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Okay I probably have your attention here for about 10 seconds, so Iโm 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: ๐ก Learned as a strategy consultant what matters and what doesnโt ๐ค Storytell to help clients implement effectively (how do you properly engage the audience and infuse that story with the key takeaways/ implications) ๐ป 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
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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? ๐ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐ญ๐ก๐ ๐ซ๐จ๐ฅ๐ ๐จ๐ ๐ญ๐ก๐ ๐๐๐ฌ๐ก๐๐จ๐๐ซ๐ โ Is it a prototype or does it need productionizing? ๐ ๐ ๐ข๐ ๐ฎ๐ซ๐ ๐จ๐ฎ๐ญ ๐ก๐จ๐ฐ ๐จ๐๐ญ๐๐ง ๐ข๐ญ ๐ข๐ฌ ๐ฎ๐ฌ๐๐ โ No point in cleaning up a dashboard that is rarely usedโฆ ๐ ๐ ๐ข๐ง๐ ๐ญ๐ก๐ ๐จ๐ฐ๐ง๐๐ซ๐ฌ ๐๐ง๐ ๐ฎ๐ฌ๐๐ซ๐ฌ โ Who owns it and who are the business users actually using it? ๐ ๐๐ข๐ฌ๐ญ ๐จ๐ฎ๐ญ ๐ญ๐ก๐ ๐๐๐ญ๐ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ โ Creating a simple list of data sources helps organize things โ๏ธ ๐๐๐ญ๐๐ข๐ฅ ๐ฐ๐จ๐ซ๐ค ๐ ๐จ๐ง๐ ๐ข๐ง๐ญ๐จ ๐ญ๐ก๐๐ฆ โ Is there manual cleaning involved in the data or is it easily automated? โ๏ธ ๐๐ฌ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐จ๐ง ๐ญ๐ก๐ ๐๐ง๐ฌ๐ฐ๐๐ซ 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? ๐ง ๐๐จ๐ฅ๐ฏ๐ ๐๐จ๐ซ ๐ญ๐ก๐ ๐ฐ๐ก๐จ๐ฅ๐ ๐๐ฎ๐ฌ๐ข๐ง๐๐ฌ๐ฌ 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? ๐
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 canโt expect business users to learn data tools and dashboards just because you spent time building it and you say they โprovide insightโ. So here are how both paths break down in the goal with and how one uses data: โญ ๐๐ฎ๐ฌ๐ข๐ง๐๐ฌ๐ฌ ๐๐ฌ๐๐ซ ๐๐จ๐๐ฅ: Easily access accurate insights to improve their decision-making 1๏ธโฃ First Interaction: Log into operational tools or request Excel files 2๏ธโฃ Query & Align: Search for data relationships within tool (or Excel) 3๏ธโฃ Analysis: Potentially consult dashboards to do some analysis (often exporting to Excel) 4๏ธโฃ Consumption & Activation: Take action via existing operational tools and methods ๐ ๐๐๐ญ๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐๐ฅ ๐๐จ๐๐ฅ: Provide the business with reliable data outputs that provide insight to improve results 1๏ธโฃ First Interaction: Identify data sources and pull into warehouse 2๏ธโฃ Query & Align: Clean and standardise data 3๏ธโฃ Analysis: Extract insights through deep analysis 4๏ธโฃ Consumption & Activation: Publish refined data for business use, often via dashboards What is the key difference between these two processes? Business Users ๐ฐ๐ผ๐ป๐๐๐บ๐ฒ ๐ฑ๐ฎ๐๐ฎ, whereas Data Professionals ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐ฒ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐ The most important thing is therefore ๐ต๐ผ๐ ๐๐ฒ ๐ฏ๐ฟ๐ถ๐ฑ๐ด๐ฒ ๐ฎ๐ฐ๐ฐ๐ฒ๐๐ ๐๐ผ ๐๐ต๐ผ๐๐ฒ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐ This is why I wrote about Reverse ETL in this weekโs 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 weekโs Data Ecosystem article (link in the comments) where I sponsored with Census to teach the masses how to better connect the business userโs path with our data analysis path! Enjoy and let me know your thoughts, comments and feedback!
At face value, if you hear the words Reverse ETL, you probably think โnot another unnecessary data term.โ But beyond the confusing vocabulary, the concept has incredible merit. ๐ Imagine you do all the hard work to source, clean, analyse and organise the data. ๐ Youโve got incredible insights loaded into a new beautiful dashboard. ๐ Your output is going to change the game for your favourite business stakeholders. But, reality hitsโฆ ๐ Stakeholders donโt care to interact with your dashboards ๐ซ When they do interact, they don't fully know how to take action ๐ Leadership doesnโt see the value in your work because itโs not tangible enough Enter Reverse ETL, the tool that brings your data insights back the business in the way they want itโvia their operational tools! So, in this weekโs article, we help explain what for dataโs 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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