Why does LinkedIn feed me with big data hype from 2011?

All  meaningful automation, quality, energy efficiency, and resilience metrics are obliterated by translating into dollars. Good business decisions are made by understanding the implications of domain-specific metrics:

1. Level of automation
2. Volume of undesirable waste
3. Energy use
4. Reliability and other quality of service attributes

Any practitioner of Kaizen knows that sustainable cost reductions are the result of improvements in concrete metrics that relate directly to the product that is being developed or the service that is being delivered. The same domain expertise that is useful for Kaizen can be combined with high quality external big data sources to produce insights that enable radical innovation.

Yes, often the results have a highly desirable effect on operating costs or sales, but the real value can only be understood in terms of native domain metrics. The healthcare domain is a good example. Minimising the costs of high quality healthcare is desirable, but only when patient outcomes and quality of care are not compromised.

When management consultants only talk about results in dollars, there is a real danger of only expressing goals in financial terms. This then leads down the slippery slope of tinkering with outcomes and accounting procedures until the desirable numbers are within range. It is too late when experts start to ask questions about outcomes, and when lacking native domain metrics expose reductions in operational costs as a case of cutting corners.

Before believing a big data case study, always look beyond the dollars. If in doubt, survey customers to confirm claims of improved outcomes and customer satisfaction. The referenced McKinsey article does not encourage corner cutting, but it fails to highlight the need for setting targets in native domain metrics, and it distracts the reader with blunt financial metrics.