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Barry Devlin

As one of the founders of data warehousing back in the mid-1980s, a question I increasingly ask myself over 25 years later is: Are our prior architectural and design decisions still relevant in the light of today's business needs and technological advances? I'll pose this and related questions in this blog as I see industry announcements and changes in way businesses make decisions. I'd love to hear your answers and, indeed, questions in the same vein.

About the author >

Dr. Barry Devlin is among the foremost authorities in the world on business insight and data warehousing. He was responsible for the definition of IBM's data warehouse architecture in the mid '80s and authored the first paper on the topic in the IBM Systems Journal in 1988. He is a widely respected consultant and lecturer on this and related topics, and author of the comprehensive book Data Warehouse: From Architecture to Implementation.

Barry's interest today covers the wider field of a fully integrated business, covering informational, operational and collaborative environments and, in particular, how to present the end user with an holistic experience of the business through IT. These aims, and a growing conviction that the original data warehouse architecture struggles to meet modern business needs for near real-time business intelligence (BI) and support for big data, drove Barry’s latest book, Business unIntelligence: Insight and Innovation Beyond Analytics, now available in print and eBook editions.

Barry has worked in the IT industry for more than 30 years, mainly as a Distinguished Engineer for IBM in Dublin, Ireland. He is now founder and principal of 9sight Consulting, specializing in the human, organizational and IT implications and design of deep business insight solutions.

Editor's Note: Find more articles and resources in Barry's BeyeNETWORK Expert Channel and blog. Be sure to visit today!

March 2014 Archives

eco-skyscraper-by-vikas-pawar-2a.jpgIn an era of "big data this" and "Internet of Things that", it's refreshing to step back to some of the basic principles of defining, building and maintaining data stores that support the process of decision making... or data warehousing, as we old-fashioned folks call it. Kalido did an excellent job last Friday of reminding the BBBT just what is needed to automate the process of data warehouse management. But, before the denizens of the data lake swim away with a bored flick of their tails, let me point out that this matters for big data too--maybe even more so. I'll return to this towards the end of this post.

In the first flush of considering a BI or analytics opportunity in the business and conceiving a solution that delivers exactly the right data needed to address that pesky problem, it's easy to forget the often rocky road of design and development ahead. More often forgotten, or sometimes ignored, is the ongoing drama of maintenance. Kalido, with their origins as an internal IT team solving a real problem for the real business of Royal Dutch Shell in the late '90s, have kept these challenges front and center.

All IT projects begin with business requirements, but data warehouses have a second, equally important, staring point: existing data sources. These twin origins typically lead to two largely disconnected processes. First, there is the requirements activity often called data modeling, but more correctly seen as the elucidation of a business model, consisting of function required by the business and data needed to support it. Second, there is the ETL-centric process of finding and understanding the existing sources of this data, figuring out how to prepare and condition it, and designing the physical database elements needed to support the function required.

Most data warehouse practitioners recognize that the disconnect between these two development processes is the origin of much of the cost and time expended in delivering a data warehouse. And they figure out a way through it. Unfortunately, they often fail to recognize that each time a new set of data must be added or an existing set updated, they have to work around the problem yet again. So, not only is initial development impacted, but future maintenance remains an expensive and time-consuming task. An ideal approach is to create an integrated environment that automates the entire set of tasks from business requirements documentation, through the definition and execution of data preparation, all the way to database design and tuning. Kalido is one of a small number of vendors who have taken this all-inclusive approach. They report build effort reductions of 60-85% in data warehouse development.

Conceptually, we move from focusing on the detailed steps (ETL) of preparing data to managing the metadata that relates the business model to the physical database design. The repetitive and error-prone donkey-work of ETL, job management and administration is automated. The skills required in IT change from programming-like to modeling-like. This has none of the sexiness of predictive analytics or self-service BI. Rather, it's about real IT productivity. Arguably, good IT shops always create some or all of this process- and metadata-management infrastructure themselves around their chosen modeling, ETL and database tools. Kalido is "just" a rather complete administrative environment for these processes.

