In this episode of Beneficial Intelligence, I discuss other people's failures. They can affect you, as the recent Amazon Web Services outage showed. 

Cat owners who had trusted the feeding of their felines to internet-connected devices came home to find their homes shredded by hungry cats. People who had automated their lighting sat in darkness, yelling in vain at their Alexa devices for more light. More serious problems also occurred as students couldn't submit assignments, Ticketmaster couldn't sell Adele tickets and helpless investors watched their stocks tank while being unable to sell.  

On a personal level, this dependency is an occasional inconvenience. But for companies, it is a problem.

When you buy cloud services directly from Amazon, Microsoft, or Google, at least you know what you depend on, and can take your own precautions. 

But your SaaS vendors depend on one of the big three cloud providers. You will find that most of them consider using two different data centers with the same cloud vendor to be plenty of redundancy. It isn't. 

Another problem is your "smart" devices that all communicate via the internet to a server controlled by the device vendor. The vendor is running that server in one of the three big clouds. That means an Amazon outage can lock you out of your building. 

Some of your systems are business crucial. For these, you need to find out what your vendors depend on. Otherwise, you will be blindsided by other people's failures.

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Beneficial Intelligence is a bi-weekly podcast with stories and pragmatic advice for CIOs, CTOs, and other IT leaders. To get in touch, please contact me at sten@vesterli.com

In this episode of Beneficial Intelligence, I discuss the people shortage. It isn't real. 

Complaining about a lack of people is what is known as a "half argument." You say what you want, but not what you are willing to give up. That's like a politician promising to build a new public hospital but won't say where the money will come from. 

The full argument for missing people is "we cannot get the people we want at the conditions we are willing to offer."  If you had a crucial project that will make the business millions of dollars, you would be able to find the resources you need. You could simply offer three times the market rate, full benefits, and a 40-hour workweek with no overtime. 

Allocating resources is a basic leadership task. You rank your tasks and projects in order of descending business value and allocate available resources to the most valuable. It doesn't make sense for a CIO to say that the organization is "missing" a hundred programmers. A full argument would be that if we had a hundred extra programmers, we could build a specific IT system that is less valuable than all the current projects.

There might be a real shortage of money or copper or clean water. But there is no shortage of people.

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Beneficial Intelligence is a bi-weekly podcast with stories and pragmatic advice for CIOs, CTOs, and other IT leaders. To get in touch, please contact me at sten@vesterli.com

In this episode of Beneficial Intelligence, I discuss data hoarding. Gathering too much data costs money and doesn't add value.  We think we need all this data to train our AI, but hoarding data is the wrong place to start. 

Using a counterproductive metaphor, some say that "data is the new oil." That is a dangerous metaphor with no less than four problems:

• First, data is not fungible like oil is. One barrel of oil is just as valuable as the next barrel. But one data record does not have the same value as another data record.  
• Second, data hoarding shows diminishing returns. The value of 100 million barrels of oil is 100 times the value of 1 million barrels. But the value of 100 million transaction records is not 100 times the value of  1 million transaction records. 
• Third, the process of refining data into valuable business insight is not repeatable. Anybody can build an oil refinery. That's just a question of money. But extracting value from data is more art than science, and even with the best data scientists, you might still not be able to extract any value from your data. 
• Fourth, the value density in data is very low. Everything in a barrel of oil becomes a useful product. But most data records do not provide any business insight. 

Gathering data in the hope of extracting value is putting the cart in front of the horse. The right way to work with data is to start with a business goal and a hypothesis about which data might provide insight. Gather the data, run the experiment and evaluate. Don't just hoard data.

Beneficial Intelligence is a bi-weekly podcast with stories and pragmatic advice for CIOs, CTOs, and other IT leaders. To get in touch, please contact me at sten@vesterli.com