Big data 10: redefining the group demographic 3

This is my tenth installment in a series on an emerging capability that has become surrounded by hype, even as it has emerged as a powerfully disruptive societal force: big data (see Ubiquitous Computing and Communications – everywhere all the time, postings 177 and following for Parts 1-7 and its continuation page, posting 207 and 209 for Part 8-9.)

I have been writing about the collection, organization, accumulation and sharing of data, personally identifiable consumer data included, in this series. And in Part 9 I turned to consider the role and activities of governmental Big Brothers in this. Then towards the end of that posting I brought up the issues of corporate Little Brothers, and corporate big data accumulation and use. And I noted that at least collectively, these Little Brothers are coming to be both more pervasive and more impactful than any of their perhaps more visible Big Brother counterparts. And while Big Brother does this for survival, Little Brother does this for profit.

I stated that I would look into the issues of Little Brother in this series installment, “focusing on concerns that have continuously seemed to have arisen regarding Facebook and its development and use of big data.” And I continue that narrative here with a set of crucial observations:

• Facebook offers incredible resources for individuals and groups – for all of us as we develop and connect into communities, and as we connect and organize to achieve goals. This is all very positive.
• Facebook also accumulates vast amounts of user submitted data, and with increasing capability for scanning and searching and tagging more and more types of data, more and more of what is added to the Facebook databases can be searched and individually identified and bundled into progressively more comprehensive individual profiles.
• The best working example as of this writing, for how Facebook is leveraging new technologies to expand its searchable and functionally usable database resources, comes with its very active program for scanning and individually identification-tagging people depicted in photos uploaded to their site.
• That and essentially everything else that they do with the personal data uploaded to their site became a problem when Facebook as a business decided to monetize and commoditize this bonanza of personally identifiable data, and when they started to change their privacy policies to better meet their business model needs – in too many cases doing so with opt-out options only, for their member users.
• So people submitted information into their user profiles, for example, under one set of usage and reuse terms and conditions. Facebook loosened the restrictions they agreed to honor, allowing them to use and to sell more of this information and in new ways and to a wider marketplace. And unless a user/member went into their profile and navigated their way through the menus there to the right screens and clicked the right opt-out choices, Facebook saw itself as authorized to proceed in selling access to their data.
• So I cited Facebook in Part 9 as a rapidly emerging poster child as to how to do this badly and wrong. Facebook, as of this writing, has on the order of a billion member profiles in their system and there are people everywhere, globally who know how that social networking site and business has repeatedly shot itself in the foot for this, to put their business practice decisions most charitably. But there are others and some are much more worrisome than Facebook could ever be who also play Little Brother in this. Many work essentially behind the scenes and in ways a public facing company such as Facebook never could. Many impact widely across hundreds of millions of lives.

My goal for this posting is to at least briefly touch upon a few of those Little Brothers that for the scope of their impact, are not actually so little at all. And I begin with credit reporting agencies.

• Experian, Equifax, TransUnion and similar credit reporting agencies accumulate vast amounts of information on essentially everyone, as to our spending behavior and any debts that we might carry and of any sort, our history for paying off those debts, our income and savings and investments, and our financial situations and behavior in general.
• And they run all of this raw data through statistical models to predict our future financial behavior and our capacity to pay off future debt obligations, assigning overall credit scores as summary overall ratings to the profiles that they assemble on us.
• Every time we seek to make a major purchase such as buy a car, every time we seek to sign a lease or buy a home, or when we seek to acquire a new credit card, the businesses we would carry through on these transactions with all go to at least one of the major credit agencies such as the three named above, to check our creditworthiness.
• It is important to note that while this can serve as a gatekeeper in deciding whether, for example, we can get a loan at all to buy a new car, this also sets the interest rates we would be required to pay on that car loan if we can get it. This, quite arguably is justified. A buyer with a lower credit score and shakier repayment history can credibly be considered as representing a greater risk to a lender than one with a significantly higher score, simply assuming that credit scores are set on sufficient amounts of sufficiently accurate data and that the models used to categorize individuals under review are well designed and empirically validated.
• But credit reports are also used for other purposes as well. As an arguably justifiable example, individuals can get copies of their credit reports to monitor what is in them, and both to know where they stand from their own financial behavior and history, and to spot if they have become a victim of identity theft. That is a reactive and after-the-fact way of finding out, but for most of us this can be a crucial tool for protecting ourselves from further harm.
• On a more negative side, a lot of hiring companies have started using credit report findings to eliminate job candidates from consideration – in spite of the fact that empirical evidence shows this not to be a valid metric of value for the purpose of finding a good let alone a best job candidate. Credit report scores tend to go down for people out of work and the longer they are, the more their finances and their credit scores can suffer. So as far as hiring is concerned, the credit score primarily just shows that a given candidate has been out of work, and perhaps through no fault of their own and that they really need this job. And that use of credit scores has become an increasingly important revenue stream for these credit reporting agencies, and that is one of the reasons why I write of them here as Little Brother examples.

Facebook is very public and very directly connected with vast numbers of members of the community. And they have found ways to both gather and organize and to monetize and sell information about essentially everyone in their system, and as both anonymized demographic data and as personally identifiable and even profile-organized information about us. The big credit reporting agencies are also well known to the public – at least for their major lines of business. But their resources are packaged and sold for other less well known purposes too, and some of them are problematical and even directly contrary to our needs and both as individuals and as members of communities. Then there are the much less known companies that accumulate, organize and sell marketing intelligence, and both anonymized and individualized and in many cases for essentially any end-use purpose. Business to business, and more generally business to organization Little Brother accumulators and sellers of big data, are becoming a major driving force in reshaping both marketplaces and the businesses that serve them – and every group or organization that is trying to publically push an agenda.