Which brings me finally back to the shores of the data lake. As described, the data lake consists of a Hadoop-based store of all the data a business could ever need, in its original structure and form, and into which any business user can dip a bucket and retrieve the data required without IT blocking the way. However, whether IT is involved or not, the process of understanding the business need and getting the data from the lake into a form that is useful and usable for a decision-making requirement is exactly identical to that described in my third paragraph above. The same problems apply. Trust me, similar solutions will be required.

Image: http://inhabitat.com/vikas-pawar-skyscraper/



Posted March 17, 2014 5:33 AM
Permalink | No Comments |
eco-skyscraper-by-vikas-pawar-2a.jpgIn an era of "big data this" and "Internet of Things that", it's refreshing to step back to some of the basic principles of defining, building and maintaining data stores that support the process of decision making... or data warehousing, as we old-fashioned folks call it. Kalido did an excellent job last Friday of reminding the BBBT just what is needed to automate the process of data warehouse management. But, before the denizens of the data lake swim away with a bored flick of their tails, let me point out that this matters for big data too--maybe even more so. I'll return to this towards the end of this post.

In the first flush of considering a BI or analytics opportunity in the business and conceiving a solution that delivers exactly the right data needed to address that pesky problem, it's easy to forget the often rocky road of design and development ahead. More often forgotten, or sometimes ignored, is the ongoing drama of maintenance. Kalido, with their origins as an internal IT team solving a real problem for the real business of Royal Dutch Shell in the late '90s, have kept these challenges front and center.

All IT projects begin with business requirements, but data warehouses have a second, equally important, staring point: existing data sources. These twin origins typically lead to two largely disconnected processes. First, there is the requirements activity often called data modeling, but more correctly seen as the elucidation of a business model, consisting of function required by the business and data needed to support it. Second, there is the ETL-centric process of finding and understanding the existing sources of this data, figuring out how to prepare and condition it, and designing the physical database elements needed to support the function required.

Most data warehouse practitioners recognize that the disconnect between these two development processes is the origin of much of the cost and time expended in delivering a data warehouse. And they figure out a way through it. Unfortunately, they often fail to recognize that each time a new set of data must be added or an existing set updated, they have to work around the problem yet again. So, not only is initial development impacted, but future maintenance remains an expensive and time-consuming task. An ideal approach is to create an integrated environment that automates the entire set of tasks from business requirements documentation, through the definition and execution of data preparation, all the way to database design and tuning. Kalido is one of a small number of vendors who have taken this all-inclusive approach. They report build effort reductions of 60-85% in data warehouse development.

Conceptually, we move from focusing on the detailed steps (ETL) of preparing data to managing the metadata that relates the business model to the physical database design. The repetitive and error-prone donkey-work of ETL, job management and administration is automated. The skills required in IT change from programming-like to modeling-like. This has none of the sexiness of predictive analytics or self-service BI. Rather, it's about real IT productivity. Arguably, good IT shops always create some or all of this process- and metadata-management infrastructure themselves around their chosen modeling, ETL and database tools. Kalido is "just" a rather complete administrative environment for these processes.

Which brings me finally back to the shores of the data lake. As described, the data lake consists of a Hadoop-based store of all the data a business could ever need, in its original structure and form, and into which any business user can dip a bucket and retrieve the data required without IT blocking the way. However, whether IT is involved or not, the process of understanding the business need and getting the data from the lake into a form that is useful and usable for a decision-making requirement is exactly identical to that described in my third paragraph above. The same problems apply. Trust me, similar solutions will be required.

Image: http://inhabitat.com/vikas-pawar-skyscraper/



Posted March 17, 2014 5:33 AM
Permalink | 1 Comment |
Parts 1, 2, 3, 4 and 4A of this series explored the problem as I see it. Now, finally, I consider what we might do if my titular question actually makes sense.

