View source for Being a data driven organization

= Being a data driven organization =
''David Taylor, notes by Josh Levinger (Citizen Engagement Lab)''

'''what can we learn from obama campaign?'''
* they learned it all from silicon valley

'''what do we mean by data?'''
* raw chunks, metrics, informed decision making
* dashboards

'''how do we act on data we collect?'''
* only produce analytics that are actionable (vs reportable?)
* vanity metrics are killing startups (clicks, hits, users)
* actionable ones key (cost per acquisition, revenue per user, dropoff rate)
* very similar to member-funded orgs (dollars raised per user, value provided to them)
* *	lead generation -> conversion -> lifetime value
* pirate metrics talk 

'''what is most central metric? just actions, dollars?'''
* twitter used avg engagement per user per day in their early days)
* campaigns could use virality, or growth velocity (recruitment)
* ability of people to spread your message (shares)

'''knowing what your goal is as organization'''
* what are we trying to do in the world?
* at the end of the day, what do we care about?

'''most of the things that are measurable are best for lead conversion'''
* 90% of what we can measure are vanity metrics
* define qualitative metrics / proxies for real world engagement (clicks vs higher bar asks)
* move people up the engagement ladder

'''fundraising culture is far behind software development in following pace of change'''
* rigidity (obliviousness) vs plasticity
* how do we create pressure on foundations to change?
* some orgs stuck in loop of email list growth & rapid response
** key performance indicators should be balanced against each other
* data collection and decision making systems don't talk to each other
** by data we mean constituents (and their interactions with the org)

'''How do we make this easy?'''
* you need a lot of data before being metrics driven makes sense
* collect everything you can, go back and design what's relevant later
* use automated tools that are built for this (kissmetrics, mixpanel)
** google analytics gives you traffic numbers (vanity metric)
** cohort tracking, not just spikes
* decide on one kpi per channel (+- on baseline, something that indicates health)
** know your baseline, and the sector baseline
* start with goals, work backwards
* * but check that it fits your organization model

'''visualization helps connect between different staff cultures'''
* intelligence dashboards
* chart.io (visually build charts from many data sources)
* make everything transparent, everyone in the organization has access to every graph

'''design tests, knowing most of them will fail'''
* but with limited resources "burning" them can be scary
* phrase a hypothesis vs "fine tuning" or optimization
* when is there an end any more?