The Six Stages of the Analytics Process

The analytics process can be broken down further into six distinct stages:

1. Discovery
2. Exploration
3. Modelling
4. Projection
5. Actionable Insight
6. Monitoring

Stage 1: Discovery

The discovery stage starts with a dialogue between the analyst and decision maker to ensure that the analyst understands the purpose of the project. Particular attention is paid to the specific decisions for which the project is intended to provide an evidential basis to support management decision making.

The starting point for all analytics projects is discovery. The Discovery stage involves a dialogue with the project sponsor to understand both Purpose (i.e. what is expected from the project?) and Context (i.e. what is already known?). The outcome of discovery is Framing the practical management problem facing the decision-maker as an analytical problem amenable to data analysis. It is crucial to ensure that the analytical problem is feasible given the available data.

Stage 2: Exploration

The exploration stage involves data preparation particularly checking the quality of the data and transforming the data if necessary. A key part of this exploration stage is the preliminary assessment of the basic properties of the data to decide on the appropriate analytical methods to be used in the modelling stage.

Having determined the purpose of the analytics project and sourced the relevant data in the initial Discovery stage, there is a need to gain a basic understanding of the properties of the data. This exploratory data analysis serves a number of ends:

• It will help identify any problems in the quality of the data such as missing and suspect values.
• It will provide an insight into the amount of information contained in the dataset (this will ultimately depend on the similarity and variability of the data).
• If done effectively, exploratory data analysis will give clear guidance on how to proceed in the third Modelling stage.
• It may provide advance warning of any potential statistical difficulties.

A dataset contains multiple observations of performance outcome and associated situational variables that attempt to capture information about the context of the performance. For the analysis of the dataset to produce actionable insights, there is both a similarity requirement and a variability requirement. The similarity requirement is that the dataset is structurally stable in the sense that it contains data on performance outcomes produced by a similar behaviour process across different entities (i.e. cross-sectional data) or across time (i.e. longitudinal data). The similarity requirement also requires that there is consistent measurement and categorisation of the outcome and situational variables. The variability requirement is that the dataset contains sufficient variability to allow analysis of changes in performance but without excessive variability that would raise doubts about the validity of treating the dataset as structurally stable.

Stage 3: Modelling

The modelling stage involves the construction of a simplified, purpose-led, data-based representation of the specific aspect of real-world behaviour on which the analytics project will focus.

The Modelling stage involves the use of statistical analysis to construct an analytical model of the specific aspect of real-world behaviour with which the analytics project is concerned. The analytical model is a simplified, purpose-led, data-based representation of the real-world problem situation.

• Purpose-led: model design and choice of modelling techniques are driven by the analytical purpose (i.e. the management decision to be impacted by the analysis)
• Simplified representation: models necessarily involve abstraction with only relevant, systematic features of the real-world decision situation included in the model
• Data-based: modelling is the search for congruent models that best fit the available data and capture all of the systematic aspects of performance

The very nature of an analytical model creates a number of potential pitfalls which can lead to: (i) misinterpretation of the results of the data analysis; and (ii) misleading inferences as regards action recommendations. There are three principal pitfalls:

• Generalisability: analytical models are based on a limited sample of data but actionable insights require that the results of the data analysis are generalisable to other similar contexts
• Excluded-variable bias: analytical models are simplifications of reality that only focus on a limited number of variables but the reliability of the actionable insights demands that all relevant, systematic drivers of the performance outcomes are included otherwise the results may be statistically biased and misleading
• Misinterpreting causation: analytical models are purpose-led so there is a necessity that the model captures causal relationships that allow for interventions to resolve practical problems and improve performance but statistical analysis can only identify associations; causation is ultimately a matter of interpretation

It is important to undertake diagnostic testing to try to avoid these pitfalls.

Stage 4: Projection

The projection stage involves using the estimated models developed in the modelling stage to answer what-if questions regarding the possible consequences of alternative interventions under different scenarios. It also involves forecasting future outcomes based on current trends.

Having constructed a simplified, purpose-led model of the business problem in the Modelling stage, the Projection stage involves using this model to answer what-if questions regarding the possible consequences of alternative interventions under different scenarios. The use of forecasting techniques to project future outcomes based on current trends is a key aspect of the Projection stage.

There are two broad types of forecasting methods:

• Quantitative (or statistical) methods of forecasting e.g. univariate time-series models; causal models; Monte Carlo simulations
• Qualitative methods e.g. Delphi method of asking a panel of experts; market research; opinion polls

Stage 5: Actionable insight

During this stage the analyst presents an evaluation of the alternative possible interventions and makes recommendations to the decision maker.

Presentations and business reports should be designed to be appropriate for the specific audience for which they are intended. A business report is typically structured into six main parts: Executive Summary; Introduction; Main Report; Conclusions; Recommendations; Appendices. Data visualisation can be a very effective communication tool in presentations and business reports and is likely to be much more engaging than a sets of bullet points but care should be taken to avoid distorting or obfuscating the patterns in the data. Effective presentations must have a clear purpose and be well planned and well-rehearsed.

Stage 6: Monitoring

The Monitoring stage involves tracking the project Key Performance Indicators (KPIs) during and after implementation.

The implementation plans for projects should, if possible, have decision points built into them. These decision points provide the option to alter the planned intervention if there is any indication that there have been structural changes in the situation subsequent to the original decision. Hence it is important to track the project KPIs during and after implementation to ensure that targeted improvements in performance are achieved and continue to be achieved. Remember data analytics does not end with recommendations for action. Actionable insights should always include recommendations on how the impact of the intervention on performance will be monitored going forward. Dashboards can be a very effective visualisation for monitoring performance.

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