Mammoth kill.jpgTo start, let's review my basic thesis. Mass production and competition, facilitated by ever improving technology, have been delivering better and cheaper products and improving many people's lives (at least in the developed world) for nearly two centuries. Capital, in the form of technology, and people--labor--work together in today's system to produce goods that people purchase using earnings from their labor. As technology grows exponentially better, an ever greater range of jobs are open to displacement. When technology displaces some yet to be determined percentage of labor, this system swings out of balance; there are simply not enough people with sufficient money to buy the products made, no matter how cheaply. We have not yet reached this tipping point because, throughout most of this period, the new jobs created by technology have largely offset the losses. However, employment trends in the past 10-15 years in the Western world suggest that this effect is no longer operating to the extent that it was, if at all.

In brief, the problem is that although technology produces greater wealth (as all economists agree), without its transfer to the masses through wages paid for labor, the number of consumers becomes insufficient to justify further production. The owners of the capital assets accumulate more wealth--and we see this happening in the increasing inequality in society--but they cannot match the consumption of the masses. Capitalism, or perhaps more precisely, the free market then collapses.

Let's first look at the production side of the above equation. What can be done to prevent job losses outpacing job creation as a result of technological advances? Can we prevent or put a damper on the great hollowing out of middle-income jobs that is creating a dumbbell-shaped distribution of a few highly-paid experts at one end and a shrinking swathe of lower-paid, less-skilled workers at the other? Can (or should) we move to address the growing imbalance of power and wealth between capital (especially technologically based) and labor? Let's be clear at the start, however, turning off automation is not an option I consider.

My suggestions, emerging mainly from the thinking discussed earlier, are mainly economic and social in nature. An obvious approach is to use the levers of taxation--as is done in many other areas--to drive a desired social outcome. We could, for example, reform taxation and social charges on labor to reduce the cost difference between using people and automating a process. In a similar vein, shifting taxation from labor to capital could also be tried. I can already hear the Tea Party screaming to protect the free market from the damn socialist. But, if my analysis is correct, the free market is about to undergo, at best, a radical change, if employment drops below some critical level. Pulling these levers soon and fairly dramatically is probably necessary; this is an approach that can only delay the inevitable. Another approach is for industry itself to take steps to protect employment. Mark Bonchek , writing in a recent Harvard Business Review blog, describes a few "job entrepreneurs" who maximize jobs instead of profits (but still make profits as well), including one in the Detroit area aimed at creating jobs for unemployed auto workers.

Moving from the producer's side to the consumer's view, profit aside, why did we set off down the road of the Industrial Revolution? To improve people's daily lives, to lessen the load of hard labor, to alleviate drudgery. The early path was not clear. Seven-day labor on the farm was replaced by seven-day labor in the factory. But, by the middle of the last century, working hours were being reduced in the workplace and in the home, food was cheaper and more plentiful; money and time were available for leisure. In theory, the result should have been an improvement in the human condition. In practice, the improvement was subverted by the mass producers. They needed to sell ever more of the goods they could produce so cheaply that profit came mainly through volume sales. Economist Victor Lebow's 1955 proclamation of "The Real Meaning of Consumer Demand" sums it up: "Our enormously productive economy demands that we make consumption our way of life... that we seek our spiritual satisfaction and our ego satisfaction in consumption... We need things consumed, burned up, worn out, replaced and discarded at an ever-increasing rate". Of course, some part of this is human nature, but it has been driven inexorably by advertising. We've ended up in the classic race to the bottom, even to the extent of products being produced with ever shorter lifespans to drive earlier replacement. Such consumption is becoming increasingly unsustainable as the world population grows, finite resources run out and the energy consumed in both production and use drives increasing climate change. As the president of Uruguay, Jose Mujica, asked of the Rio+20 Summit in 2012, "Does this planet have enough resources so seven or eight billion can have the same level of consumption and waste that today is seen in rich societies?"

