Mind+machine, Analytics

Does More Data Really Mean More Insight?

The number of incredible opportunities with great potential for mind+machine is large and growing. Many companies have already successfully begun leveraging this potential, building whole digital business models around smart minds and effective machines. Despite the potential for remarkable return on investment, there are pitfalls—especially if you fall into the trap of believing common wisdoms like “more data means more insights”.

If you want to get the most out of your organization’s data, you need to have a really clear understanding of what successful analytics means. It’s not about getting data; it’s not even just about getting insights. Successful analytics means that the right decision maker gets the right insight at the right time and in the right format. Anything else means a lessened impact of all that data gathering and analytics—and that is a truly unsatisfactory experience for all involved.

My favorite analogy is food service: success in a restaurant is getting a delicious portion of the food you actually ordered, presented appropriately and delivered on time. It’s not enough to have a great chef if the food doesn’t reach the table promptly. And the most efficient service won’t save the business if the food is of poor quality.

The impact of analytics on your business should be clear and strong. However, many organizations struggle and fail to get the right return on their investments.

Why is that?

One common failing of analytics is: the more, the better. There is certainly no issue with finding data! Companies can even get overwhelmed if they try to keep pace with the rapid expansion in information sources and data repository possibilities. More data and more computing power are not the sole solution to all analytics problems: the human element cannot be underestimated.

I vividly remember my first year at McKinsey, where one of my first projects was a strategy study in the weaving machines market. I was really lucky to discover around 40 useful data points and some good qualitative descriptions in the 160-page analyst report created by our excellent library team. We also conducted 15 qualitative interviews and found another useful source.

By today’s standards, the report provided a combined study-relevant data volume of 2–3 kilobytes, but we used this information to create a small but robust model. It’s a perfect proof that with the right combination of mind+machine, valuable insight can be derived even from small amounts of data.

For more details on the fallacies surrounding data analytics and how to achieve the best results using mind+machine, order Marc Vollenweider’s book – Mind+Machine: A Decision Model for Optimizing and Implementing Analytics – here.

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