Here’s what… so what, now what?

For museums digging into the world of big data and analytics, the first focus is often on getting touch free dashboards and reporting in place, which certainly deliver quick win value in cost and time savings. Though speedy and easy data is important, once these challenges are addressed, the museum’s attention should turn squarely to deriving exponential value from the insight created by this new capability. In the data driven museum, this mantra will sound like “here’s what… so what, now what?”

Finding a compelling way to communicate and lead with data is an essential skill set for any modern executive, especially true in a cultural sector constantly challenged to do more with less. This mantra is equally applicable for achieving in a management session, all hands town hall, or donor development meeting. Distilled, it is about persuasively conveying facts, meaning and action. This three part mantra can equally be used to structure a delivery as well as elicit analytical thinking from others.

Step 1. Here’s what: telling a story with data

Given math and creativity require polar skills, telling a story with data can be hard to master (often easier to accomplish as a team). Once you’ve decided upon your focus topic, the first step in data storytelling is to describe what is happening. What are the key data points and how are they trending? What else was going on at the time? Was what happened expected or surprising?

Once a compelling narrative is decided upon, to be presented effectively, it needs to be:

  1. Tailored to the audience’s seniority, topic familiarity and data confidence
    Usually, this means starting top down and providing the audience with an overview of the story’s place within the museum environment, before diving in. Starting bottom up with a data point is confusing and misses the opportunity to command attention before trying to persuade.
  2. Clear in its strategic alignment
    Does the story speak to the museum’s visitation, or visitor experience? Is it regarding exhibition programming? Is it about operational efficiency? Create a foundation that links the story’s relevance with the museum’s mission, strategy and key performance indicators to explain its importance.
  3. Connected to a human link
    To present a balanced view and appeal to various listener styles, include an example interest story, feedback in the visitor’s own words or qualitative information. This connection might also include countering popularity with inclusivity, relating to the museum’s educational mission or encouraging open participation in debate.
  1. Contextually enriched
    Rarely does one piece of information tell a complete story on its own. It is important to include a relevant discussion of what was happening in the museum at the time, or the world around it – such as events, economic conditions, environmental factors and exhibitions. Context will prove out causation over correlation and inspire creative thinking. This task is substantially easier if the museum’s analytics already incorporates multiple data feeds and allows for collaborative notes, rather than resorting to manual addition.
  2. Illustrated for impact
    A good story is a visual one. Well selected, crucially simple visualizations will convey additional information, present alternate perspectives and enforce story takeaways. Visualizations can be author driven and static or interactively reader driven, reinforcing the importance of general staff access into a business ready analytics solution rather than restricting complex tools to analyst use only.

Given data journalism itself is a form of curation, it is imperative the author remain objective. Be aware the dangers in novelties, outliers and archetypes, which can lead to triviality, spurious results and oversimplification. Trends should include a comment on variance, debunking efforts weighed against confirmation bias and forecasts tested for overfitting.

Step 2. So what: getting to insight

But what does it all mean? Numbers alone deliver little. To convey value in data, we have to go one step further to uncover insight. How is our data trending over time? Against our targets, are things good or bad? Stable? Optimal? How do the metrics relate to an outcome, such as meaningful visitor experience? Does correlation equal causation? What are the underlying contributors? Do they indicate levers within the museum’s control, or highlight influences it needs to respond to? What isn’t the data telling us?

The objective here is to focus in on our problem by defining business impact and deriving an economic equation (whether that be financial or an alternate value consideration). What are the ramifications we’re seeing? What is the impact if we don’t act? What are our objectives, our expectations if we do?

This analysis, though it may be comprehensive, should be significantly distilled using the same story telling principles. As much as the analyst may have spent effort in data cleansing, or calculating regression variables, neither mention is appropriate for a business audience. Supporting material can be sufficiently referenced for credibility, but bore or baffle an audience with too much detail and risk losing attention or support.

Step 3. Now what: creating (then measuring) action

Finally, the ultimate challenge for the story teller is to facilitate or evaluate and recommend action. What have we done before that’s worked, or that hasn’t? Would now be any different? What have others done, in our field or in other industries? What have we decided not to do? Using business analysis techniques such as a force field analysis, cost benefit comparison or Harvey Ball illustration, all this must be refined to a succinct summary of innovations and improvements. The selected solution may be a set of actions balancing quick wins with strategic plays. Following an agile and lean method, the story teller should pose the solution as a series of hypothesis to test, complete with expected consequence.

No data driven culture would be complete without measuring the success of these solutions, communicating the results and taking a decision to pivot or persevere – the sin of ‘spray and pray’ (making an uninformed decision) is closely followed by ‘spray and walk away’ (deciding with insight then failing to measure return). Evaluation is what helps take the ego out of decision making – someone’s owned idea is instead an independent experiment to be proven, often what some refer to as a culture where “it’s ok to fail”. This discipline ultimately can be used to measure the return on insight, the value of analytics itself in the museum.

It’s also why analytics should be prioritized in advance of other initiatives during any museum transformation, rather than after the fact: having analytics in place not only helps steer decision making in the right direction, it provides an important baseline to prove success.

At Dexibit, our iterative methodology for establishing a data driven culture begins with deriving what to measure from a strategic objective before analyzing performance, taking action and evidencing value to iteratively build upon – rather than constantly jumping straight to the next best idea. So better yet – here’s what, so what, now what… what’s next?