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Dylan Anderson

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Okay I probably have your attention here for about 10 seconds, so Iโ€™m going to be quick: My niche is navigating the complex data world for organisations to help them deliver on their strategy Basically, I bridge the gap between data and strategy Want to know more? Here is how I do it: ๐Ÿ’ก Learned as a strategy consultant what matters and what doesnโ€™t ๐ŸŽค Storytell to help clients implement effectively (how do you properly engage the audience and infuse that story with the key takeaways/ implications) ๐Ÿ’ป Use my R coding and strategy consulting experience to bridge the data scientist's "get lost in the data" approach with the "tell me what I need to know on one page" approach of the business executive โ˜… Live off my passion to see through positive outcomes, be that at work, on my blog or even when helping a friend So why should you care? 1) I post my best tips 3-5x per week on LinkedIn 2) I make it relevant to you, as a student, business professional, executive, or whomever. My goal is to share relevant stuff with my audience 3) I try to be unique in what I share and deliver, as few people have the combination of experience I boast 4) I value feedback and input from all others out there. I pride myself on learning from others' perspectives so please add it to my content! Also some of my other Outputs: Medium - https://dylansjanderson.medium.com/ Blog Website - https://www.policyinnumbers.com/ Election App - https://danderson.shinyapps.io/Canada-Electoral-Model/ Academic Journal Article - https://www.cambridge.org/core/journals/canadian-journal-of-political-science-revue-canadienne-de-science-politique/article/tipping-point-of-a-strategic-vote-when-does-an-individual-vote-strategically/7FEDD21C59DF03AC15311CD13080881B

Check out Dylan Anderson's verified LinkedIn stats (last 30 days)

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43,686
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3
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1,089
Likes
935

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Dylan Anderson's Best Posts (last 30 days)

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Every pretty front end has a backend... And that backend usually doesn't have quite that same appeal... So what do you do to fix it? ๐Ÿ† ๐”๐ง๐๐ž๐ซ๐ฌ๐ญ๐š๐ง๐ ๐ญ๐ก๐ž ๐ซ๐จ๐ฅ๐ž ๐จ๐Ÿ ๐ญ๐ก๐ž ๐๐š๐ฌ๐ก๐›๐จ๐š๐ซ๐ โ€“ Is it a prototype or does it need productionizing? ๐Ÿ“ˆ ๐…๐ข๐ ๐ฎ๐ซ๐ž ๐จ๐ฎ๐ญ ๐ก๐จ๐ฐ ๐จ๐Ÿ๐ญ๐ž๐ง ๐ข๐ญ ๐ข๐ฌ ๐ฎ๐ฌ๐ž๐ โ€“ No point in cleaning up a dashboard that is rarely usedโ€ฆ ๐Ÿ”‘ ๐…๐ข๐ง๐ ๐ญ๐ก๐ž ๐จ๐ฐ๐ง๐ž๐ซ๐ฌ ๐š๐ง๐ ๐ฎ๐ฌ๐ž๐ซ๐ฌ โ€“ Who owns it and who are the business users actually using it? ๐Ÿš€ ๐‹๐ข๐ฌ๐ญ ๐จ๐ฎ๐ญ ๐ญ๐ก๐ž ๐๐š๐ญ๐š ๐ฌ๐จ๐ฎ๐ซ๐œ๐ž๐ฌ โ€“ Creating a simple list of data sources helps organize things โœ๏ธ ๐ƒ๐ž๐ญ๐š๐ข๐ฅ ๐ฐ๐จ๐ซ๐ค ๐ ๐จ๐ง๐ž ๐ข๐ง๐ญ๐จ ๐ญ๐ก๐ž๐ฆ โ€“ Is there manual cleaning involved in the data or is it easily automated? โš™๏ธ ๐ˆ๐ฌ ๐š๐ฎ๐ญ๐จ๐ฆ๐š๐ญ๐ข๐จ๐ง ๐ญ๐ก๐ž ๐š๐ง๐ฌ๐ฐ๐ž๐ซ โ€“ Pipelines take a lot of work to build, do we need that or can it be automated through a few scripts or a tool like Orchestra? ๐Ÿง  ๐’๐จ๐ฅ๐ฏ๐ž ๐Ÿ๐จ๐ซ ๐ญ๐ก๐ž ๐ฐ๐ก๐จ๐ฅ๐ž ๐›๐ฎ๐ฌ๐ข๐ง๐ž๐ฌ๐ฌ โ€“ Before cleaning up pipelines and data for one dashboard, figure out how that data maps to other tools and products throughout the organization and save yourself some future time   A lot (and I mean a lot) of projects I do is about strategically thinking about how data assets need to be organized in companies to feed into these types of dashboards   So before cleaning the pipeline for one dashboard, figure out if it is even worthwhile and think about how to streamline for more products/ tools How many dashboards have you built that look like this? ๐Ÿ˜‰


