competitive strength and business profitability in your enterprise. Answering them in a direct and specific manner would help you to more readily and cost-effectively fulfill your business plan and business model.
• What questions, as an at least minimally business success-defining set, would help you the most there?
• Now what types and volumes of data would you need to have in order to answer them, and with a degree of reliable accuracy and over a sufficient timeframe to meet your planning needs?
• And how would you best organize all of this data and categorize it by data type and other relevant criteria?

Let me take that out of the abstract with what by now would probably seem to be one of my favorite business analysis contexts: inventory selection and management in a retail business that for its business sector, would be required to have and maintain a large and complex range of product offerings (e.g. a supermarket, or even just a minimart.) And there, I add as a relevant aside that “large and complex” is relative, and a small business with correspondingly smaller cash flow and reserves, might quite legitimately find a size and complexity of overall inventory to be large, that an overall larger business might see as smaller and more constrained.

Inventory selection and management as a complex of business processes, is essentially entirely data driven:

• Begin with your immediate here and now. What does your business have in stock and either on the shelf or otherwise available in your shopping space, or as held in reserve in storage, waiting to be brought up to the storefront now?
• What is your overall inventory turnover rate, and what are the more particular turnover rates of what you offer within that whole and ideally on an individual SKU by SKU basis for real granularity and detail of understanding in your analysis?
• What is your shrinkage rate for that from expiration dates being reached before you can sell, or from breakage or theft – from any cause that would lead to you’re facing loss to your business from having purchased items to sell at a profit, that you cannot ultimately sell?
• Add in items that you might have to discount to sell here too, where loss might not be complete but where your goal there might now be more break-even than anything else. And specifically consider loss leaders here too: items that you might offer at discounted prices to bring people into your business, who you would expect to make profitable full price purchases too, but that you sell for marketing purposes at very reduced profit margins if any.
• And of course, what are the markups and profit margins that you can expect, or in the case of those loss leaders, the scope of the lack of direct profitability that you can expect from these items as your sell them, and both with and without consideration of value depleting, sale preventing factors such as shrinkage?
• And what are you assuming when you make the predictions that I have been indicating in these bullet points and how would you justify them? (I am not going to delve into details such as three scenario modeling for that here, with “best case”, “worst case” and normative expectation calculations as I do so in other places in this blog. I simply note that such analyses do take into account measures of the unknown and what-if scenarios.)

A business of the type under consideration here that would at least attempt this type of inventory review and analysis would also have to take overhead and payroll and insurance coverage and utility bills and more into account there too (e.g. marketing expenses.) So inventory per se might only represent part of this story and even for a store with a minimal physical space footprint such as an online store that seeks to employ just in time inventory management approaches to limit their cash investment tied up in their inventory as actually held at any given time. But even then, everything in such a business is still data-driven. Without essential data the owners and managers of such an enterprise cannot fully know where their business is now, or realistically begin to anticipate where they can take it as they seek to more effectively plan ahead.

• Much of this data would of necessity come from within the business, but it is increasingly important to bring in outside sourced data too, and both on how other competing businesses are doing in your business sector, and for your inventory item sources where changes there can impact upon your business too. And outside sourced market data would also be necessary.
• My point there is that as much data as a business develops internally and from their own systems and processes, they are likely going to need at least as much from outside of themselves too. And this all has to be coordinately organized and analyzed if effective and timely use is to be made of it.

I am going to continue exploring these issues in a next series installment where I will expand on those last two bullet points. Then after completing this phase of this narrative, and after offering some data mining and big data references to help flesh it out, I will turn this discussion around to consider both 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 I will do so in large part in terms of the cloud-based approach to this type of data storage and analysis that I have at least begun addressing in this series already. Then after completing that line of discussion, at least for purposes of this series, I will reconsider these issues but from a more business-development timeline perspective, bringing in the issues and challenges of cost-effectively developing a business for all of this and how and when, so as to bring in necessary change while controlling possible risk. That will, among other things, mean reconsidering outside funding and organic, strictly in-house sourced funding where capital development expenses would be faced.

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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