Lean Analytics: Why is Big Data so Disruptive?


Big Data technologies have changed the way we collect data, enabling us to handle infinite amounts of data.

In the old model, with relational databases, you first define the schema for what you collect and then put data into that schema, before analyzing it with BI tools. This is how data warehouse and data mining have been used during decades. You first needed to figure out the question, then you collected the data.


Big Data enables you to change that order. Now, you collect unstructured data first, and you ask the question later.


Modern analytics start by collecting everything, and then formulating your question.

This is what Alistair Croll explains in his book and in the slides and charts above.

The new paradigm allows you to search for the “unknown unknowns.” In analytics, most good answers will lead to another question. Data driven decisions depend on the ability to ask better questions and then ask again.

Implications for Business

In times of rapid market changes, what differentiates your business is how fast you experiment, how fast you can ask iterative questions and measure your progress, and how fast you readjust your business based on your learnings.

These are the principles behind the lean startup, and behind innovation. The most important metric for modern companies becomes “how fast does your organization learn?”

Big Data changes the cost of making data-driven decisions. It is an enabler for a more disciplined and empirical thinking about innovation and strategy.  In that sense, Big Data analytics becomes one more enabler for disruption.

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3 thoughts on “Lean Analytics: Why is Big Data so Disruptive?

  1. Arthur

    It is disruptive mostly because Big Data is not that difficult and can run on commodity hardware thus enabling small shops to board the bandwagon.

  2. JoseMCansado

    Thanks Arthur for your comment. I agree, the decreasing cost of the technologies involved is one of the key factors in enabling faster and more flexible analytics.

  3. Doug Thompson

    I would note that we are having commodity hardware , but there is still an issue that diskIO is still slow, even with sata6, SQL2012 is now moving to in memory processing (a 2005 technology), then we have intel developing 32x32x32 core chips with 12mb L2 cache. There are only a few vendors doing in-core Analytics yielding terra bytes of data in seconds. So there are two speeds of Analytics.
    Of course now no reason not to get into BI/Analytics and to make it work for you.
    Do like the article btw.

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