Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 3 of the blog series.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 2 of the blog series.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Not everyone, of course, has access to all the sources, so to kick things off let me present a framework of the possible, within which each organization can determine what its actual will be. Part 1 of the blog series.

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Disintegrating Castles & Category Kings

By Geoffrey Moore, Oct 03, 2017

The most prevalent impact of digitalization on the structure of markets has been to reduce the barriers to entry for a whole raft of established categories—as it has, for example in media, retail, consumer packaged goods, fast food, and transportation. A flood of small but numerous new entrants, individually nothing more than minor nuisances, become collectively a real presence. This shows up in market-share pie charts where the catch-all category Other is growing faster than the market as a whole. The result in each case is category fragmentation, and the big loser in each case is the currently reigning category king.

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Digital Systems Maturity Model

By Geoffrey Moore, Aug 17, 2017

Every so often a phrase emerges from the Word Cloud to achieve capital importance, the sort of thing that authors and pundits can dine out on years to come (well, we do have to eat too, you know). At present that phrase is digital transformation.

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Saving Retail

By Geoffrey Moore, Jul 18, 2017

Okay, so you know a sector is in trouble when there is a Web page in Wikipedia entitled “The Retail Apocalypse.” This post is not about how much trouble retail is in. This one is about how it can get out.

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The Manufacturer’s Dilemma

By Geoffrey Moore, Jun 20, 2017

There is a lot of serious talk in America these days about improving the state of our manufacturing sector. Smart products, Internet of things, robotics, predictive maintenance—all great stuff. But none of it addresses the most fundamental challenge facing the sector: how to deal with a demand/supply inversion which has made the customer king.

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Understanding Power in the Digital Economy

By Geoffrey Moore, May 09, 2017

We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.

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As I have discussed in prior blogs, the focus of enterprise computing for most of the 20th century was on deploying Systems of Record, first on mainframes, then minicomputers, then client-server systems. These were and continue to be the transaction processing backbones that drive global commerce. In the first fifteen years of this century, however, we have seen a profound shift in spending emphasis away from Systems of Record, which are now in maintenance mode, and toward Systems of Engagement, the focus being on connecting with customers, partners, and employees in digitally effective ways leveraging the ubiquity of smart phones. That movement has been inside the tornado for some time now such that, while there will be a lot of money spent here over the next ten years, I think it is time to look ahead to the next wave.

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AI is from Venus, Machine Learning is from Mars

By Geoffrey Moore, Oct 20, 2016

The rise of cloud computing brings with it the promise of infinite computing power. The rise of Big Data brings with it the possibility of ingesting all the world’s log files. The combination of the two has sparked widespread interest in data science as truly the “one ring to rule them all.” When we speculate about such a future, we tend to use two phrases to describe this new kind of analytics—artificial intelligence (AI) and machine learning. Most people use them interchangeably. This is a mistake.

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