I learnt about DATA STORYTELLING.
It is communicating the meaning of a dataset with visuals and a narrative that are customized for each particular audience. There are 3 data story steps.
đź’ Engage your audience
đź’ Create compelling visuals
đź’ Tell the story in an interesting narrative.
SPOTLIGHTING is scanning through data to quickly identify the most important insights.
DASHBOARDS is a tool that organizes information from multiple datasets into one central location for tracking, analysis and simple visualization through tables, charts and graphs.
LIFE AND STATIC INSIGHTS
To identify whether data is live or static depends on certain factors.
 - How old is the data?
 - How long untill the insights are stable or no longer valid to make
decisions
 - Does this data or analysis need updating on a regular basis to remain
valuable?
➡️ STATIC DATA: Involves providing screenshots or snapshots in presentations or building dashboards using snapshots of data. There are PROS
and CONS to Static data.
â• PROS
 - Can tightly control a point-in-time narrative of the data and insights.
 - Allows for complex analysis to be explained in depth to a large audience
â• CONS
 - Insights immediately begins to lose value and continues to do so the l
longer the data remains in a static state
 - Snapshots cant keep up with the pace of data change
➡️ LIVE DATA: Means that you can build dashboards, reports and views
connected to automatically updated data.
â• PROS
 - Dashboards can be built to be more dynamic and scalable
 - Gives the most up to date data to the people who need it at the time
when they need it
 - Allows for up-to-date curated views into data with the ability to build a
scalable " single source of truth" for various use cases
 - Allows for immediate action to be taken on data that changes
frequently
 - Alleviates time/resources soent on processes for every analysis
â• CONS
 - Can take engineering resources to keep pipelines lives and scalable
which may be outside the scope of some companies data resources
allocation
 - Without the ability to interpret data you can lose control of the
narrative, which can cause data chaos (ie teams coming to conflicting
conclusions based on the same data)
 - Can potentially cause a lack of trust if the data isnt handled properly
I also learnt about how to SHARE DATA which is all about DATA PRESENTATION or SLIDE PRESENTATION.
When exploring a slide presentation, use your knowledge of effective
presentation practices to evaluate it. This includes REVIEWING YOUR OWN
WORK. When checing over slide presentations there are some best
practices you can check for.
đź’ Include title, subtitle, and date. Making sure that your deck presentation
has a title, subtitle and date makes sure that your audience knows
exactly what you are presenting and when the information was from.
đź’ Use a logical sequence of slides
đź’ Provide an agenda with a timeline
đź’ Limit the amount of text on slides
đź’ Start with business task
đź’ Establish the initial hypothesis
đź’ Show what business metrics you used
đź’ Use visualizations
đź’ Introduce the graphic by name
đź’ Provide a title for each graph
đź’ Go from general to specifics
đź’ Use speaker notes to help you remember talking points
đź’ Include key take aways.
Awasume Marylin
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Presentations Tips: What to know on data analysis presentation Phase 1.
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Benchmarking Timescale, Clickhouse, Postgres, MySQL, MongoDB, and DuckDB for real-time analytics. Introducing RTABench 🚀
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