We live in the land of number crunching. Josiah Stamp, an economist and banker of early 20th Century England, was often quoted referring to the census, “The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the village watchman, who just puts down what he damn pleases.” While Stamp did participate in gathering data, he really wasn’t clear about why. He could never have imagined the advantages we have today ― computers, cell phones, and remote data gathering. The beauty of which is that the same computing power that makes data-gathering more powerful allows us to build out systems that interpret and analyze the data.
Common Controls
The National Institute of Standards and Technology (NIST) generated the Risk Management Framework, which repeatedly references something called “common controls” of information systems. Common controls are built-in cybersecurity safeguards that are embedded in many of our software packages. They limit back doors for hackers. They create protections that span multiple systems and software. For all intents and purposes, they are built-in governance.
The beauty of common controls is that they represent active, vital approaches to ensure that our systems function well. They serve that purpose in the background. They don’t trumpet that accomplishment every time a cyberthreat lurches over the horizon. They just do the job. It’s the job of analytics in action.
Why do common controls work? Three key aspects to them point us all in the right direction of how to move analytics to action:
- They are consistent
- They don’t require extensive outside intervention
- They have clear value at a variety of levels
Objective and Consistent Analytics
Consistency is a key for how we treat analytics when we’re managing them well. For example, we understand that 27 is a number. We all look at 27 roughly the same way. It’s the one that’s after 26 and before 28. That’s great, as long as we treat it that way. But the moment we ascribe personal, subjective values to that number, it’s no longer consistent. Twenty-seven years old could be interpreted as young by some and maturing by others. Would having $27 in your pocket make it a good day or bad?
If an action will follow your analytics, stay objective and consistent.
A Systematic Approach
We encounter error messages every day. Warnings. Alerts. Sometimes we heed them, and sometimes we struggle to turn them off. When we literally have analytics at our fingertips, we need to treat our response with that same level of consistency. Any time the budget is over by x%, we should spit out a consistent response. We don’t need to re-invent a lot of wheels. The fact that the analytics come in and we know precisely how to respond makes our lives easier. But it also makes life easier for those around us. They know our reactions before we have them. It becomes a system.
That systematic approach benefits everyone concerned. If someone has a problem with the response, we can readily point to the data and the analysis to ask where it may have gone astray. There’s no need to kick into self-defense mode. The data drives the action.
It’s important, however, not to fall into Josiah Stamp mode. He doubted his data and its sources. If our data comes into question, our actions will as well. However, if we value our datasets and consistently devise valid action and interpretation approaches, everyone comes out ahead. And the actions that ensue? They’re virtually automatic. Again, consistency is key.
Reference
Stamp, Josiah (1929). Some Economic Factors in Modern Life. P. S. King & Son. pp. 258–259.
Turning Analytics into Action
We live in the land of number crunching. Josiah Stamp, an economist and banker of early 20th Century England, was often quoted referring to the census, “The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the village watchman, who just puts down what he damn pleases.” While Stamp did participate in gathering data, he really wasn’t clear about why. He could never have imagined the advantages we have today ― computers, cell phones, and remote data gathering. The beauty of which is that the same computing power that makes data-gathering more powerful allows us to build out systems that interpret and analyze the data.
Common Controls
The National Institute of Standards and Technology (NIST) generated the Risk Management Framework, which repeatedly references something called “common controls” of information systems. Common controls are built-in cybersecurity safeguards that are embedded in many of our software packages. They limit back doors for hackers. They create protections that span multiple systems and software. For all intents and purposes, they are built-in governance.
The beauty of common controls is that they represent active, vital approaches to ensure that our systems function well. They serve that purpose in the background. They don’t trumpet that accomplishment every time a cyberthreat lurches over the horizon. They just do the job. It’s the job of analytics in action.
Why do common controls work? Three key aspects to them point us all in the right direction of how to move analytics to action:
Objective and Consistent Analytics
Consistency is a key for how we treat analytics when we’re managing them well. For example, we understand that 27 is a number. We all look at 27 roughly the same way. It’s the one that’s after 26 and before 28. That’s great, as long as we treat it that way. But the moment we ascribe personal, subjective values to that number, it’s no longer consistent. Twenty-seven years old could be interpreted as young by some and maturing by others. Would having $27 in your pocket make it a good day or bad?
If an action will follow your analytics, stay objective and consistent.
A Systematic Approach
We encounter error messages every day. Warnings. Alerts. Sometimes we heed them, and sometimes we struggle to turn them off. When we literally have analytics at our fingertips, we need to treat our response with that same level of consistency. Any time the budget is over by x%, we should spit out a consistent response. We don’t need to re-invent a lot of wheels. The fact that the analytics come in and we know precisely how to respond makes our lives easier. But it also makes life easier for those around us. They know our reactions before we have them. It becomes a system.
That systematic approach benefits everyone concerned. If someone has a problem with the response, we can readily point to the data and the analysis to ask where it may have gone astray. There’s no need to kick into self-defense mode. The data drives the action.
It’s important, however, not to fall into Josiah Stamp mode. He doubted his data and its sources. If our data comes into question, our actions will as well. However, if we value our datasets and consistently devise valid action and interpretation approaches, everyone comes out ahead. And the actions that ensue? They’re virtually automatic. Again, consistency is key.
Reference
Stamp, Josiah (1929). Some Economic Factors in Modern Life. P. S. King & Son. pp. 258–259.
Carl Pritchard
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