Like Rome, Data Culture isn’t built in a day. It is a gradual process that calls for changes in habits, attitudes, staff and even resources. But, once established, it becomes a lot easier to sustain and grow a nonprofit’s true impact.
Whole Whale has identified three key components for building a Data Culture and outlined the role that each play in the process. While each component is individually necessary, like Captain Planet, their powers combined are far more powerful. Plus it might even let you pull off green hair and red tights like a boss.
People are the key to building and sustaining a Data Culture. The thing about a culture is that everyone in the organization must be a part of it. People at all levels must recognize the importance of embracing data, and using an analytic approach to decision-making. CEOs, for instance, must lead by example through showing that they use data- and not simply experience or instinct- in shaping strategy. Wherever possible, people should appreciate the need to collect measurable data, and understand how the data can translate into action. Data analysis, in other words, shouldn’t be something that scares anybody.
If your organization has a data analyst, she should not be quarantined in a quiet cubicle to prepare bar charts for powerpoint presentations. In a data culture, the role of the data analyst is much more involved and multi-faceted than that. She should be continuously communicating with other members of the organization. She should be championing a data-driven mindset and analytic approach. She should be triumphed as a sage. Have your analyst hold weekly “office hours” where anyone in the organization can get consultations for anything data-related. At the very least, the data analyst should have good working relationships with members of an organization, and not simply be a number crunching hermit.
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Some questions to ask:
- What is the staff structure as it relates to data reporting?
- Do staff members have the training they need to understand relevant data?
- Do staff members understand how to glean insights and actionable steps from data?
- Do staff members have good working relationships with data analysts?
The scientific method has been used for over 1,000 years as a way to build knowledge about the world over generations through a consistent iterative process. In a labor market where the average employee spends less than 5 years at a job, it is important to have strong structures in place to institutionalize data knowledge. Data insights and findings from past years should be maintained for posterity to inform later employees. Maintaining records of prior decision-making also saves time when similar issues arise in the future.
The #1 killer of good data structures are Data Fiefdoms – these are teams or people that create needless silos around data or tools. These fiefdoms can cause bottlenecks in the process of accessing or storing data for the entire organization. In an ideal process, every member of the team can access the relevant data they need. The flow of data does not stop at the data analyst, but reaches members at all levels of the organization.
In a data culture, it is important for workers to adopt the “Build-Measure-Learn” philosophy espoused by Dan Ries in The Lean Start-Up. Members should be asking themselves, “how can I measure the success of this product”? They should know not only how to seek out relevant metrics, but also appreciate why doing so is an effective strategy. And, most importantly, they should understand how to learn from the data. This entails utilizing the feedback provided by the data to make neccessary changes and inform decisions.
Some questions to ask
- Are staff accessing and communicating data across teams well?
- Do staff act on data or regularly share learnings from experiments?
- Are goals set in a way that can be tracked through metrics?
- Does the organization use a Gather<Analyze<Insight method?
- How often do staff receive data feedback?
You can’t fly astronauts to the moon without a good spaceship. In the same light, there are numerous tools and services that can help support a data culture and embody the build-measure-learn approach. Fortunately, many are simple to master and do not require an advanced degrees in statistics. These tools allow people, in all departments, to incorporate an analytic approach to their work.
Constant Contact, for instance, is a web service that the content team can use for e-mailing clientele. It allows you to test out different subject lines with your audience, and see which one leads to the highest open rate. Very intuitive. Very fun.
A similar tool that can be used by web designers is offered by Optimize.ly. This service allows you to experiment with different content and layouts on your web pages and compare how they perform. You pick the metrics that are important to you, and the site will help show you how to optimize your page. Effectively, it replaces guesswork with data.
These tools and other ought to be supported by the HiPPO and other members of the team. It lets your organization know that you are serious about building a data culture, and are equipping them with the necessary tools.
Some questions to ask
- Are tools in place to analyze large data sets (beyond Excel)?
- Are consistent naming and storage conventions in place across databases?
- Are dashboards and metrics updated as automatically as possible?
- Is data stored in a way that reporting can be done across the organization?
- Are semi-annual security audits and passwords changed?
If you build it, they will come. But, remember to always be wary of those pesky destroyers of data culture!