David Kim

Want to ditch data analysis confusion & become a rockstar? Our latest post defines reproducibility & shows you how to document your work for clear, repeatable results. https://wix.to/kEpcU5r #newblogpost #DataScience #Reproducibility #dataanalystskills #analytics

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Would you like to run MMM or run after MMMs? I think a lot of companies run AFTER MMMs rather than letting the MMMs run FOR them. Naavics helps companies in three ways. 1. Identify and fix the delays that creeps in your data 2. Work with your teams to set Data quality standards that apply for MMM 3. Making sure the results timeline aligns with your Media planning #MMM #attribution

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All #datavisualization practitioners should have a style guide for any series' of visualizations or if desiring to create their own brand. Companies sometimes have templates, often focused on the brand's colors and logos.

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Your Data Infrastructure (or lack thereof) Is Costing You BIG TIME This simple adjustment led to 110% more purchases and a 33% reduction in Cost Per Purchase. What was the change? Was it new creatives? Nope. Was it new copy? Nope. Was it simplifying their offer? Nope. The real game changer? Hiring our team of specialists to optimize the D A T A infrastructure & strategy Your Data Infrastructure is the foundation of ALL your marketing and growth activities & a robust first-party data strategy is crucial for your growth efforts. Here's the issue. . . Marketing and growth teams are stretched thin— managing clients, creating content, testing creatives, developing strategies—the list goes on. And what inevitably happens is that the very foundation of all of their efforts is left unmanaged. The consequences of neglecting your data infrastructure and data strategy include: > Erratic ad performance > Rising acquisition costs > Missed growth opportunities All of which directly impact your bottom line. After the release of iOS 14.5, brands have adopted a variety of strategies to overcome tracking and data challenges, typically falling into three main categories: Bucket 1: Ignore the need for effective first-party data systems & endure rollercoaster ad performance. Bucket 2: Use standard CAPI through platforms like Shopify. Better than nothing, but still limited and will never deliver optimal performance. Bucket 3: Rely on tracking, CAPI or attribution tools just because a well known podcast or thought leader recommends it. Spoiler alert. . .most of these tools are just glorified tag managers. Most of our clients come to us from Bucket #3—using tracking, CAPI, or attribution tools recommended by influencers or podcasts. When they put these popular solutions to the test against us, we've never been outperformed. Upgrade your data strategy today with audienceOS & join our clients who experience: ✅ More consistent ad performance ✅ Lower costs per acquisition ✅ Enhanced ability to scale Bottom line, an optimized data strategy & infrastructure are no longer 'nice-to-haves'—they’re essential for consistent, predictable growth. Don’t let your data infrastructure be your Achilles’ heel. Invest in your data infrastructure today & unlock massive growth and stability for your brand or clients. Comment below or send me a DM & let's see how much more profit we can help you squeeze out of your paid acquisition efforts.

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❓ Did you know that Tableau's character sort is case-insensitive?   ⚖️ That means that it treats capital letters and lowercase letters as the same.   👍 That's probably a good thing and a useful default.   😀 The (somewhat funny) problem that I ran into is that has my RDBMS (Teradata) has case-sensitive sorting.   📃Capital letters sort first, lower case letters later.   ❌ I was testing my dashboard with some queries against the source data and many of my metrics were off for the first row.   🔍 In the Teradata tool, the text fields were also being truncated for display as in the "Output" section of the image.   👁️ So initially, I couldn't see the sort disparity.   👍Once I saw it, I wrapped my ORDER BY with an UPPER(variable), which eased the comparison, and everything was fine.   📝 Sometimes it's the little things (assumptions) that cause problems.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #data #datascience #analytics #VizoftheRay

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Data teams can rarely affect a business on their own They need to work with stakeholders to figure out: How the business makes money What areas the business can improve How to scale things that the business does well Sitting in isolation and taking orders doesn't do anyone any favours. Spotting things the business can improve and not sharing them doesn't change a business. Building new data products and then surprising your colleagues with them isn't going to drive adoption. If you work in data, get out there and talk to your colleagues. It will be amazing what you find out and solve together. Discover more of my posts by following hashtag #datatranslator #data #analytics #stakeholders