My counter-intuitive suggestion here, and one I have not seen raised by economists (surprisingly?), is to ramp down consumerism, mainly through a reinvention of the purposes and practices of advertising. Reducing over-competition and over-consumption would probably drive interesting changes in the production side of the equation, including reduced demand for further automation, lower energy consumption, product quality being favored over quantity, higher savings rates by (non-)consumers, and more. Turning down the engine of consumption could also enable changes for the better in the financial markets, reducing the focus on quarterly results in favor of strategically sounder investment. Input from economists would be much appreciated.

But, let's wrap up. The title of this series asked: will automation through big data and the Internet of Things drive the death of capitalism? Although some readers may have assumed that this was my preferred outcome, I am more of the opinion that capitalism and the free market need to evolve rather quickly if they are to survive and, preferably, thrive. But, this would mean some radical changes. For example, a French think-tank, LH Forum, suggests the development of a positive economy that: "reorients capitalism towards long-term challenges. Altruism toward future generations is a much more powerful incentive than [the] selfishness which is supposed to steer the market economy". Other fundamental rethinking comes from British/Scottish historian, Niall Ferguson, who takes a wider view of "The Great Degeneration" of Western civilization. In a word, this is a topic that requires broad, deep and urgent thought.

For my more IT-oriented readers, I suspect this blog series has taken you far from basic ground. For this, I do not apologize. As described in Chapter 2 of "Business unIntelligence", I believe that the future of business and IT is to be joined at the hip. The biz-tech ecosystem declares that technology is at the heart of all business development. Business must understand IT. IT must be involved in the business. I suggest that understanding the impact of automation on business and society is a task for IT strategists and architects as much, if not more, as it is for economists and business planners.

Image from: www.moonbattery.com/archives/2010/07/prehistoric-cli.html. All elephant photos in the earlier posts are my own work!


Posted March 11, 2014 6:47 AM
Permalink | No Comments |
Parts 1, 2, 3, 4 and 4A of this series explored the problem as I see it. Now, finally, I consider what we might do if my titular question actually makes sense.

Mammoth kill.jpgTo start, let's review my basic thesis. Mass production and competition, facilitated by ever improving technology, have been delivering better and cheaper products and improving many people's lives (at least in the developed world) for nearly two centuries. Capital, in the form of technology, and people--labor--work together in today's system to produce goods that people purchase using earnings from their labor. As technology grows exponentially better, an ever greater range of jobs are open to displacement. When technology displaces some yet to be determined percentage of labor, this system swings out of balance; there are simply not enough people with sufficient money to buy the products made, no matter how cheaply. We have not yet reached this tipping point because, throughout most of this period, the new jobs created by technology have largely offset the losses. However, employment trends in the past 10-15 years in the Western world suggest that this effect is no longer operating to the extent that it was, if at all.

In brief, the problem is that although technology produces greater wealth (as all economists agree), without its transfer to the masses through wages paid for labor, the number of consumers becomes insufficient to justify further production. The owners of the capital assets accumulate more wealth--and we see this happening in the increasing inequality in society--but they cannot match the consumption of the masses. Capitalism, or perhaps more precisely, the free market then collapses.

Let's first look at the production side of the above equation. What can be done to prevent job losses outpacing job creation as a result of technological advances? Can we prevent or put a damper on the great hollowing out of middle-income jobs that is creating a dumbbell-shaped distribution of a few highly-paid experts at one end and a shrinking swathe of lower-paid, less-skilled workers at the other? Can (or should) we move to address the growing imbalance of power and wealth between capital (especially technologically based) and labor? Let's be clear at the start, however, turning off automation is not an option I consider.

