Dennis Sawyers’ Post

Curious if anyone knows of an analytics data storage method (coupled to a warehouse or standalone) that handles data “end of life” gracefully? Looking to set rules to offload no-longer-needed historical data to cold storage / outright delete it. Bonus points if it allows for aggregation / profiling prior to removal, so that aggregate metrics can be retained.

Data Classification Buyer's Guide: How To Choose a Data Classification Solution

📊 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴: How to create effective charts. Find the right chart with this overview. Creating effective and convincing charts is an extensive topic. ↳ What kind of data do I want to present? ↳ What do I want to say with it? (data story) ↳ Which chart type is suitable for my data? 𝗘𝘀 𝗴𝗶𝗯𝘁 𝟰 𝗔𝗿𝘁𝗲𝗻 𝘃𝗼𝗻 𝗗𝗮𝘁𝗲𝗻𝗱𝗶𝗮𝗴𝗿𝗮𝗺𝗺𝗲𝗻: ↳ Comparison ↳ Distribution (distribution) ↳ Composition (Composition) ↳ Relationship (relationship) ____ More tips and ideas for better presentations? Follow our newsletter here on LinkedIn: https://lnkd.in/dzaWF6i3

Do you need a data warehouse? Supermetrics loves them 🫶, but not all marketing teams have the resources to manage one. That's why we created Supermetrics Storage! Swipe to find your best option, and check our blog to see the differences between a data warehouse and Supermetrics Storage. 'Supermetrics Storage vs. your own data warehouse: which one should you choose?' 🔗 https://lnkd.in/driXKn9C

💡 Mid-Week Data Tip 💡 When working with large datasets, it’s essential to clean your data effectively before jumping into analysis. Here’s a quick tip: always use SQL Window Functions for advanced calculations like running totals, ranking, and moving averages. They make your queries more efficient and easier to read. Window functions can help you spot trends in your data over time—like identifying top-performing products or regions based on sales growth. 🌟 💻 Tip in Action: SELECT customer_id, order_id, SUM(order_amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS running_total FROM sales_data; #SQL #datacleaning #dataanalytics #SQLtips #datapreparation #businessintelligence #datavisualization"

Here's a sample data set of ticket handle times in minutes: [3, 3, 3, 4, 4, 5, 5, 6, 7, 8, 9, 10, 583] Last Friday, I had a beer with a recently promoted support lead 🍻 He was having difficulty understanding the difference between Average and Median. So, I thought it would be worthwhile to spend a couple of minutes creating a simple bar chart. This chart shows both metrics across a sample data set. Here's a bar chart that illustrates the difference between average and median ticket handle times. 🔸 The Average Handle Time is skewed by the outlier (583 minutes), resulting in a significantly higher value. 🔸 The Median Handle Time represents a more accurate middle point of the data set, unaffected by the extreme value. This visualization clearly demonstrates why median is often a more reliable metric for understanding typical performance in the presence of outliers

🔍Dive into this SD Times article on custom matching strategies to better achieve a full customer view with Tim Sidor, data quality analyst at Melissa. Learn how to navigate the complexities of data matching and choose the right strategy for your business. Read now!📊https://lnkd.in/gNNqDKXY #DataMatching #CustomerView

🔍Dive into this SD Times article on custom matching strategies to better achieve a full customer view with Tim Sidor, data quality analyst at Melissa. Learn how to navigate the complexities of data matching and choose the right strategy for your business. Read now!📊https://lnkd.in/eWStXVK9 #DataMatching #CustomerView

Ever wondered what is the real secret of high-quality data? Meet Emily, A sharp data engineer that ensures her company's data runs smoothly. She's always on the lookout for ways to keep high quality data. One day she spotted odd numbers in the sales report. Without wasting time, Emily jumped into four smart moves. 1. Double checking sums and counts to catch early errors 2. Ensuring files are delivered on time to prevent delays 3. Comparing file sizes to support irregularities 4. Monitoring format changes and verifying metadata to avoid issues. With these steps, Emily catches 95% of the errors, saving time and keeping the business on track. Want to ensure top data quality like her? Book a meeting with complere experts from the below link. https://lnkd.in/gEnfsymj Thank you and see you in next week. #DataQuality #DataAccuracy #DataManagement #ErrorPrevention #SmartDataStrategies #DataIntegrity #complereinfosystem

🔍Dive into this SD Times article on custom matching strategies to better achieve a full customer view with Tim Sidor, data quality analyst at Melissa. Learn how to navigate the complexities of data matching and choose the right strategy for your business. Read now!📊https://lnkd.in/eQSwA2yj #DataMatching #CustomerView