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Erik Hoffmann
Erik Hoffmann
VP Products at Mirror42

KPI Library


Find signals in the midst of noise

Posted over 8 years ago

“Data becomes information only when it informs. For that to happen, we must find a signal in the midst of the noise and study it closely enough to understand it. This takes time. This takes attention. This takes skill. Only when this occurs has something useful entered our minds.”

This is a thoughtful observation by Stephen Few on analysing ‘Big Data’ and data overload. Massive amounts of data become useless for decision making because most of it is noise.

This is not a mere data visualization issue in my opinion. In a lot of business reporting there is just too much data for the brain to understand. It is a lack of deep understanding of the drivers of business success, and being able to focus on those drivers for an extended period of time, looking at trends and influences. Without being distracted by the noise from details, or day to day operations.

This is an art. You do not pick the right business drivers from the start, and these drivers may change as markets, business and technology circumstances change. It requires agility in business and technology thinking.

Businesses — therefore — cannot get away with current BI and datawarehouse implementations for the purpose of analyzing business metrics, because of lack of necessary agility when deploying these kind of reporting solutions. Its output most often provides too much detail, and lacks the ability to quickly anticipate on change. Businesses require a different mindset on business metric reporting, and a different technology approach.

In my view, monitoring business metrics requires a top-down approach in which metrics that make sense analyzing are identified, instead of the data-driven bottom-up approach that most BI solutions provide. And it requires a system that provides statistical trend information for those business metrics to gain perspective, instead of detailed data sets.

Such an approach should disconnect business metric monitoring from the underlying data sources, so it is flexible in times of change. Data becomes information not only when it informs — but when it also informs at the right time.

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