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🧩 Reading [Google Research's latest article](https://lnkd.in/ejk3_wMH) on understanding relationships between datasets got me thinking about the bigger picture in data management. This research brings out the importance of uncovering and automating complex relationships within raw data—a task that’s essential but often challenging. ✨ Palantir’s Ontology approach came to mind here as a powerful example of putting these ideas into action. By creating a digital twin of business entities and their relationships, Palantir enables companies to interact with data at a higher, business-relevant level. This abstraction layer transforms raw data into meaningful insights. ⚙️ To bring further structure, tools like Featuretools, Woodwork, Compose, and EvalML offer an open-source framework set that standardizes features and prepares data for machine learning. With **Featuretools** for automated feature engineering and **Woodwork** for consistent data typing, this toolkit enables the creation of reusable data products right from raw schemas and tables. 🌐 Imagine combining Google’s research-driven insights, Palantir’s digital twin framework, and the Featuretools ecosystem. Together, they could enable a truly dynamic data ecosystem—one where relationships are not just mapped but also standardized, enriched, and primed for decision-making. #DataManagement #Ontology #FeatureEngineering #GoogleResearch #Palantir #MachineLearning #OpenSource

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I have been utilizing and maximizing Tableau containers in building my dashboards at work but I have learned and appreciated it more during today's Next Level Tableau session with Andy Kriebel. Thanks, Andy and NLT-mates for the productive learning session! 🤗🙏🏼 #NLT #Tableau #containers

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The below really resonated with me and got me thinking. Whether you're a data/analytics leader or one of the incredibly busy analysts that are delivering endless low value add pieces of insight...start 2025 on the right foot. Create a simple analytics process/ruleset and start implementing. My steps below are over generalised as there's enough material out there on data and analytics processes to fill a small library, but as a guide: 1. When a task or question is presented to you, scrutinise it. Really understand what the ask is and what the stakeholder ultimately plans on doing with any insight received, you should be helping them define or redefine their initial problem statements 2. Scope out the piece of work and create a proposed structure for your analysis. Share the plan with the stakeholder and get alignment/agreement on timescales now you have a better idea of how you can tackle the problem. Take them on the journey 3. Execution... 4. Draw your conclusions/recommendations and communicate your findings, ideally face to face or on a call...deliver your insights with passion and showcase the hard work it takes to deliver answers to the questions being asked 5. Depending on the ask there may be a need to track and touch base in the near future, so put the time in the diary and circle back with any new learnings there may be from your recommendations It's so easy to just say yes, feeling like you're pleasing a stakeholder by doing boring, low value add tasks day in and day out, but that isn't where personal gratification or progress will be made. It won't be long before AI has automated the majority of these low value add tasks, the analysts future is in being a strategic thought partner that helps define and build strategic products, that helps business leaders rethink their questions and proposes answers that they couldn't and wouldn't otherwise have had access to without their INvaluable analysts.

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Cluster Timeout or Idle Kills Caption: Long merges might get killed if your Databricks cluster or Synapse Spark session times out. Adjust auto-termination or keep small, frequent merges. Question: Do you design for short-living clusters in your cost-saving strategy?

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The Bygone Era of Legacy BI - Dashboards are Dead! If you've ever felt like you’re stuck in a time loop every time you open a dashboard, you're not alone. Dashboards, those flashy displays of KPIs and metrics, were supposed to be the knight in shining armor for data-driven decisions. Yet, here we are, years later, still shackled by their limitations. A stagnant adoption rate of 30%, an average report turnaround time of 4.5 days, and poor data experiences reported by 84% of frontline business workers make it painfully clear: DASHBOARDS are DEAD! ☠ Unpacking the Hidden Costs of Legacy BI Let’s get real for a moment. When was the last time your data team celebrated a dashboard? More likely, they’re cursing the operational nightmare it has become. Reports show that 92% of data workers spend their time on tasks outside their roles, while 68% of data teams lack time to implement profit-driving ideas. Your team isn't just buried in data—they’re drowning in it. Consider this: 50% of your data team’s headcount budget is wasted on remedial tasks instead of actual data analysis. Imagine hiring a Michelin-star chef only to have them spend their days peeling potatoes. The hidden costs of maintaining these dashboards extend far beyond just financials—they’re a productivity black hole. Read more in the link to CDO TIMES independent research on how you can leverage AI to achieve productivity gains and ultimately hand the keys over to your business teams to ask questions about your data and get petabyte scale data insight on an individual level. #aianalytics #aidriveninsights #cdo #datastrategy #cdotimes #AIstrategy #digitalstrategy

