How continuous improvement will change by applying BIG data

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Continuous versus traditional data analysis

Advising customers on effective operations improvement and test-preparation requires a continuous and instantaneous analysis of data. In traditional decision support situations, an analyst takes a pool of data, sets it aside for analysis, comes up with a model, and advises the decision maker on the results. However, with big data, the data equals not so much a distinct piece of a data pool, it is more an ongoing, fast-flowing stream. Big data suggests that a more continuous approach to sampling, analyzing, and acting on data is necessary.

This is in particular an issue for applications involving ongoing monitoring of IT operations e.g. within a service integrator. One potential problem with such monitoring applications is the tendency for managers to view a continuing stream of analysis and reports without making any decisions or taking any action.

‚Management by Exception‘ is key

For ongoing monitoring work in the context of a shift from iterative to continuous data analysis, there should be processes agreed between the different parties of a value network for determining when specific decisions and actions are necessary—when, for example, warning levels or error report data fall outside certain limits. This management principle is called Management by Exception (MBE) and I believe this will be a more and more important aspect of business intelligence compared to traditional management concepts like ‘management by objectives’ (MBO). Management by exception simply means we focus on anomalies. But with Big Data we might also want to start thinking about cohesive pools of anomalies that must be categorized across providers. Concretely we need to define what exception will have information, warning or error indication character. We still need to analyze those pools of anomalies over time to trigger the right actions of the right teams.

Security Management as a use case for big data

Big Data fruits in the overall ecosystem

Big Data fruits in the overall ecosystem

In a mature Service Management organization one potential application of big data would be the monitoring of data provided by a set of key service suppliers and this way creating a social network for hypothesis formation, evidence collection, and collective decision-making. The idea is that as data begin to reveal a trend or finding—say, for example, problem management data suggesting a security breach that could lead to service downtime —an analyst on the line would post the thread and the data on which it was based, and suppliers could weigh in with new analyses and data. Such suggestive hypotheses have been described as “digital smoke signals”. One goal is to determine how likely the thread is to be worthy of detailed analysis and action. But what is more important in my view is that the idea that a service integrator would have a system for circulating data-driven problem indications across suppliers marks a major change in that organization’s culture.

Conclusion

Whether the analysis and decision processes are social within a value network or individual within a service integrator’s value chain, the continuing stream of big data suggests that IT organizations need to think about new ways of making decisions. If it’s worth investing in the collection and analysis of big data, it’s also worth thinking about how the outcome will have an impact on decisions and actions.

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