My suggestions, emerging mainly from the thinking discussed earlier, are mainly economic and social in nature. An obvious approach is to use the levers of taxation--as is done in many other areas--to drive a desired social outcome. We could, for example, reform taxation and social charges on labor to reduce the cost difference between using people and automating a process. In a similar vein, shifting taxation from labor to capital could also be tried. I can already hear the Tea Party screaming to protect the free market from the damn socialist. But, if my analysis is correct, the free market is about to undergo, at best, a radical change, if employment drops below some critical level. Pulling these levers soon and fairly dramatically is probably necessary; this is an approach that can only delay the inevitable. Another approach is for industry itself to take steps to protect employment. Mark Bonchek , writing in a recent Harvard Business Review blog, describes a few "job entrepreneurs" who maximize jobs instead of profits (but still make profits as well), including one in the Detroit area aimed at creating jobs for unemployed auto workers.

Moving from the producer's side to the consumer's view, profit aside, why did we set off down the road of the Industrial Revolution? To improve people's daily lives, to lessen the load of hard labor, to alleviate drudgery. The early path was not clear. Seven-day labor on the farm was replaced by seven-day labor in the factory. But, by the middle of the last century, working hours were being reduced in the workplace and in the home, food was cheaper and more plentiful; money and time were available for leisure. In theory, the result should have been an improvement in the human condition. In practice, the improvement was subverted by the mass producers. They needed to sell ever more of the goods they could produce so cheaply that profit came mainly through volume sales. Economist Victor Lebow's 1955 proclamation of "The Real Meaning of Consumer Demand" sums it up: "Our enormously productive economy demands that we make consumption our way of life... that we seek our spiritual satisfaction and our ego satisfaction in consumption... We need things consumed, burned up, worn out, replaced and discarded at an ever-increasing rate". Of course, some part of this is human nature, but it has been driven inexorably by advertising. We've ended up in the classic race to the bottom, even to the extent of products being produced with ever shorter lifespans to drive earlier replacement. Such consumption is becoming increasingly unsustainable as the world population grows, finite resources run out and the energy consumed in both production and use drives increasing climate change. As the president of Uruguay, Jose Mujica, asked of the Rio+20 Summit in 2012, "Does this planet have enough resources so seven or eight billion can have the same level of consumption and waste that today is seen in rich societies?"

My counter-intuitive suggestion here, and one I have not seen raised by economists (surprisingly?), is to ramp down consumerism, mainly through a reinvention of the purposes and practices of advertising. Reducing over-competition and over-consumption would probably drive interesting changes in the production side of the equation, including reduced demand for further automation, lower energy consumption, product quality being favored over quantity, higher savings rates by (non-)consumers, and more. Turning down the engine of consumption could also enable changes for the better in the financial markets, reducing the focus on quarterly results in favor of strategically sounder investment. Input from economists would be much appreciated.

But, let's wrap up. The title of this series asked: will automation through big data and the Internet of Things drive the death of capitalism? Although some readers may have assumed that this was my preferred outcome, I am more of the opinion that capitalism and the free market need to evolve rather quickly if they are to survive and, preferably, thrive. But, this would mean some radical changes. For example, a French think-tank, LH Forum, suggests the development of a positive economy that: "reorients capitalism towards long-term challenges. Altruism toward future generations is a much more powerful incentive than [the] selfishness which is supposed to steer the market economy". Other fundamental rethinking comes from British/Scottish historian, Niall Ferguson, who takes a wider view of "The Great Degeneration" of Western civilization. In a word, this is a topic that requires broad, deep and urgent thought.

For my more IT-oriented readers, I suspect this blog series has taken you far from basic ground. For this, I do not apologize. As described in Chapter 2 of "Business unIntelligence", I believe that the future of business and IT is to be joined at the hip. The biz-tech ecosystem declares that technology is at the heart of all business development. Business must understand IT. IT must be involved in the business. I suggest that understanding the impact of automation on business and society is a task for IT strategists and architects as much, if not more, as it is for economists and business planners.

Image from: www.moonbattery.com/archives/2010/07/prehistoric-cli.html. All elephant photos in the earlier posts are my own work!