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Business stakeholders use data differently to you And if you don't understand that, you will not succeed in driving business value After all, we canโ€™t expect business users to learn data tools and dashboards just because you spent time building it and you say they โ€˜provide insightโ€™. So here are how both paths break down in the goal with and how one uses data: โญ ๐๐ฎ๐ฌ๐ข๐ง๐ž๐ฌ๐ฌ ๐”๐ฌ๐ž๐ซ ๐†๐จ๐š๐ฅ: Easily access accurate insights to improve their decision-making 1๏ธโƒฃ First Interaction: Log into operational tools or request Excel files 2๏ธโƒฃ Query & Align: Search for data relationships within tool (or Excel) 3๏ธโƒฃ Analysis: Potentially consult dashboards to do some analysis (often exporting to Excel) 4๏ธโƒฃ Consumption & Activation: Take action via existing operational tools and methods ๐ŸŒŸ ๐ƒ๐š๐ญ๐š ๐๐ซ๐จ๐Ÿ๐ž๐ฌ๐ฌ๐ข๐จ๐ง๐š๐ฅ ๐†๐จ๐š๐ฅ: Provide the business with reliable data outputs that provide insight to improve results 1๏ธโƒฃ First Interaction: Identify data sources and pull into warehouse 2๏ธโƒฃ Query & Align: Clean and standardise data 3๏ธโƒฃ Analysis: Extract insights through deep analysis 4๏ธโƒฃ Consumption & Activation: Publish refined data for business use, often via dashboards What is the key difference between these two processes? Business Users ๐—ฐ๐—ผ๐—ป๐˜€๐˜‚๐—บ๐—ฒ ๐—ฑ๐—ฎ๐˜๐—ฎ, whereas Data Professionals ๐—ฝ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐—ฒ ๐—ถ๐—ป๐˜€๐—ถ๐—ด๐—ต๐˜๐˜€ The most important thing is therefore ๐—ต๐—ผ๐˜„ ๐˜„๐—ฒ ๐—ฏ๐—ฟ๐—ถ๐—ฑ๐—ด๐—ฒ ๐—ฎ๐—ฐ๐—ฐ๐—ฒ๐˜€๐˜€ ๐˜๐—ผ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ถ๐—ป๐˜€๐—ถ๐—ด๐—ต๐˜๐˜€ This is why I wrote about Reverse ETL in this weekโ€™s Data Ecosystem. The whole goal is to make data and its insights more accessible via the operational tools business users use every day If we understand their business processes, maybe we will do better catering to their needs? So check out this weekโ€™s Data Ecosystem article (link in the comments) where I sponsored with Census to teach the masses how to better connect the business userโ€™s path with our data analysis path! Enjoy and let me know your thoughts, comments and feedback!


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At face value, if you hear the words Reverse ETL, you probably think โ€œnot another unnecessary data term.โ€ But beyond the confusing vocabulary, the concept has incredible merit. ๐Ÿ› Imagine you do all the hard work to source, clean, analyse and organise the data. ๐Ÿ“Š Youโ€™ve got incredible insights loaded into a new beautiful dashboard. ๐Ÿš€ Your output is going to change the game for your favourite business stakeholders. But, reality hitsโ€ฆ ๐Ÿ‘€ Stakeholders donโ€™t care to interact with your dashboards ๐Ÿšซ When they do interact, they don't fully know how to take action ๐Ÿ›‘ Leadership doesnโ€™t see the value in your work because itโ€™s not tangible enough Enter Reverse ETL, the tool that brings your data insights back the business in the way they want itโ€”via their operational tools! So, in this weekโ€™s article, we help explain what for dataโ€™s sake Reverse ETL means and why the concept (and practice) is not just a trend and needs to be a core piece of your data infrastructure! Check it out via the link in the comments! Also shout out to Census for partnering with me on this article. Their focus is for you to understand what Reverse ETL is and its benefits, giving me free rein to write about it. If it interests you, definitely check them out as they have been amazing supporters of the newsletter!


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