When I wrote Big Data 7 I invoked the extremes of utopias and dystopias, and in the course of this series I have at least pointed towards both. But most of what I have been writing about fits more into the vast gray area in between. In Part 9 and this installment I have touched on both extremes while noting the Big and Little Brothers of our world, and their developing impact upon us all. I am going to continue this discussion in my next series installment, beginning with a basic question that comes out of this series and its installments up to here as a whole:

• The first ten installments of this Big Data series have delved at least briefly and selectively into where we are now. Where are we going, and what can we as individuals and as businesses, and there perhaps particularly as small businesses, do to more effectively succeed in the midst of all of this?

Meanwhile, you can find this and related postings at Ubiquitous Computing and Communications – everywhere all the time and its continuation page.

Opening up the online business model for new and emerging opportunity 4: blue ocean strategies and business models 2

This is my fourth installment to a series on new and emerging online business models and on developing best practices for finding and creating new and novel online business opportunity (see Startups and Early Stage Businesses, postings 142 and loosely following for Parts 1-3.)

I finished Part 3 of this series by beginning a discussion of a potential paradox, noting that:

• The businesses most able to support innovation financially, can be the least able to do so strategically and organizationally, and those most able to do so strategically and organizationally can be least able to do so from a firmly supportive financial base.

And I stated that the strategic and operational goal of any business that seeks to innovate should be to pursue, develop and implement approaches for combining sources of necessary strength so “able” and “willing” can come together in support of innovative excellence. I said that I would at least begin functionally unraveling this challenge here in this posting.

To round out the background context for this posting, when I wrote Part 2 of this series, I raised an open question that I simply noted there, stating that I would come back to it later:

• And what should be an innovative business’ perhaps more conservative Plan B, and when should a striving for breakout success and the blue ocean strategy and development that would lead to it be the Plan B?

My goal for this posting is in fact to address both of these sets of points:

• The first of those can be seen as a matter of finding a productive, risk management-aware balance between maintaining business stability and capability on the one hand while opening up that business to the possibility of breakaway, innovative success with its increased risks on the other.
• The second is one of knowing which of these two basic approaches: stable and fiscally centered, or innovatively risk taking should predominate and lead in defining and setting the business’ overall strategy and priorities, and which should follow.

I approach those decision points from a variety of approaches when making an assessment, and will at least briefly note some of those possibilities here. I will start, however, by noting that no such assessment no matter how grounded can safely be considered as if set in stone and good and valid for all time – and no matter how cogently valid when initially arrived at. So I am writing about an ongoing process. And in that, both the results reached and the approaches taken in making an analysis of this type can be context and business development stage-dependent.

With that in mind, I will begin considering a startup or early stage business that is still developing itself and still finding its way into a prospective marketplace.

• A point that I have made several times in this blog, but that bears repeating here is that it does not make sense to build a startup if your goal is to be 37th best: the 37th most competitively effective and profitable in a field of 40. The numbers vary as I state that point, but the basic idea remains the same. If you are going to go to the expense, effort and risk of building a new business, you should be looking for and developing toward a realistic capability of being among the best in your market, and certainly for your prospective customer base.
• That might mean staking out a narrow and specialized market niche that you can excel in or it might mean building a more general product and service offering business. But in either case, if you are to build to be the best, that is probably going to mean building to be different, and to offer something unique and uniquely valuable to the customer.
• This point is particularly true if you seek to build your new business venture into an already crowded field. It is obviously less important if you face less direct competition. But for a startup or early stage business, capacity to offer new and different, and innovatively valuable to a marketplace and its customers can be the defining distinction between being successful and simply surviving – or not succeeding at all.
• Now consider building a new business in an at least somewhat competitive field and environment – which I posit as a basic, predictably standard new business context. You are going to be building in the face of competition with more established businesses that already have positive cash flow and at least some profitability, and that in many cases will have at least some reserves to cushion any risks they take in business evolution and development.
• So you need to build securely and soundly (e.g. see for example, my series: Understanding and Navigating Burn Rate at Startups and Early Stage Businesses, postings 67-78.) That represents the sound and stable business strategy and development track. But at the same time, much of what you do has to be driven by an ongoing effort to define, create, develop, monetize and profitably offer a unique value proposition and that represents the more risk accepting innovative track.
• The more competitive the field you would build your venture into, the more risk accepting you are likely going to have to be, and the more towards the front this innovation driven business development track is going to have to be.

Now let’s flip that around and consider the same set of operational and strategic dynamics from the perspective of the established business. I am going to continue this discussion, picking up on it there in my next series installment, where I will also explicitly compare and contrast these strategic and operational contexts and how they would be addressed. Meanwhile, you can find this and other related postings at Startups and Early Stage Businesses. You can also find related material at Business Strategy and Operations and at its continuation page: Business Strategy and Operations – 2.

Some thought concerning a rapidly emerging internet of things 1: starting a new series

I have been writing on an ongoing basis in this blog about what could be called the internet of people, and about using the tools and resources, and the connectivity reach of cyber space for achieving meaningful goals through it. A second separate, parallel if connected network has also begun to take shape as an internet of things.

• And if the number of people – the number of connectable nodes involved in the former is limited as an absolute maximum to the few billion who populate this planet,
• The number of devices and objects – the number of nodes achievable in the later can realistically be expected to expand well into the trillions.

