Platt Perspective on Business and Technology

Building a startup for what you want it to become 27: moving past the initial startup phase 13

Posted in startups by Timothy Platt on October 19, 2017

This is my 27th 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 1-26.)

I wrote in largely abstract terms in Part 26 about data and data analysis in a business, as a foundation for developing and pursuing a consistent, effective strategic and operational approach, only briefly citing and selectively developing a quick sketch of a retail store case study for clarification there. And more specifically for that, I cited a still young and still small retail store with a complex inventory, as for example would be found in a grocery or hardware store with its potentially complex sales and inventory-oriented data collection and analyses.

I then stated at the end of that installment that I would at least attempt to bring its discussion into clearer and more actionable focus here, by addressing (by way of working example) how a business might add greater rigor and structure into its systems, to facilitate its orderly development and growth as it moves forward.

• Everything, or at least seemingly everything in a business might start out looking ad hoc and new and novel at first, and certainly for a business such as a startup, and certainly if its founders are new to actually building and running a business venture of their own.
• When and how should organized system and structure first begin to be developed and added to this mix, with its at least up-front additional costs and complexities and certainly for a business owner who has never had to manage inventory, to further pursue this working example, in a structured data-driven manner?
• When and how should that more rigorous, data driven approach to running the business take over and become the basic rule for how things are done there?

I tend write about organized and systematically structured systems, and about the ad hoc approach as an often problematical alternative. My goal for this installment to this series is to at least begin addressing how that circumstance arises. And I begin doing so by directly challenging a statement that I offered in Part 26, in a new and small business context, with the limited financial, personnel and other resources available to it:

• “… And costs and timing demands would constrain them to at most, doing only simple and even cursory data analyses and business performance modeling … and with that largely based on aggregate analysis of product categories and not of individual item types. That definitely holds for smaller and even for medium sized businesses. Massive retail business systems, tend to be the ones that truly dive into the data, and with all of the effort and expenses involved as mistakes or lost opportunities take on scales of impact, financially, that they cannot leave to chance.”

There is still, as of this writing, a significant element of truth to that presumption and certainly for startups and other more cash-strapped businesses. But the advent of big data and of secure outsourceable data storage and analysis in the cloud, as a disruptive source of business practice change, is altering the balance of when complex data collection and analysis becomes cost-effective and even an essential driver of business success. I write this series, at a transition point in the history of how businesses can and do effectively operate. And in a few years every business will have to be effectively data driven and big data driven if it is to competitively succeed, and certainly in the face of an increasingly globalized bricks and mortar plus online context.

With that stated, I offer my case study example for this posting, as promised above: a still small and young retail business that fits the complex inventory criterion of the grocery or hardware stores already cited here, but that as a still early stage business, seeks to develop along a post-transition approach for its data collection, analysis and use, and in planning and carrying out its here-and-now and its next-step development. And to make this more interesting, I will focus on a boutique business that would offer a fairly wide and diverse range of home goods and related items, many of which would appeal to people looking for gifts and well as for making purchases for themselves and their own families. That means this store, which I will refer to as HomeWorks Fashion, has to maintain a much more fluid inventory than a grocery or hardware store would, with their much more standardized basic, core inventories of long-established products.

• A retail business such as HomeWorks Fashion carries a larger percentage of its goods for sale as seasonal and otherwise cyclically sellable stock, than a grocery or hardware store would, and both for their selection of distinct stock keeping units (SKUs) (or range and diversity of distinct types of item carried and sold) and for overall volume of items carried and sold (where numbers of each of those distinct item types are considered too, where that represents the proportion of overall shelf space devoted to seasonal and related items.)
• And at the same time, a store such as HomeWorks Fashion, would be expected to carry a much larger percentage of fad and other short-term readily marketed and sold products than any grocery or hardware store would.
• All of this makes detailed analysis of sales performance on an item-type by item-type basis both more complex and more necessary, than would be the case where a perhaps very large percentage of all SKUs sold, are year-around, steady sellers (e.g. such as eggs, milk and bread in a supermarket.)

This is where capability for outsourcing access to the more expensive to buy and maintain infrastructure for detailed data analysis, through use of cloud based resources, begins to really make sense and both for data collection and storage, and for data analysis using cloud based software as a service resources for carrying out the necessary statistical and related tasks. Effectively organized third party systems of this type, I add, also offer a great deal of help in both determining what types of statistical and related tests should best be performed to address what types of business analysis and planning questions, and how much data and of what types would be needed for those tests to be able to yield actionable solutions. A number of large online businesses, such as Google have in fact actively developed this type of service and supporting resource base for it, as a to-them, marketable product in a business-to-business marketplace. And they definitely target medium sized and small businesses as potential clients for these services.

I am concurrently writing another series in this blog: Career Planning, in which I explore and discuss disruptive change in the workplace as it is reshaping jobs and employability (see Guide to Effective Job Search and Career Development – 3, postings 459 and following for its installments.) The emerging disruptive changes that I write of there, all impact upon what it means to work and to be employable. But they also, and just as significantly impact upon the businesses that those people currently work in or at least potentially might work in too, and generally in ways as implemented in them, that in aggregate benefit those businesses in significant some way. That, in many cases, is why these changes are brought in and intentionally so, and certainly when a business as a real choice there. Big data could very reasonably be added to the list of examples cited and discussed there too, and as a rapidly and impactfully emerging disruptive change to what businesses can do and cost-effectively, that would even dramatically change how they do business if they are to remain competitive in the face of other businesses in their industries that might embrace this change more effectively.

I am going to continue this example in a next series installment where I will consider 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 here. 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 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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