Platt Perspective on Business and Technology

Building a business for resilience 20 – open systems, closed systems and selectively porous ones 12

Posted in strategy and planning by Timothy Platt on April 14, 2017

This is my 20th installment to a series on building flexibility and resiliency into a business in its routine day-to-day decisions and follow-through, so it can more adaptively anticipate and respond to an ongoing low-level but with time, significant flow of change and its cumulative consequences, that every business faces in its normal course of operation (see Business Strategy and Operations – 3 and its Page 4 continuation, postings 542 and loosely following for Parts 1-19.)

I offered a multiple point to-address list of issues relevant to this series and its narrative in Part 19 and then set a preparatory foundation for delving into them in the balance of that posting. I repeat that list here for purposes of continuity of discussion and with a goal of starting my way through the list, beginning with its Point 1. And as I noted in Part 19, that means considering each of this set of issues fairly specifically in terms of change and its demands, and in developing resilient capabilities that would prepare for it where possible and more affectively responding to it where needed. This list of issues and topics, with clarifying updates added is:

1. Thinking through a business’ own proprietary information and all else that it has to keep secure that it holds.
2. And reducing avoidable friction where there are apparent trade-offs between work performance efficiency, and due diligence and risk remediation requirements. This, in anticipation of discussion to come, means consideration of both short-term and long-term value created and received, as well as short-term and long-term costs.
3. And this means thinking through the issues of who gathers and organizes what of this information flow, who accesses it and who uses it – and in ways that might explicitly go beyond their specific work tasks at hand.
4. What processes are this information legitimately used in, and who does that work? With the immediately preceding point in mind, what other, larger picture considerations have to be taken into account here too?
5. And who legitimately sees and uses the results of this information as it is processed and used and with what safeguards for the sensitive raw data and the sensitive processed knowledge that are involved, where different groups of people might have legitimate need to see different sets of this overall information pool?
6. Think in terms of business process cycles here, and of who does and does not enter into them.

And as just stated, I begin the basic narrative of this posting with its Point 1. And I begin doing so by posing a question. What is at least a likely strongest contender for being the most pressing and increasingly significant single issue, that businesses face in securing and managing sensitive and confidential information, and certainly when direct challenge from change in pertinent regulatory law as an outside consideration, and the challenges of personnel turnover and training are not considered? My answer to that and certainly as a change and resiliency consideration is scale. And that is in fact probably a best answer here, even if those two priority-competing factors are considered too.

• Business held data stores and of all types, grow. Big data, as I have for example discussed in my series: Big Data and the Assembly of Global Insight Out of Small Scale, Local and Micro-Local Data (as Reexamining the Fundamentals, Section IV), has become mainstreamed and fundamentally so, and data accumulation and storage, use and maintenance have become open ended for their scale.
• And this holds true even in contexts where businesses assiduously seek to identify and delete older data such as outdated personally identifiable customer information that they could be challenged for retaining long-term under personal privacy and other regulatory law in place, if found to be doing so (e.g. data coming from and related to former customers who will not be returning as such, and where data files have not been updated with new information for extensive periods of time because of that.)
• And this holds as being just as valid if a business just as carefully seeks to maintain current, error-free data bases and with the deletion of older content that has been proven error-ridden.
• And that holds true when a business has and actively seeks to enforce a policy of identifying and deleting personal copies and backups of business data content that individual employees might place on their work computers – or on their own personal computers, laptops, tablets or smart phones or in their own personal cloud storage space that they dual-use for personal and work-related purposes – which has become an increasingly impossible goal and certainly in that wider type of context. (For references and resources related to that, see my series: Navigating the Bring Your Own Tech Puzzle as can be found at HR and Personnel as postings 73 and following.)

Data and I add partly and more fully developed business knowledge that is based upon it, grow and they will continue to do so. And if that can and should be taken for granted as an accepted fact that businesses have to more effectively address, so is the challenge that this creates for their being able to manage all of this.

