Community Commons’ Post

In community change work, we are often called upon to use data for different circumstances—to assess community conditions, monitor trends, and prioritize areas for investment. Gathering, organizing, and evaluating community data is one step in the process; using that data to create policy and system change is next. Detailed information accessed through publicly available data dashboards, indexes, and/or mapping tools can make a profound difference through informing policy and system change that builds on community assets and addresses gaps in resources and/or supports. Explore the second installment of the Our Favorite Data Sources series which highlights data sources that are great for browsing and provide sensemaking and context to support data interpretation here: https://bit.ly/3Lgtd75 #DataSources #DataDashboard #DataInterpretation [Image description: A blue rectangle contains several logos of organizations and data sets along with text that reads "Our Favorite Data Sources Part II". A green banner spans the top right corner and contains white text that reads "CC Original". https://bit.ly/4cCyuBO is listed at the bottom.]

For #MetricsMonday, Let's talk about how a Metric Abstracts Data. When something happens, we make a "count" that something is occurring. Then we ascribe a "value" to that action. Why do we care? After this, we can mix/match these various counts into a formula, and from there, we have created for ourselves a toolbox that answers questions. Unfortunately, the higher up the layers go, the further and further away we get from the people who acted in the first place. And this is the nature of the "abstraction" problem in #communitymanagement. For traditional "deductive" processes, these metrics are easily collated and stable. But communities don't work that way. Community actions such as sentiment and reason are "inductive," meaning a LOT more layers of abstraction are needed before you can determine ROI. So the name of the game, dear community managers, is to find ways to abstract community data in as few layers as possible while staying accurate and answering questions. I've created an ENTIRE framework to do just that in my free introductory metrics course, "The 3 WIFM Dashboard Approach to Measure Your Community Data | SC.O" Click here to access it! https://lnkd.in/emXkHPi4

Thank you to FeverBee - Community Consulting (especially to Richard Millington ) for being a valuable source of knowledge in my journey to master community management, both effectively and measurably. Yes, you heard that right -- measurably. I have helped build communities from the ground up, ranging from high school students to expectant moms (we have terms : Mums). Interaction within the community is crucial as it keeps a community manager updated on trending discussions, helps better understand community needs, synthesize community values, and uncover a lot of potentials. Since 2022, I have always sought to extract actionable data from the online communities where i am active. Raised on the mantra of "mana datanya?" or "ada datanya ngga?" In 2020 and 2021, we managed to extract various data points such as total community members, patterns of community engagement with app features, user preferences, text analysis, popular topics within the community, and much more. Special thanks to my Key Data Collaborator Steven Hasan Haekal Yusril Faizin Sihab and Syifa for realizing my vision of fostering a data-driven mindset to build a more user-centric community.

For #MetricsMonday, Let's talk about how a Metric Abstracts Data. When something happens, we make a "count" that something is occurring. Then we ascribe a "value" to that action. Why do we care? After this, we can mix/match these various counts into a formula, and from there, we have created for ourselves a toolbox that answers questions. Unfortunately, the higher up the layers go, the further and further away we get from the people who acted in the first place. And this is the nature of the "abstraction" problem in #communitymanagement. For traditional "deductive" processes, these metrics are easily collated and stable. But communities don't work that way. Community actions such as sentiment and reason are "inductive," meaning a LOT more layers of abstraction are needed before you can determine ROI. So the name of the game, dear community managers, is to find ways to abstract community data in as few layers as possible while staying accurate and answering questions. I've created an ENTIRE framework to do just that in my free introductory metrics course, "The 3 WIFM Dashboard Approach to Measure Your Community Data | SC.O" Click here to access it! https://lnkd.in/e5P_BKmg

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