I have at least briefly touched upon the emerging existence of an internet of things a few times in the course of writing this blog, but the first time I explicitly wrote about it was quite recently with a 16th installment to a series on information systems security: Information systems security and the ongoing consequences of always being reactive – 16: the internet of things and the emergence of next generation DDoS attacks.

• I began discussing this emerging reality there and in that context, as it is when a complex and comprehensive system is first being planned out and implemented, that basic systems security and risk management capabilities have to be built into it,
• If basic risk management and related considerations are not built in from the beginning, all subsequent efforts will have to keep addressing them ad hoc and forever.
• I cited in that posting, as a still unfolding historical example, how gaps in basic source identity validation that were overlooked in the early pre-public internet: the original ARPANET still haunt us as root vulnerability causes of much of the malware and black hat hacker activity that we still see today.

So I first formally began addressing the issues of an emerging internet of things, in terms of a need to build this right, and from a solid and well thought out and implemented foundation that is as flexibly and robustly secure from tampering as possible. And at the end of that information systems security posting I said that I would follow it with a series on the internet of things per se, and on what it is and on what it is headed towards becoming. I begin that here.

I defined some key terms in my security series posting that I will be referring to in this series, and begin here by noting two of them (though I strongly recommend reading that posting too.) I divided the emerging internet of things into two basic and fundamentally distinct spheres of activity:

• The Internet 1.0 of Things where more and more items and objects are tagged and in ways that can be connected into the internet and tracked through it. These objects – these nodes in this system are passively connected in so this can also be thought of as the passive internet of things.
• The Internet 2.0 of Things where more and more nodes and types of node are added that do communicatively, 2-directionally interact with the internet and with other nodes, and more actively and even proactively than would be possible with simple ID tagging or other 1.0 activity. This can be thought of as the active internet of things.

I cited barcode tagged and RFID tagged objects as the primary (as of now) sources of passive nodes in this overall system. Active nodes are already much more diverse than that, and even just in the still embryonic stage of development that we are in for this internet of things. I cited as one source of such examples, smart appliances as would be found in an emerging 21st century kitchen. I add here an entirely separate area of examples that, referring back to that security systems posting for its topic area, will become all but ubiquitous and that will become crucially important and even nationally from a security perspective: Supervisory Control and Data Acquisition (SCADA) systems.

I actually did raise the possibilities there of small, home-SCADA systems for managing the emerging household of tomorrow, but large scale industrial and core infrastructure SCADA systems are going to become progressively more fine-grained networks of active 2.0 nodes in a globally interconnected network of things too.

I am going to start delving into some of the details in all of this in my next series installment, where I will focus on the passive, internet 1.0 of things and how a myriad of objects and items are being connected in, with the implications that this creates. Meanwhile, you can find this and related postings at Ubiquitous Computing and Communications – everywhere all the time and its continuation page, and at Social Networking and Business.

Accommodating and thriving in the midst of change in jobs and careers 5 – facing the challenge of long-term and chronic unemployment

This is my fifth posting in a series on change and even disruptive change as it can reshape our work lives and our careers (see my Guide to Effective Job Search and Career Development – 2, postings 306-309 for Parts 1-4.) And I have been writing this series as a direct successor to my immediately preceding series: Career Changes, Career Transitions (same directory, postings 285-305.)

When you count both my main directory listings and my supplemental postings, I have added just over 350 short essay installments to my Guide and its Part 2 continuation page. And over the course of that I have at least tried to offer a fairly wide range of resources that can be used for job search and career development, and throughout their various stages. And what I offer here is all distilled from my own experience and from that of people I have worked with – I always seek to focus on the empirically validated in what I write to this blog. But with that said, I freely admit here, that there are circumstances where none of what I do or could offer would work. And I want to start this posting by briefly sharing a selectively abridged story of a friend and associate I have tried to work with and help. I will call her H and simply state that she is a younger graduate of one of my alma maters who I first met at an alumni event. I was working as a consultant at the time and enjoying a weekend day off; she was trying to network for job opportunities and leads.

I am not going to delve into any of the details as to how this might have happened, but H graduated with her Bachelor’s degree in what should be a directly practical field, businesses were actively hiring in her field even if not doing so at a maximal job candidate-favoring rate, and she was unable to get that first, foot in the door job in her desired field. When I first met her, she had been out of school for a number of years but she had still never landed that first full time position in her field that she had trained for throughout her Bachelor’s degree program.

I did try helping her to network more effectively, and I did share some specific leads with her to people who I knew and had worked with. And I tried helping her to more effectively write a resume and cover letter. Nothing worked and I do add that she vocally pushed back against and rejected any real change from what she was doing and how she was doing that. I am only occasionally in contact with her now, but to the best of my knowledge she has still never found that dream job – a full time job in her field of study. And considering how long she has been out of college and how dated her degree is, when she is competing against recent graduates and with her employment record she really is out of the running now. And she has been in need for a long time now, of a real and genuine Plan B change of direction – which she has never and probably will never fully acknowledge.

This all began before I wrote my first posting of any sort to this blog, so I have been aware of it and of her and her circumstances throughout all of it. So I have always written here with an awareness and understanding that even the most carefully planned and the most empirically validated and refined tools and approaches cannot help or even work all the time and for everyone.

On one hand, I cannot help but think of my efforts to help H to break out of her pattern as representing a real failure on my part. But I also know that I cannot take ownership of H’s decisions or actions and I should not try to. Some of the most important approaches and options that I tried to get her to at least consider trying, simply provoked anger on her part, driven by frustration. Ultimately, she has been the one who has made the same decisions and repeated the same actions in pursuit of them, in hopes of seeing new and different outcomes from them.

