Extracting Value from Data

As I prepare for a Kafka Summit talk in October 2018, I've started reading a few articles around extracting value from data.

I recently come across these three McKinsey papers:

1. McKinsey Five Fifty: The Data Disconnect

2. Achieving business impact with data

3. Fueling growth through data monetization.

Whilst I greatly respect McKinsey (links to these articles below), here's why I think McKinsey misses the point around the value of Data...

In all three articles, McKinsey assumes Data is a static resource - something that has to be put somewhere in order to be queried or analyzed. This is good traditional database thinking - but it's out dated!

Instead, Data is often most valuable when it is in flow. This mindset shift is relatively simple. Rather than static data in databases, we now have event-streaming platforms which enable us to work with data, or events, in real time.

An 'event' simply means something happened; a booking, a financial transaction, a customer experience online, an invoice, an IoT connected device senses something... the list is endless...

Rather than let the data populate a static database, the data itself can trigger an action or analysis in real-time. In many cases, that offers new value. The Silicon Valley companies get it. Organizations such as; Uber, Ebay, Netflix, Yelp... and more have architected themselves around event-streaming platforms.

If organizations such as McKinsey change their view of how we can work with data, they will turn-on to the real value of data;

  • Business models can be adapted to offer real-time experiences, or

  • New business models can be developed, such as many of those behind the Silicon Valley start-ups.

  • Inefficiencies can be removed and / or risks mitigated, for example fraud can be prevented before a transaction completes, rather than identifying it after the event.

Jay, Confluent's CEO explains this here: https://youtu.be/c8-oUdr2NDY

When I talk about monetizing data and achieving business impact with data, in October, I'll be talking about data, or event, streaming.

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The three McKinsey Articles

For more details read my other blogs.

Also, this article from O'Reilly provides a nice definition of data engineering, which is a key area for those close to event-streaming platforms.

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