The five data stakeholders

When it comes to data stakeholders (people / processes / systems / interfaces / etc), I like to think of them in five categories:

  1. Creators – The primary data creation / collection source, which could be people or machines
  2. Ingestors – The stakeholders that take the source data and compile it into a repository such as a database
  3. Curators / Librarians – The stakeholders that manipulate / reconcile / correlate data in the repository
  4. Presenters – The stakeholders that take data from the repository and distill it for presentation to others
  5. Consumers – The stakeholders that consume the data that by now is hopefully turned into information / insights that are useful enough to do something with

Any given stakeholder might fit into one or more of the categories above BTW.

In OSS, we spend a lot of our time and effort on the first four steps. Ultimately though, it’s the final step that is the most important. If all the steps don’t result in purposeful consumption, then they are effectively wasted. All other data is superfluous to needs.

We perhaps don’t always start with the end in mind – identifying the consumers that need their information curated, distilled and delivered (without asking) in a time-frame that suits their needs. These consumers are usually to pivotal to need the distraction of the irrelevant and don’t care about the preceding four steps (unless there is a gap in the pipeline that impacts their ability to get the right information at the right time).

The question becomes how we figure out what IS relevant. The answer is probably obvious – spending more time with the consumers to understand and refine what they need presented to them. But from the perspective of being proactive, it also means understanding the decision-making process of their role well enough to gather additional, vitally concise information to push to them.

Oh, by the way, there’s one really important point that’s missing from this five-step plan – the entire feedback loop that looks at:

  1. The actions coming from the consumed data / information
  2. The results coming from those actions
  3. Determining how results could be improved through refinement of any / all of the previously mentioned steps in the data stack. This is the hardest, but most valuable part of the entire chain

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