So I turn in this posting and in this series to consider chronic and long-term unemployment. I have addressed a few perhaps specialized aspects of that before, and in that regard I specifically site postings I have added here on addressing resume gaps (see for example, Unemployment Gaps and Related Resume Problems and its Part 2 continuation.) I wrote those postings with two very specific audiences in mind – people I knew who had simply been out of work for periods of time and who were looking to get back in, and a couple of people I knew who had become caught up in financial industry improprieties and illegalities and who had spent time in prison as a result. And for them coming out and getting back into the work world, meant that any door back to anything like what they had been doing was in most cases closed to them and by court order. They were barred from certain lines of work when reentering the workforce, and for life. I worked with them as friends, in finding new direction Plan B alternatives.

For H, her Plan A did not and with time it ceased to even be a realistic possibility. But she has never really considered any Plan B – and even as any conceivable doorway to achieving her Plan A has closed and disappeared for her.

There are a lot of reasons why doors can close and disappear. Sometimes a desired Plan A work and career goal isn’t a good or even a realistic one. Sometimes that Plan A is good at first but becomes a dead end and disappears out from under us. And the loss of a job and the ending of a Plan A career path does not necessarily have anything to do with fault or blame or failure on our part, and certainly when that happens as a result of changing workplace circumstances that do not in any way involve or reflect our effectiveness or value in the workplace. But regardless of cause, when we face the prospect of being chronically or long-term unemployed, or find that we have slipped into that circumstance, it is up to us to find and take action to get out of that trap.

This is my last posting to this series and I end it by stating that we own our careers and our lives and that this is a good thing. But it means we have to be willing and able to step out of our comfort zone at times, and not just in terms of what we would do professionally – in terms of how we view ourselves and think of what we can do and are willing to do as well. We have to be willing to set aside our pride at times and particularly when circumstances render it more hubris. And I add that most definitely applied to the failed financers I cited above – though time in prison had pretty well accomplished that for them. But mostly, Plan A, Plan B and further work life and career planning iterations are all about open eyed and open minded resiliency. And that is perhaps particularly true at a time such as we are all currently facing, with its rapid change and uncertainties.

I am finishing this series here and on that note. I will be starting a next series in this Guide in a few days, with that on “Offering a Unique Value Proposition as an Employee.” Meanwhile, you can find this and related postings and series at my Guide to Effective Job Search and Career Development – 2 and at my first Guide directory page on Job Search and Career Development.

Information systems security and the ongoing consequences of always being reactive – 17: incentivizing more secure software and information systems

This is my seventeenth installment to a series on the state of information systems security going into the second decade of the 21st century, and on challenges that will have to be addressed in moving forward from where we are now (see Ubiquitous Computing and Communications – everywhere all the time, postings 185-188 for Parts 1-4 and its continuation page, postings 189 and loosely following for Parts 5-16.)

I have been writing about reactive and proactive approaches to information and cyber security in this series, and about integrated and multi-level approaches to making these systems more agile and effective. And I have been writing here about approaches for making this work. But even where the approaches I suggest and others would offer real value, they cannot work if they are never tried and applied. Doing so would require incentivizing change, and I add removing some disincentives too. My goal for this posting is to at least begin a discussion of that, and I want to begin with the disincentives side of this set of issues, which in this case revolves around antitrust laws on the one hand, and the pressures of a highly competitive industry to thwart collaboration even when allowed on the other.

Antitrust laws, also called competition laws are formulated and enforced to prevent collaborative agreements between competitors that would artificially restrain trade and control consumer prices and access to marketplace choice. In principle at least any collaborative relationship or understanding between businesses in for example, the antivirus software arena, could at least potentially be seen as fitting that outlawed pattern. But it is recognized that if these computer and information security resources are to work at all and provide any benefit, they have to be based upon and updated according to the best and fullest of what is known about threats faced by all. And certain best practices frameworks of understanding are going to have to be collectively shared across the industry, as well as insight into the rapidly evolving nature of those threats collectively faced. So certain types of industry-wide organizations are allowed for and even encouraged – provided they meet certain basic requirements of openness and participatory inclusiveness. One that comes immediately to mind for me is The Open Web Application Security Project (OWASP).

But a shift from a more strictly reactive approach to information security to a more proactive paradigm of information and computer systems security, is going to call for new types of collaboration. The implications and requirements of this shift are going to have to be incorporated into the legal frameworks that limit and permit collaboration between competing for-profit businesses in these arenas. And as already noted in several contexts in this blog, the law is always reactive and when a field is rapidly changing, it can be significantly behind the curve and disconnected from addressing actual current needs and circumstances. It is a hallmark of information and cyber security that their challenges and priorities and the solutions they have to provide in response, change faster than any regulatory law ever could.

• So law regulating competition in this specific arena has to be open as far as specific technology or processes are concerned, and focus entirely on openness and inclusion of participation in any umbrella organizations involved, and on their transparency.
• But even if the law was perfectly in tune with industry and marketplace needs for this, competitive pressures in this marketplace all too often put a greater premium on pushing new products and services out the door, and less on providing the most robust possible security and risk management solutions. The pace and force of competition here effectively compels that.

I turn to address that set of challenges with a very specific working example from a very different industry in mind, that while different in detail might offer insight of value here too: organic food and more specifically, California’s legally defined standards as to what can be called organic food.

