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| Graphical Representation Series |
In a diagram adapted from an older article [1], Brent Dykes, the author of "Effective Data Storytelling" [2], makes a parallel between Data Analytics and marathon running, considering that an organization must pass through the depicted milestones, the percentages representing how many organizations reach the respective milestones:
It makes sense, isn't it? On the other side the devil lies in details and frankly the diagram raises several questions when is compared with practices and processes existing in organizations. This doesn't mean that the diagram is wrong, just that it doesn't seem to reflect entirely the reality.
The percentages represent author's perception of how many organizations reach the respective milestones, probably in an repeatable manner (as there are several projects). Thus, only 10% have a data strategy, 100% collect data, 80% of them prepare the data, while at the opposite side only 15% communicate insight, respectively 5% act on information.
Considering only the milestones the diagram looks like a funnel and a capability maturity model (CMM). Typically, the CMMs are more complex than this, evolving with technologies' capabilities. All the mentioned milestones have a set of capabilities that increase in complexity and that usually help differentiated organization's maturity. Therefore, the model seems too simple for an actual categorization.
Typically, data collection has a specific scope resuming to surveys, interviews and/or research. However, the definition can be extended to the storage of data within organizations. Thus, data collection as the gathering of raw data is mainly done as part of their value supporting processes, and given the degree of digitization of data, one can suppose that most organizations gather data for the different purposes, even if only a small part are maybe digitized.
Even if many organizations build data warehouses, marts, lakehouses, mashes or whatever architecture might be en-vogue these days, an important percentage of the reporting needs are covered by standard reports or reporting tools that access directly the source systems without data preparation or even data visualization. The first important question is what is understood by data analytics? Is it only the use of machine learning and statistical analysis? Does it resume only to pattern and insight finding or does it includes also what is typically considered under the Business Intelligence umbrella?
Pragmatically thinking, Data Analytics should consider BI capabilities as well as its an extension of the current infrastructure to consider analytic capabilities. On the other side Data Warehousing and BI are considered together by DAMA as part of their Data Management methodology. Moreover, organizations may have a Data Strategy and a BI strategy, respectively a Data Analytics strategy as they might have different goals, challenges and bodies to support them. To make it even more complicated, an organization might even consider all these important topics as part of the Data or even Information Governance, or consider BI or Analytics without Data Management.
So, a Data Strategy might or might not address Data Analytics at all. It's a matter of management philosophy, organizational structure, politics and other factors. Probably, having a strayegy related to data should count. Even if a written and communicated data-related strategy is recommended for all medium to big organizations, only a small percentage of them have one, while small organizations might ignore the topic completely.
At least in the past, data analysis and its various subcomponents was performed before preparing and visualizing the data, or at least in parallel with data visualization. Frankly, it's a strange succession of steps. Or does it refers to exploratory data analysis (EDA) from a statistical perspective, which requires statistical experience to model and interpret the facts? Moreover, data exploration and discovery happen usually in the early stages.
The most puzzling step is the last one - what does the author intended with it? Ideally, data should be actionable, at least that's what one says about KPIs, OKRs and other metrics. Does it make sense to extend Data Analytics into the decision-making process? Where does a data professional's responsibilities end and which are those boundaries? Or does it refer to the actions that need to be performed by data professionals?
The natural step after communicating insight is for the management to take action and provide feedback. Furthermore, the decisions taken have impact on the artifacts built and a reevaluation of the business problem, assumptions and further components is needed. The many steps of analytics projects are iterative, some iterations affecting the Data Strategy as well. The diagram shows the process as linear, which is not the case.
For sure there's an interface between Data Analytics and Decision-Making and the processes associated with them, however there should be clear boundaries. E.g., it's a data professional's responsibility to make sure that the data/information is actionable and eventually advise upon it, though whether the entitled people act on it is a management topic. Not acting upon an information is also a decision. Overstepping boundaries can put the data professional into a strange situation in which he becomes responsible and eventually accountable for an action not taken, which is utopic.
The final question - is the last mile representative for the analytical process? The challenge is not the analysis and communication of data but of making sure that the feedback processes work and the changes are addressed correspondingly, that value is created continuously from the data analytics infrastructure, that data-related risks and opportunities are addressed as soon they are recognized.
As any model, a diagram doesn't need to be correct to be useful and might not be even wrong in the right context and argumentation. A data analytics CMM might allow better estimates and comparison between organizations, though it can easily become more complex to use. Between the two models lies probably a better solution for modeling the data analytics process.
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