Portable

Running a company with data is WAY easier than most people think. 1. Identify your company's current strategic goals 2. Find metrics that can be used to measure progress against those goals 3. Identify the bets your company is making to hit each goal 4. Find metrics that can be used to measure whether those bets are paying off 5. Get all of this ?? in front of your executives so they can run the business __ The first image below: How I ALWAYS recommend companies structure their exec level dashboards. The second image below: How much effort should be invested in ALL of the other dashboards at the company.

For those of you that live in Salt Lake City -- I'm very excited to be in town for next week's Low-Key Data Happy Hour! Here are the Details: - Tuesday September 10th - 7-9pm - Location = The Register in SLC Looking forward to catching up in person with ?? Rho Lall ??, Joe Reis ??, and Watt Wood who make these things happen every month. Here's the link to sign up: https://lnkd.in/dY58KF2M See you there. (If you can't make it this month, but live in the area, you should definitely join the meetup group to hear about SLC data events going forward)

I cannot talk highly enough about the amazing service we get from Portable! If you're looking for ETL & ELT connectors, you won't find a more valuable partner. Every time we've encountered an edge case, they've been ready to dive in and help us formulate solutions. Their commitment to our success truly sets them apart. A special shout out to the team at Mozart Data for the introduction. Good partners make all the difference, and we at TubeScience are grateful for the synergy between Portable and Mozart Data.

There are a LOT of open roles in data. and A LOT of great candidates looking for roles in data. __ Who's hiring in data right now? Have an open role, or know of one? Mind dropping it below? ??

If you don't know Python, can you ever be truly great at data + analytics? In 2018, I had just became the 'Head of Business Intelligence' at LiveRamp (NYSE:RAMP). And I had... ABSOLUTELY NO CLUE WHAT I WAS DOING __ I knew my goal was 'To use data to create value for the business' But, I had some serious questions: 1. What data am I supposed to use? 2. Which questions do I answer to 'create value'? 3. Am I even capable of bridging the gap between data + value (I barely knew SQL, and had no chance of learning Python)? So, I started talking to people. __ I was talking to experts in the data world (Colin Zima was one of the first people I was introduced to ??) to learn about the data + tooling in the industry. I was talking to leaders inside of our company to identify the best ways to 'create value'. And I started talking to vendors about the tooling that was available to analysts like me, that DO NOT know Python. __ Honestly, I was blown away by how simple it was to set up a data warehouse, connect a visualization, and copy + paste data from business applications into a warehouse - all with NO CODE. It got me hooked on the value that can be created by empowering analysts with the tooling they need to create value. __ I was so hooked, that I even started a company. It's why I've been working on Portable for 4+ years at this point, and will happily sign up for another 4+ years on this problem. There are tens of thousands of really smart analysts in the world. They are great at identifying high value business problems, but they simply don't know Python. And they shouldn't need to know Python. They just need great no-code solutions to fill in the gaps. __ At Portable, our goal is to make ELT simple, affordable, and accessible to all data teams. You shouldn't need a Fortune 500 budget to sync your data sources to your analytics environment... And you definitely don't need to know Python.

Build vs. buy in the data world is a myth. For the majority of teams, it's a fake concept. __ The reason? Data teams fall into two buckets, and both buckets have only one real option (build or buy). Here are the personas: 1) Tech-driven data teams (the ones that love writing code, open-source software, and cherish their ability to write Python). These teams only know how to build. 2) Business-centric data teams (definitely don't know Python, and focus on turning raw data into value as fast as possible). These teams don't have the capabilities to build, and therefore have to buy. __ Tech-driven data teams primarily exist in regulated industries (finance, healthcare), and outside the US (where access to SaaS products is more limited and access to engineering talent is easier). Or when the data team started within an engineering function, and expanded from there. Business-centric data teams typically start in a business unit (like marketing or product) with someone that barely knows SQL, but can create value insights from data. They keep expanding, buying tools along the way that fill in the gaps. They focus on ROI, and only add capabilities when there's a business reason to do so. __ So, why is build vs. buy a myth? Tech-driven teams NEVER seriously consider buying. They say things like 'It doesn't have an open-source component? I won't consider it'. Or 'I need to be able to extend things' Business-centric data teams DO NOT KNOW PYTHON. So they can't legitimately build a solution in most scenarios. Sure... there are ~10% of data teams where they have the technical ability to build, and the business acumen to realize when they should not build. But that is the exception, not the rule. 90% of the market falls squarely into one of the two buckets. __ Does your team know Python? You are going to BUILD. Does your team not know Python? You are going to BUY. __ Is this a problem? Ehh... I'm not sure. But here's where it causes issues: - Don't tell yourself there's a real build vs. buy decision if there isn't. Know your team's strengths / weaknesses / culture and invest in it. If you build everything. BUILD. If you buy everything. BUY. Don't kid yourself - If you build everything, you should seriously consider whether that's sustainable in a high interest rate environment for your company. If you're in a heavily regulated industry with long procurement cycles, you're going to keep building - it doesn't matter what happens with interest rates. But if you work at a small to mid-sized companies, and you are trying to build everything from scratch, you should seriously consider whether you're taking the right approach. Tech-driven data teams love to point to low cloud costs as a measure of success. They rarely add the total compensation for their data team to the equation. If they need 5 engineers (1m+ in compensation) to maintain data pipelines that would cost a few hundred thousand dollars a year to buy, something is VERY wrong...