Posted March 11, 2014 6:47 AM
Permalink | 2 Comments |
Parts 1, 2, 3, and 4 of this series explored the problem as I see it and examined the views of some economists and other authors. This was supposed to be the final part, where I answered the question of the title. But, instead I have a bonus blog... inspired by Brynjolfsson and McAfee's Second Machine Age.

Addo Elephant B.JPG"The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies" by Erik Brynjolfsson and Andrew McAfee, published as recently as last January, has been lying half-read on my Kindle desk for some weeks, mainly because its early chapters overlap with much of the technological material I'd referenced already. But a couple of tweets in response to Part 4 (thanks to Michael @mjcavaretta and Patrick @itworks sent me back to reexamine the content. There I found enough interesting thinking to fill a new post. I have to say that I was also hoping that the authors might come to my rescue as I searched for answers to my question above.

The central discussion of the book about the economic effect of advances in technology is couched in terms of bounty and spread. The former is the overall benefit accruing to the economy and the people whose lives are affected. The authors believe this bounty is enormous and growing, although they do admit that traditional economic measures, such as GDP, are no longer adequate. Nonetheless, my gut feel is that technology has, by many measures, improved the lot of humanity, or has the potential to do so. Spread is perhaps more easily quantified: it is the gap in wealth, income, mobility and more between people at the top and bottom of the ladder. And it has been demonstrably growing wider in recent years--which is socially and economically bad news. The authors' question then is whether the growth in bounty can counteract that in spread. Will the rising tide raise all boats, even though the super-yachts may be raised considerably further than the simple rowboats or even the rafts made of society's junk?

The news is bad. The authors report that "between 1983 and 2009, Americans became vastly wealthier overall as the total value of their assets increased. However... the bottom 80 percent of the income distribution actually saw a net decrease in their wealth." Combining this with a second observation that income distribution is moving from a normal (bell) curve towards a power law curve with a long tail of lower incomes, my conclusion is that the negative effect of spread is outpacing the positive lift of bounty. Note finally, that the above discussion is focused on the developed economies. A brief visit to one of the emerging economies should suffice to convince that the inequality there is far greater. The old saw that the rich get richer and the poor get poorer appears increasingly appropriate.

And Brynjolfsson and McAfee do end up agreeing with me in Chapter 11 as they sort through various arguments on the relative importance of bounty and spread. "Instead of being confident that the bounty from technology will more than compensate for the spread it generates, we are instead concerned about something close to the reverse: that the spread could actually reduce the bounty in years to come."

Turning their attention to the main underlying cause, the advance of technology and the possibility of "technological unemployment", they come desperately close to my argument: "there is a floor on how low wages for human labor can go. In turn, that floor can lead to unemployment: people who want to work, but are unable to find jobs. If neither the worker nor any entrepreneur can think of a profitable task that requires that worker's skills and capabilities, then that worker will go unemployed indefinitely. Over history, this has happened to many other inputs to production that were once valuable, from whale oil to horse labor. They are no longer needed in today's economy even at zero price. In other words, just as technology can create inequality, it can also create unemployment. And in theory, this can affect a large number of people, even a majority of the population, and even if the overall economic pie is growing."

But, they instantly--and, in my view, unjustifiably--veer back to the new economic orthodoxy that machines are more likely to complement humans than displace them and then focus on that scenario, suggesting that we must focus on equipping humans for this role through better and different education. While accepting that there are certainly instances where this is true, I see no evidence in recent years that this is the primary scenario we need to address. Only in Chapter 14, do they finally make some "Long-Term Recommendations" that suggest how changes in taxation might be required to rebalance the inequality in income and unemployment that they see eventually emerging. In this they echo both Martin Ford and Tyler Cohen, as we've seen earlier.

But I cannot help but feel this is far too little and far too late. So, what would I suggest? I believe we need some rather counter-intuitive thinking. And that is the long-promised topic of the real Part 5.


Posted March 4, 2014 5:46 AM
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