Big Data vs Intelligent Data (and what Startups can do with it)

Depending on your perspective, big data is probably the coolest or most annoying buzzword of the last year (though growth hacking definitely gives it a run for its money).  That said, on a personal level, I’ve wondered how applicable “big data” is to your typical web startup (and directly to what I’m doing everyday at Klaviyo).

I’ve come to believe that for most companies today, it’s more important to have “intelligent” data (taking a huge amount of data and reducing it to the right data) rather than “big” data (being able to analyze all of the data you have).

A few reasons that “big” data may not actually matter for many use cases:

  1. Statistics mean we can draw very good conclusions from a subset of the data. Having a million data points isn’t necessarily that much better than 10,000.
  2. Taking action requires that we reduce complexity – recommending a different action for each of a million customers may not workable (or equally likely it doesn’t provide enough incremental value to make it worth dealing with). If we can’t translate data and analyses into actions (that we wouldn’t have taken previously), then they aren’t useful.
  3. Access to more types of data (particularly new data sets) means we can be highly selective about what data to base our actions off of. A great example is calculating a customer’s happiness (and ultimate likelihood of churning). Because I can use API’s to pull together email, support and usage data to easily combine data into a single model, I can actually make a better prediction with fewer variables needed.

What is Intelligent Data

I’d offer a few specific criteria for what makes intelligent data:

  1. Data that is clear and unambiguous – i.e. the data values can be defined and measured in a repeatable fashion
  2. Data that is concise – i.e. the data represents the smallest number of data points that would lead to the same action. If you need 90% certainty to take action, it’s the amount of data that will safely give you that.
  3. Data that is directly linked to action – i.e. based on different values of that data, different decisions will be made and implemented.

In short, intelligent data is data that is a direct input to analysis – and very specifically to the right analysis needed to decide between decision A or B.

How Startups can use Intelligent Data

There are numerous web applications actively helping companies use intelligent data. Unbounce or Myna (for A/B testing), Hubspot (for marketing analytics), Klaviyo (for user management and marketing), and countless others.  The key for anyone to take advantage of intelligent data is to think clearly about what data means and what you’re going to do with it.  Only in the doing does data actually start to matter.

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Try Klaviyo today to see how we help you use intelligent data to help you manage and market to your customers.

 

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4 comments

  • Couldn’t agree more with the distinction between “big data” and “intelligent data”. That is our working assumption for Wolfram|Alpha Pro (http://blog.stephenwolfram.com/2012/02/launching-a-democratization-of-data-science/)

  • but does it have pie charts

  • the key is to make sense of data whatever data is used. This is the most important element of data science.

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