Words like natural, healthy and organic convey powerful messages when marketing food items, and credible claims that a food product offered is organic or that it is made entirely from organic ingredients, to pick up on that key word from that, increases its sales and profitability. So as a result, some businesses began using the word organic very loosely. Consumers in the state of California spoke up in response and got their legislators involved, and as a result California passed what at that time was the strictest set of guidelines in United States law as to what can be identified as being organic. This was seen as a truth in advertising and a consumer protection initiative, and this law: California’s Organic Foods Production Act of 1990 became the gold standard for regulating this area of the food industry. And that is where this story connects with the narratives of this posting and this series.

California is among other things one of the major producers of fruits, vegetables and other produce in the United States, so when growers there were restricted to only using the word organic when strict standards were adhered to, that had national and even international impact California produce is sold very widely. But perhaps more importantly for this discussion, California’s population is very large and in fact constitutes a significant market segment for essentially any wide sales distribution processed food manufactured essentially anywhere in the entire country. So when California law imposed very specific and precise accuracy requirements for calling a food organic, businesses that produced foods in other states and even in other countries noticed. The potential of losing this part of their market share forced a lot of businesses to rethink their ingredients and their production processes if they were to continue to use that word – remember here that if they had suddenly just taken “organic” off their labels that would have sent a clear message that they had been lying and that their foods were not as pure or as good as they had been claiming.

My point is that when a sufficiently large market segment or share of a customer base suddenly demands that some new standard be met and in specific ways, that puts real pressure on all prospective providers to meet those new standards, and for all of their intended customer base. Now consider how this applies to antivirus and related anti-malware software.

• If even just a few key, high purchase volume state governments and a few major corporations were to suddenly demand that a new collectively agreed to higher standard be met for information and computer systems security, as a threshold requirement before any of them would consider purchasing a given product or service, every major information and computer security provider would in effect be forced to meet that new standard and for all of their customers, everywhere.
• To clarify that last point here, they would need to meet those new standards for this market segment to keep its business. And they couldn’t very well tell the rest of the world “we sell software and other products that really works to our larger and more demanding customers …and we sell our old design-paradigm stuff to everyone else.”
• Other customers would, of course, begin demanding that this new standard be met for them too.
• An organized consumer base that only collectively included a significant minority of the overall market could begin this process and in effect force this industry to meet higher standards for all.

Antitrust and competition laws limit and control producer collaborations and the prospect of producer collusion and marketplace manipulation. They do not address or seek to address consumer-side collaboration or the development of consumer-side standards that would have to be met by any successful vendor or provider. A “consumer-side trade group” could in effect force all significant participants on the producer and seller side of this to uniformly meet and adhere to newly defined minimal standards and even to new types of standards.

• If governmental and other major purchasers required that the software and systems they acquire be secure, and according to a specific robust standard as to what that means, that would incentivize all software manufacturers and other IT systems, products and services providers to build and maintain to that higher standard.

You can find this and related information security-related postings at Ubiquitous Computing and Communications – everywhere all the time and its continuation page, and at Social Networking and Business.

Innovators, innovation teams and the innovation process 4 – identifying, developing and supporting the individual innovator 3

This is my fourth installment in a series on innovators and the process of innovation (see HR and Personnel, postings 154-156 for Parts 1-3.)

So far in this series, I have been discussing how innovators would be identified and worked with. And I have at least touched upon how important it is to select, train and manage and cultivate managers who work with creative and innovative employees too, to make this work (see particularly Part 3 in that regard.) But in doing so, I in effect set innovators apart and in a way that can be misleading.

• Everyone in your organization, and at every level and position in it holds within themselves a creative and innovative potential. So while I have been writing in this series up to here about innovative employees, I point out here that this potentially includes any or all of the people in your business.
• Here, the goal is to be open and supportive of your business’ community of members for when they have their great ideas.

A hands-on employee who works on a specific set of tasks in your business and who sees what is and is not working effectively there, is likely in the best position to see where improvements would be beneficial, and they might be in a best position to see at least in outline how those improvements might be framed. To take this out of the abstract, a janitor who cleans up at the end of the day after work might very well be in the best position to see which doors are locked or left unlocked – and even when they are supposed to be locked. That janitor might be the employee who comes up with a better procedural approach for non-confrontationally encouraging people to leave doors unlocked when they should do that for easier maintenance access, but locked when that should be the higher priority.

I cite this type of example for several reasons:

• Janitors go everywhere in a business and at least potentially see a lot in every one of those places. But they are generally not considered as possible sources of insight or best practices solutions.
• In this case a janitor is – and to make their insight work and to develop that new best business practice approach out of it, they would have to be able to work collaboratively with others – Security comes to mind here, but any such solution might very well require buy-in from others as well. And they would need managerial support in order to do that, or even to be listened to in the first place.
• And this brings me back to Part 3 and what I wrote about innovators and managers there. To take my points in that series installment out of the abstract, don’t just think in terms of how a manager would work with “the inventor/innovator” on their team, but rather about how they would work with any team member who has a great idea – but who comes up with this from their standard day-to-day work and while carrying out basic support activities that are not thought of as being sources of innovative insight.
• That is precisely what “disruptive” means in disruptively innovative – arriving unexpectedly and from completely unexpected directions to offer new and novel sources of value.

And this might mean coming from a consistently creative and innovative employee who is in fact explicitly an inventor/innovator on the team and it might come from a member of an explicitly organized inventor/innovator team, or it might come from a more routine-function employee who has a flash of insight.