• New types of data are brought into the input streams that feed into these big data repositories.
• New types of users – direct hands-on raw data access users, and more distant end-users who require selected and processed information that those professionals develop and offer, keep proliferating too.
• And all of this data and its flow are increasingly being used in new ways, as well as in meeting more established needs and through more familiar processes. And here, I am only considering established and approved business intelligence usage and users, and without having to consider more ad hoc and novel extensions of those data management needs-creating requirements where access and usage exceptions would be made.
• So even absent any outside contextual change such as the possibility of regulatory oversight change per se, data systems grow and in complexity as well as in scale.
• And this growth and elaboration in overall systems complexity cannot be expected to follow any simple, linearly scalable pattern. And that is where the opening words to my above-repeated Point 1 become particularly important: “thinking through.”
• Simple linearly expanding growth in scale, can often be accommodated by simple linear scaling up of management systems that would govern it – at least until that breaks down from the complexity that a with-time, less than efficient approach can create, and a new and more organizationally efficient (and probably disruptively new) approach becomes needed.
• To take that out of the abstract, consider an accounts receivable system with five account managers who each hold responsibility for a distinctive defined area of a business and its major customers base, and who know their areas of responsibility very well. Now consider the impact of simply linearly scaling this system up, creating a situation where there are now fifty of these accounts managers and they are all working with the same number of major business-to-business customers – but they are all now working with seemingly disconnected pieces of the business and its major customer base as a whole, and where this maze of disconnects in what these hands-on accounts manager know, creates a tremendous new work load and challenge load for their supervising managers, and for their senior level manager too. This is just one example of how linear scalability, or rather attempts at it can create compelling demand for more fundamental and even disruptively novel change – where any alternative is too inefficient and problematical to endure as is.
• But to return to the more pressingly relevant issues that I have been focusing on here: when new and expanded have to be accommodated, of the types that I made note of in the first three bullet points of this list, simple linear growth and expansion can be expected to fail automatically, and without any seeming grace period of the sort that my above example might have suggested, when for example this business had only increased their number of accounts managers from five to ten – and their specific areas of responsibility were still wide enough so they could still see more of what their service was doing, and when they were few enough in numbers so they were more likely to still be more actively talking with each other and sharing a common understanding of what their service does.
• Further considering that example, five accounts managers get to really know each other and certainly when they are working together in the same office and area of it and they can and do talk shop and share perspectives and understandings. At fifty, it is likely that many will not even be able to identify all of their same work area peers by appearance – knowing their faces, let alone knowing their names and really knowing them as people. And now consider the possibility that this business works with customers across multiple time zones and that different groups of these accounts managers now work in completely different shifts, in different regionally located offices or both. Linear scalability breaks down and new becomes pressingly needed and even with the disruptions that disruptive innovation can be expected to create, at least short term.
• Where do extra challenges emerge out of this for managers and senior managers? As the number of direct hands-on account managers increases, new lower level supervising managers have to be brought in too and at the very least. And new types and levels of business process and supervisory support become necessary if all of these people are to follow a single same for all customers approach, and still more complexities have to be added if this business operates according to a regular and favored customer model. Consistency is important there too – and an over-stretched linearly scaled up system here will become less and less efficient as it outgrows its effective reach.

I have to add here that when I was offering this perspective on Point 1 and one of its defining issues, I was also at least beginning to address Point 2 as well, as the issues and the specific scenario example offered here were centered around information flow and business systems friction challenges. I am going to continue this discussion in a next series installment where I will very explicitly delve into that side to this overall challenge. And in anticipation of that, I note that not all friction in a business is created equally – and even baring consideration of need to sequester confidential or proprietary information per se, on a realistic need to know basis.

• Business systems friction has its trade-offs, where locally increased friction can reasonably be considered acceptable and even as a preferred cost – as a means of reducing overall, system-wide friction and risk that might become more open ended in nature.

I am going to address Point 2, from the above repeated to-address list, at least in part in differential cost/benefits terms of this type. Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 4, and also at Page 1, Page 2 and Page 3 of that directory.

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