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From Systems of Record, thru Systems of Engagement to Systems of Action


In my last blog post, I introduced the idea of systems of action and how this breaks our “one type and location of compute power does it all”.

In this post, I’ll look at how systems of action compare them to what has gone before - systems of record and systems of engagement.

Let’s start with Systems of Record.

Systems of Record

With systems of record, the data is structured and nicely defined, by humans, for easy processing.

How the processing on that data is performed is defined by humans – thru our computer programs.

And the amounts of data we process in systems of record are “small” (small in comparison with that’s about to come). For example, as of 2015, Walmart stored 30 petabytes of shopping information.

Transactions need to be processed in a timely manner.

And accuracy of processing matters very much indeed.

Systems of Engagement

With systems of engagement, the data is human interaction data - photos, texts, vidoes, tweets.

The amount of data processed by social media systems is higher than that for structured data. For example, by May 2016, Facebook was processing 4 petabytes of data a day. While this is high, it’s actually only 4KB per active Facebook user.

This data is messy from a computer’s point of view – interpretation of the human interaction steam (e.g. text, voice) is required before we can get computers to process it.

But processing is not time-critical and accuracy is not that crucial. For example, if we “lose” a tweet or display some status posts in the wrong order, it’s not critical.

When social media started some 13 years ago, all of the processing was human-defined. However, companies like Facebook and LinkedIn now use some machine learning. In other words, machine learning systems learn how to process the data based upon past experience.

Systems of Action

With Systems of Action, we’re about to take another leap in magnitude in the amount of data we’ll be processing. For example, just one self-driving car will generate 4 terabytes of data a day

Data, per day, from a self-driving carData, per day, from a self-driving car

That’s just one car. There will be 10 million of them by 2020 - 40,000 petabytes a day. And that’s just self-driving cars. We’ll be generating such levels of data from IoT sensors all over our “physical world”.

Why so much data? Because we are moving to an era where we sense reality. And, unlike structured data and social media data, reality is not digital. We need to create a digital version our analog reality. That takes a lot of data.

This data is “messy” from a computer’s point of view because it is our digitization of an analog reality – video, pressure, sounds, vibrations, etc. There is no structured record to tell a computer that it is looking at a face. This has to be inferred.

Very often, the rules as to how to process the data are “machine learnt” – self driving cars get better the more they drive because they learn.

Timely processing of this data – inferring “what’s going on” from the data – is often essential. If the system can’t infer that my car is heading towards a stop sign in a timely manner, I’m in trouble.

Accuracy is important too. If my self-driving car drives “approximately between the white lines”, it will probably crash.

So, with systems of action, the amount of data is usual high. The processing is tough because analog reality has to be interpreted and processing must be timely and accurate.

Systems of Record, Engagement and ActionSystems of Record, Engagement and Action 

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Mike Shaw
Director Strategic Marketing

linkedin.gifMike Shaw

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About the Author


Mike has been with HPE for 30 years. Half of that time was in research and development, mainly as an architect. The other 15 years has been spent in product management, product marketing, and now, strategic marketing. .

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