CYBAEA is an independent consulting organization. We help our clients prepare for and profit from disruptive technology innovation.

We support organizations in defining, communicating, and delivering their strategy for the use of technology. Additionally, we provide independent research that identifies and analyzes major trends in technology and their impact on business.

Navigating Technology

History is littered with the corpses of companies that made assumptions about their future based on known technologies, only to be swept away by innovation rendering entire industries obsolete.

Such disruptive innovation can be a threat or an opportunity. It all depends on how prepared you are.

CYBAEA is an independent consulting organization. We support our clients defining, communicating, and delivering their strategy for the use of technology.

We work closely with our clients to add measurable value through each stage of the process of realizing your business objectives:

Vision. Everything starts with a vision. You've got to have a dream, as the song has it. How will you articulate and communicate your vision to ensure that it is taken up by investors, employees and other stakeholders?

Research. Research places the vision in context. What is the competition doing, what disruptive technologies are on the horizon, what are the alternative strategies?

Strategy. Strategy is the vision made concrete. How will you change the way you do business, your organization, and your relationship with suppliers, customers, employees, and investors?

Change program. Rarely, can a business strategy be implemented in a single step. Usually a change program is required. What is the portfolio of projects that will successfully implement the strategy, what are the project dependencies, and what is the optimal scheduling?

Read more about CYBAEA: What we do and who we are.

cybaea (latin), ae, f.: trading vessel; a transport, cargo, or merchant ship.

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Recent blog entries

Area Plots with Intensity Coloring

On 2010-07-13 07:47:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:

I am not sure apeescape’s ggplot2 area plot with intensity colouring is really the best way of presenting the information, but it had me intrigued enough to replicate it using base R graphics.

The key technique is to draw a gradient line which R does not support natively so we have to roll our own code for that. Unfortunately, lines(..., type="l") does not recycle the colour col= argument, so we end up with rather more loops than I thought would be necessary.

We also get a nice opportunity to use the under-appreciated read.fwf function.

Employee productivity revisited

On 2010-06-22 11:45:00, Allan Engelhardt wrote in CYBAEA Journal:

We have a mild obsession with employee productivity and how that declines as companies get bigger. We have previously found that when you treble the number of workers, you halve their individual productivity which is scary.

We now re-do the analysis four years later and, just because we can, we are using the leading companies of the London stock exchange instead of the largest American companies.

The results still hold. We called it the 3/2 rule: treble the number of workers and you halve their individual productivity. Large companies with ten times the number of employees are ¼ as productive as their smaller competitors.

Employee productivity is a big issue. If all the FTSE-100 companies achieved their average profits per employee, then the index would generate almost £1 trn of additional net profits for the economy.

Employee productivity as function of number of workers revisited

On 2010-06-22 11:20:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:

We revisit the analysis for the FTSE-100 constituent companies and find that the relation still holds four years later and across a continent.

Comparing standard R with Revoutions for performance

On 2010-06-17 09:05:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:

Following on from my previous post about improving performance of R by linking with optimized linear algebra libraries, I thought it would be useful to try out the five benchmarks Revolutions Analytics have on their Revolutionary Performance pages.

Faster R through better BLAS

On 2010-06-15 10:21:00, Allan Engelhardt wrote in CYBAEA Data and Analysis:

Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.

But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.

The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.