Sunday, October 30, 2011

Big Data Analytics – The McKinsey Mash – Part Two

In Big Data Analytics – The McKinsey Mash – Part One we discussed the potential for “Big Data” to heighten our organization’s competitiveness, enhance competitive advantage, create valuable assets, and improve efficiency and effectiveness of our operations.
This was based on a series of McKinsey articles (and the synthesis thereof which I’m calling a “mash” - that and it’s Halloween in the US, when the "Monster Mash" song is popular), the links for which appear at the bottom of this posting.
How will we be able to bring the benefits of the above to fruition? We will need to focus on our:
·         Business model,
·         Management practices,
·         Human capital practices

A Business Model is a Terrible Thing to Waste
From a business model perspective, Big Data represents “radical transparency”, in which case “companies with proprietary data will be threatened”. If we rely on a model where we have access to lots of information and only share it begrudgingly when the price is right, we could have problems.
This reminds me of an adage I ran across in the social media world – “content wants to be free”. We can see the impact of this already in the recording industry. It seems to me that consultants rely on their knowledge and expertise as a means to secure engagements.
I remember being part of a project where the consultants claimed to have a “best practices database” (though an alum from that institution told me that it was a bunch of hooey - most of their engagements were based on a variant of 5 slide decks).
We are already beginning to experience the personalization of marketing due to data. This is a central ingredient of Google’s model. As an example, I could place on this blog “AdSense” (which is a Google product) that essentially determines why you came to this (awesome!) blog post and place relevant ads (relevant being determined by data - your recent search criteria, web activity, Google plus profile, etc.) in a box to the side.
According to McKinsey, “you need to make a commitment to conceiving of data as a competitive advantage.”, and, somewhat ominously (from a status quo perspective), “…future competitive benefits may accrue to companies that can not only capture more and better data but also use that data effectively at scale”

No Room for Old School
From a management perspective, Big Data is likely to bring about major changes.
Due to sheer granularity, we will be able to test all of our decisions prior to making them, or use “controlled experiments…[to] distinguish causation from correlation”. Big Data may even be able to replace management elements, because it “expands the universe for application of algorithms and machine-mediated analysis”.
In prior posts we have discussed incentives of entrenched power-holders, and how these contribute to less use of hard facts. Big Data may disrupt some of that tendency - “Our research has found a shift from using intuition toward using data and analytics in making decisions.”
Though not without cost. “You have to have a different kind of confidence to be willing to let the data speak…One CEO told me that when he pushed this attitude, he had to change over 50% of his senior-management team because they just didn’t get it.” Uh…later on, dudes!
The message to the rest of us blokes? Get with the program (hmm…pun intended?)!

Is a Data Scientist Born, or Made?
The question of human talent and abilities might very well be the gate-keeper for Big Data. McKinsey says “the harder thing is to get the set of skills”.
What skills, exactly? Among the ones mentioned:
·         “analytical”
·         “a set of attitudes and an understanding of the business”
·         “more to do with sampling methodologies, designing experiments, and working these very, very large data sets without becoming overwhelmed”
·         “creative in seeing patterns and for people who can be entrepreneurial in creating new business opportunities that take advantage of these patterns”
These skills may be very hard to find: “People realize that there is a gap between the current role of statistician or data analyst or business analyst and what they actually want. They are grappling with the set of tools and the set of skills that they need. Across the whole research cycle, it’s a combination of skills that social scientists understand, plus additional programming skills, plus the ability to do aggressive prioritization. And, of course, a good grounding in statistics and machine learning. That collection of skills is difficult to find.”
Based on what has been presented above, I suppose it doesn’t really matter whether you were born with it or you developed it, so long as you got it!

Key Treasury Café Takeaways
Big Data, so long as we are able to obtain or retain a relevant set of capabilities, talents, and mindset amongst our employees, can make a difference in our competitive abilities, though we must be willing to change our management practices in order to utilize.

What percentage of employees will need to be devoted to Big Data in order for the effort to ‘bear fruit’?
How many do you have vs. how many do you need?
Are you able to envision a scenario where competitive advantage in your industry accrues to those able to utilize ‘big data’?

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McKinsey Article Links:


  1. I think a big problem with big data is understanding what you need from it. Look at the wrong combination and you could be missing causal vs. correlational information. I think this is where people with good intuition can help derive what is needed from the data.

  2. Maddie,

    That is an insightful comment. There is a raging LinkedIn discussion about whether analytical people are creative, and I think that coming up with different perspectives to look at the data can in fact be a very creative act, and necessary to achieve the analytical outcome.

    Thanks for the comments