• It is the hallmark of an innovative organization that the potential for this type of insight and innovation is supported wherever it comes from, and employees are both listened to and included when they seek to offer innovative potential and its fruits.

I am going to continue this discussion in my next series installment, there turning to consider the organization and its policies and practices, and its corporate culture. And at least one of the working examples that I will cite is provided by Google. Meanwhile, you can find this and related postings at HR and Personnel and also at Business Strategy and Operations and at its continuation page: Business Strategy and Operations – 2.

Acquisitions and divestitures 9: the value added acquisitions and divestitures business model

This is my ninth installment in a series in which I look at acquisitions and divestitures and related processes, and examine businesses from a very modular prospective as to how value is created and sustained (see Business Strategy and Operations – 2, postings 358 and following for Parts 1-8.)

Early in this series I conceptually and operationally divided businesses built around acquisitions and divestitures trading, as fitting either of two fundamental business model patterns: the chop shop model and the value added model (see Part 8: the chop shop acquisitions and divestitures business model where I define both terms, and where I at least begin a discussion of the chop shop model and how it is most certainly seen as the public face for acquisitions and divestitures now, at least as of this writing.

I stated at the end of Part 8 that I would continue that discussion here, this time delving into at least the primary features of value added model businesses. And I do so here, by picking up on a scenario that I briefly touched upon in Part 8 where the acquisitions and divestitures per se consist of salvageable resources from a failing business that could not successfully recover through change management approaches and remediation.

• Admittedly oversimplifying here, a chop shop model business that acquires a failing company for saleable parts is not likely to attempt to turn it around to see if it can in fact be salvaged as an overall business concern.
• Premium would be placed on packaging, marketing and selling off anything of marketplace value and as quickly as possible, and at as little direct expense or risk (indirect expense) as possible to the acquisitions and divestitures business that sets up and manages – and profits from those transactions.
• A value added model business would calculate risk and benefits determination from a wider perspective and along a longer timeframe. If they could save the business and turn it around to be profitable again, the value receivable there might not be as high on a short-term basis as what could be realized from selling off the parts as scrap, but longer term value would in many cases be higher, and over time even considerably higher.
• Here, a salvaged business acquired by such a management and development oriented company, sees at least a potential worth considering of developing long term, new revenue streams. And a recovered business could always be spun off and sold and for a return on investment there too, if it did not fit into that acquisitions and divestitures portfolio of held resources or fit into its long term strategic plans or priorities.
• The basic approach is fundamentally different, with the value added business selling quickly if necessary, but also pursuing longer term investment strategies in what it acquires. And in this, the words “value added” become centrally important. The more an investment acquisition can be increased in marketable value and the more competitive a market can be developed for businesses and entrepreneurs who might want to acquire it, the higher the price point it can be marketed to and the more it can be successfully sold for.
• Here, the calculus of risk and benefits in play balances costs for preparing an acquisition to be profitably marketable and to some likely specific selling price range, against returns actually receivable after making that investment. And the goal is to develop a divestiture offering so as to realize the greatest possible profitable return on investment from it, and under circumstances where that greatest return on investment or at least a return close to it is most likely to be achieved.
• This can best be done by developing an acquisition to show significant value and even defining and distinguishing value for any business that acquires if from the value added model business. That is where competitive interest can be developed when divesting this repackaged and perhaps reorganized business asset, and that is where the greatest return on investment can be achieved.
• The chop shop business is quick to take out up-front service and related fees from any liquid assets available in an acquisition they take on. They in effect gut the business of its immediately available liquidity and then walk after extracting any other removable value. The value added model business seeks to increase value in what they acquire then sell off, creating new value for everyone involved – as that makes their transaction processes sustainable and supports long term gains.

It is easier to find acquisitions and divestitures businesses that run closer to the chop shop model as most businesses in general think and act short-term. Whatever their basic business models, more should think, plan and act with more of a long-term awareness.

I am going to finish this series here with this posting. Meanwhile, you can find this and related postings at Business Strategy and Operations and at its continuation page: Business Strategy and Operations – 2. I have also included this series installment in Startups and Early Stage Businesses.

Big data 9: redefining the group demographic 2

This is my ninth installment in a series on an emerging capability that has become surrounded by hype, even as it has emerged as a powerfully disruptive societal force: big data (see Ubiquitous Computing and Communications – everywhere all the time, postings 177 and following for Parts 1-7 and its continuation page, posting 207 for Part 8.)

I began a discussion of how big data serves to redefine and expand the types of hypotheses that can be tested from empirical population data in Part 8, where I offered a societally positive working example. My goal for this series installment is to continue that discussion, here focusing on the types of negative examples that can and do provoke pushback against big data as a developing capability.

• At the end of Part 8 I stated that I would turn here in this posting to consider the ways that “Big Brother” authoritarian states are coming to use big data to “identify and crush political dissent and open public discussion” and I will at least briefly look into that here.
• But more than that, I will at least begin to look into how a legion of “Little Brothers” such as major corporations can and at times do misuse big data capabilities too, and certainly when their policies and practices for collecting, organizing, using and commoditizing individually identifying and tagged information puts them at odds with the needs and wishes of the people they develop all of this big data content from.

But I begin here with Big Brother, and I begin that by making a basic foundational observation:

• Governments that accumulate big data about their own nationals and about foreigners who interact with them, with a specific goal of tracking them to control them – governments that play a true Big Brother role, do this as a perceived survival requirement, and not out of intentionally malicious or evil intent. “Malicious” and “evil” are traits that might be attributed to them, but they are not ones that they would embrace or accept as accurately applying to them. They do this out of perceived overriding need.

