Data analytics is the process of analysing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive prioritisation and decision making.
It is a key pillar of lean experimentation, in inspecting the outcome from running an experiment supporting a hypothesis. If the data sources points
A key aspect of data analytics is attaining a suitable confidence interval(statistical significance) in the interpretation and validation of the summary analysis, to then drive subsequent decisions. This technique is focused on descriptive and diagnostic analytics
👥Who
One of Data Analyst or Product Manager or Service Designer, or combinations thereof.
🛠 Running the technique
Undertake a data audit against the key metrics that matter for the prioritised hypotheses and associated experiments so you can understand what data is captured, where it is captured and how to access it.
Typical data sources include: Google Analytics for web based services, back office tools, customer support tools, 3rd party application providers, specialist experience management or product analytics tools (e.g. Qualtrics, Qualaroo, Heap, Amplitude)
Inspect the data to ascertain the efficacy of both the raw data and how it is surfaced for interrogation by your team members.
Where there is significant knowledge gaps for a current service, create a visual mapping on top of a service blueprint or customer journey map to identify the data source, metrics supported, level of confidence in its efficacy
Work with the data analyst or architect or developer to identify the smallest effort to bridge the gap to attaining the metrics needs to complete the validation phase
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