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Perhaps you shouldn't work in data... If you think selling is beneath you. Most data people will read that and disagree. They'll say, data people aren't sales people. They'll ignore this message and scroll on. But if we look at the realities for a lot of data teams you'll see that many are under-resourced, under-represented, and generally lack influence and gravitas within their organisations. A failure to sell data teams wins and value-add results in: 🏆 Others taking credit for data team work. 🔻 Missing out on promotions and careers stalling. 💰 Companies not knowing where to invest resources. 🎯 Data teams first in line for cuts when resources are tight. As you can see, your reluctance to sell is selling you short. Life is too short and our careers are too important for that. There is only upside to honing your sales skills. And they look like this: 👩🏼‍💼  Gain confidence as a leader. 📊  Help the organisation succeed. 💷  Earn more money in your career and sooner. 🚀  Improve your standing among peers and superiors. 🌟  Gain the recognition you deserve for you and your team. You sell more, you get recognition, you raise your profile, you get promotions, you get more money in your pay packet. As I said, your reluctance to sell is selling you short. ---------- If you're a data leader and you feel like you could use some help in honing your skill set and strategic approach in leadership then let's talk. I work with data leaders all over the world to help them maximise their influence to transform their organisations are skyrocket their careers. Grab sometime with me for an informal chat via my website: https://lnkd.in/gb6CQYCZ

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🎃 New podcast alert: Data Science Rabbit goes full moon. (Warning: May cause uncontrollable urges to howl at datasets.) Brace yourselves, data nerds. The Data Science Rabbit Podcast just dropped a Halloween special that's more terrifying than your last failed model deployment. 🐺 Episode 1: "When AI Met Werewolf" https://lnkd.in/ex4Hbq4p More scary episodes to come in the next few weeks. Don't say I didn't warn you. Available now on Spotify and all major podcast platforms. Listen at your own risk – side effects may include increased hair growth and an inexplicable desire to chase squirrels while coding. PS: If you turn into a werewolf after listening, please rate us 5 stars. It's the least you could do before devouring the interns. #DataScience #AIWerewolves #PodcastsFromHell

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The best data analysts aren’t just great with numbers, they’re great with people. Understanding the needs, challenges, and goals of your team and business leaders is just as important as the metrics and reports you create. This happened to me this week when I presented opportunities for metrics I thought might be important and the business leader met me with: "I need to do these other metrics first". Slow down. Listen before making suggestion or proposals. It's not that they don't need the metrics you are proposing, it's that some of the others are more important today. What’s one skill outside of data you think every analyst should have?" #DataAndPeople #LeadershipInAnalytics #GrowthMindset

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If you use the right predictive model, you can have an outsized influence on the "movable middle." Here's how our approach is moving the needle for our clients: 1. Model Selection: We're flexible. Whether it's a regression model or a sophisticated machine learning algorithm, we choose what works best for the specific challenge at hand. 2.Customer Ranking: Our models rank customers from most to least likely to exhibit a desired behavior. 3. Decile Division: We split these rankings into 10 equal groups, or deciles. From there, we can lump customers into one of three categories: -Top Deciles: They'll likely engage without extra incentives. -The Middle: This is often where the greatest opportunity lies. -Bottom Deciles: Unlikely to engage regardless of efforts. We focus on that potentially movable middle group. By testing different offers, messages, and channel mixes, we can influence behavior where it matters most. By understanding where customers fall in these deciles, we can optimize marketing efforts and allocate resources for maximum impact. #DataDrivenMarketing #CustomerInsights

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"We need a dashboard that shows... everything." Ah, the joys of vague requirements. "I want cake" can mean tiny cupcake or 10-tier gluten-free, vegan, purple cake. How to navigate the murky waters of ambiguous requests: - Ask probing questions - Define clear objectives - Start small, iterate often - Provide options, not just solutions - Set expectations early and often Remember: If something isn't clear, clarify. Clear requirements lead to better results. What's the vaguest request you've ever received?

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I talk a lot about partnerships and how to use them to grow your business. Here's a great example of how we're doing that at Capitalize Analytics: Showcasing ways where we are working with our partners at Databricks to bring real, tangible solutions to our clients... not just talking about it. I'd recommend giving JT Morris a follow if you're interested in more practical examples, use cases, and step-by-step guides on how to apply technologies like Databricks, Alteryx, and more to your business processes.

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