And with that in mind, I turn to consider the People’s Republic of China as a first working example.

• China is widely known, at least in the West for its Great Firewall – its Golden Shield Project as it is more officially called there. But this should only be seen as one half of a larger and more comprehensive system, as simply tracking the online conversation and blocking or allowing online access can only be seen as half of a solution to controlling and managing their population so as to stifle the possibility of dissent.
• The other half of this comes from knowing who is doing what online and off and in being able to predict who might do what, and with tracking online activity attempted and pursued only constituting a small part of that. This other half is where China’s one allowed Party and its government seek to in effect predictively know their entire population and on both a fine-meshed population demographic basis, and on an individual basis.
• This approach to population management at a demographic and individual level goes back much farther than do computer systems or computerized databases, of course but the advent of those systems have made it possible to know and to predict with a level of detail and at both levels, never previously even conceivable let alone possible.
• Publically and openly, big data systems development are still in a relatively early stage in China with the bulk of this activity appearing to be taking place in their internet industry, with for example, companies such as Taobao, Tecent Holdings and Baidu developing big data applications on open source software frameworks. Financial sector institutions and others are also beginning to actively, publically enter this arena in China too now.
• And of course, China’s big data objectives go far beyond simply accumulating data about individuals and population groups. They are also collecting data about and from businesses and organizations, private sector and public and of all sorts too. And some of this also has a more public face as well.
• Here, it is crucially important to remember that the boundary between China’s true private sector with its privately owned businesses on one hand, and its government and government owned enterprises on the other is porous and hazy at best, and not just from the way that its People’s Liberation Army controls and even directly owns a larger share of China’s overall business and industrial sectors than any other participant. So in a fundamental sense, China’s private sector big data initiatives are governmental big data initiatives too. And that government can and does collect together as much as it can from all of these data accumulators and processors and more for its own use too.
• China’s government is still, by all appearances, at an early state in developing a Big Brother big data capability but that is clearly one of their highest priority information technology and knowledge management systems goals. I expect to see more and more of that news story to come out as these capabilities continue to be developed and put in place.

And what China is doing, others are at least attempting to do too, and that in at least embryonic stages of development includes initiatives arising in countries such as Iran and North Korea too.

• Whenever you find a country is developing or seeking to develop its own counterpart to China’s Golden Shield Project, you can be sure it is also at least planning and prioritizing for building a matching computer systems-based big data population oversight and control capability too.

But with that said, I would argue that the “Little Brothers” of corporate big data accumulation and use, as noted above at the top of this posting are going to at least collectively be both more pervasive and more impactful than any of their perhaps more visible Big Brother counterparts. And while Big Brother does this for survival, Little Brother does this for profit. I am going to look into the issues of Little Brother in my next series installment, there focusing on concerns that have continuously seemed to have arisen regarding Facebook and its development and use of big data. Meanwhile, you can find this and related postings at Ubiquitous Computing and Communications – everywhere all the time and its continuation page.

Accommodating and thriving in the midst of change in jobs and careers 4 – job and career timeframes

This is my fourth posting in a series on change and even disruptive change as it can reshape our work lives and our careers (see Guide to Effective Job Search and Career Development – 2, postings 306-308 for Parts 1-3.) And I have been writing this series as a direct successor to my immediately preceding series: Career Changes, Career Transitions (same directory, postings 285-305.) And as already noted I offer this series with a goal of addressing the challenges of job search and career planning and development in a period of profound change, of the degree and severity that we have been seeing, and that we can expect to continue seeing and certainly through any anticipatable future.

Even when we are thinking and planning long-term, we live in our immediate here and now. That holds for our work lives and our career paths as much as it does for anything else in our lives. But if we are going to find and pursue our own best possible career path and take the steps that would make it our reality we still have to think and plan long-term too. So career planning involves juggling two very different types of timeframe. And each has its own purposes and contexts where it offers value and each: long-term and short-term carries its own assumptions and limitations too. Either can lead us off-track if we pursue them in the wrong contexts.

My goal here in this posting is to explore timeframe issues and challenges, and I would begin with the immediate here, and now and short-term timeframe.

• When we are working at a job we need to think and act short-term in addressing our immediate here and now work responsibilities and the goals and priorities we face, and that we will be performance reviewed on.
• When we are actively looking for a new job, and whether or not we are already working, we take a fairly specifically short timeframe approach there too, when mapping out and carrying through upon job search campaigns (for a discussion of that see for example the series Finding Your Best Practices Plan B When Your Job Search isn’t Working, at my Guide to Effective Job Search and Career Development, as postings 56-72.) The goal in all of this is to keep moving forward and with a search momentum that can help us to succeed in securing that next-step job.

But at the same time, we need to think long-term when visualizing and planning for a career path.

• When we are out of work and looking with a compelling need to find that next job now, long term considerations can easily be set aside, no matter how important they are.
• And it can be easy to slip into a pattern of never really looking all that much beyond the short-term here and now – in which case our career path and our work life are simply what happens as we are busy doing something else, and actually a long succession of immediate here-and-now something elses.

The day to day pressures and realities that we face tend to favor our over-reliance on short-term planning more than long, but problems can arise if we try only planning and thinking long-term too. I have probably seen more of this in the context of startup planning than career planning, but either way the result is building with gaps and with prioritization failures.

• Short term planning can help us define our priorities as we have to address them.
• Long term planning shows us where all of that should be taking us.

Keeping track of what timeframe and what type of timeframe assumptions we are pursuing is key to making both our jobs and our careers work for us.

I am going to turn in my next series installment to consider the issues and challenges of long-term unemployment. Meanwhile, you can find this and related postings and series at my Guide to Effective Job Search and Career Development – 2 and at my first Guide directory page on Job Search and Career Development.

Commoditizing the standardized, commoditizing the individually customized 4: acknowledging the consumer demand for choice 2

This is my fourth installment in a series on the changing nature of production and commoditization (see Business Strategy and Operations – 2, postings 364 and loosely following for Parts 1-3.)

Up to here, I have been discussing the development of the first automobiles, and how Henry Ford reinvented the car for the general public, and certainly for its emerging middle class. And in Part 3 I began a discussion of how the public began to demand variety and choice in the cars they bought, and for both practical reasons and for variety that would reflect their individuality. I also at least noted how economy of scale made assembly lines more cost-effective for production of product variety that would support consumer choice.

I refer here, back to Part 1 and Part 2 of this series where I wrote of the importance of standardization in making assembly lines work at all, and how end product variety serves as a stressor to assembly line cost-effectiveness per se.

So in a fundamental sense, I left Part 3 with a contradiction in needs and a fundamental challenge to the assembly line production system per se.

• Yes, it is true that a company such as Ford’s could maintain much of the efficiency of single process and single product-type work flow in its assembly lines by scheduling single type runs for producing different specific product builds.
• And with production scale they could essentially recapture all of the potential of a single process and work flow system by running separate and parallel production lines, one for example producing a coupe model car and another a Model TT truck design.
• And for real efficiency, every part and process going into assembling one of these vehicles that could be, would be standardized and fit equally well into any other Ford vehicle produced – and across vehicle lines and from year to year too.
• But the middle class and their expanding consumer base that came to want and need cars and trucks wanted more and more variety, and new models and designs.
• And the increasing competition of other businesses that also built for this growing and demanding market offered more and more variety and choice too.
• Annual new model releases became a basic fact of life for the mass production automotive industry and the pressures became intense to offer wider and wider variety in any given year too. And something had to give – either the basic price point that a car would have to go for would have to go up, or the profit margin per vehicle assembled and sold would have to go down, or consumer demand for variety would have to be thwarted.
• But competitive pressures made it difficult to simply increase the price of a car or truck, and it made it difficult to simply ignore consumer demands too. This, among other things meant a rethinking of the assembly line per se and its cost centers and how it could become more streamlined and effective.

And with this, I cut away from the early days of the mass produced automobile to much closer to today.

• Employee salaries and benefits are expensive, and I have to add that pension systems can with time become even more so as the major auto manufacturers in the United States all found out the hard way.
• Skilled employees and the human eye and touch are needed and will remain so for a significant time to come, but automation of more and more rote production processes, from spot welding to painting and more, have transformed the assembly line completely – and some assembly line systems are already fully automated except for quality control and managerial oversight.
• But for purposes of this discussion, I would focus on a none-of-the-above for rethinking the assembly line: rethinking what product variety and even personalization mean.
• And in that regard I note that the one, and in many respect only place where variety of product really has to show a distinctive difference is where you see it.

Let’s say, to take that out of the abstract that you are building cars with three different types of seating for the driver and front passenger: a standard model bench seat and two different bucket seat styles, and with each available in three cover materials, and each of them in five colors that would be coordinated with paint color selected. Just considering seating, this means 45 different combinations and 45 different model differences that would have to be supported in an assembly line system. But let’s also say that everything that goes into those seats and both for design and materials, that can be standardized across all of them are standardized.

• So they all connect to the chassis of the car at the same basic assembly points, and with the exact same bolts and brackets too. And the seat padding is the same for materials used – just the seat covers differ.
• So you set up your assembly lines to be supplied for parts and materials using lean just in time strategies and operational approaches for managing what you need to have in inventory, and for getting that where it is needed when it is.
• And you automate as noted above wherever possible.
• And you leverage this with smart information technology for tracking and preparing for demand for each and every product variation that you would produce, and as close to real time as possible so you can produce and ship as quickly as possible, and with the right balance of product output produced, and shipped to the right dealerships at the right time.

Now expand out the range of options that can be modified and even customized across the entire vehicle and allow for the customer to buy any of a huge range of options and choices, depending on how much they would be willing to pay to get that extra special feature or build. And this brings me to the Scion: a Toyota Motor Corporation brand manufactured primarily for their North American market, which for purposes of this posting and series, I would hold up as being as much a benchmark at that first Model T.

• The basic idea behind the Scion is not that you buy one of a select set of basic, preconfigured models with for example one paint color going with one seat cover color – it is that you be able to buy a car that you can see as personalized to you, as the range of options and combinations of them available to choose from, expands past the “standard options” range to a personalization range for the variety that can be selected from.
• So going back to the first true assembly lines and the first mass produced Model T’s and with this in mind as an evolutionary descendant of that, with the modern assembly line as exemplified by the Scion, assembly line meets what amounts to artisanal for variety and even individualization of products possible – but with assembly line efficiencies and costs.
• And I see the Scion and its production in this as just a first step in what is going to become much more the basic and standard assembly line and mass production approach – mass production of the individualized and personalized, and for what I expect to be a progressively wider range of products and product types.

I am going to turn in my next series installment to consider 3-dimentional printers and single copy-friendly printing kiosks for on-demand book publication while you wait. Meanwhile, you can find this and related postings at Business Strategy and Operations and its Part 2 continuation page.