The Lifecycle of Data Analytics Projects is Endless 🔹 Data analytics isn’t just about crunching numbers—it’s about collecting, transforming, and presenting data to empower smarter decision-making. But here’s the catch 👇 An analytics project never really ends. It’s not a one-time delivery—it’s a continuous cycle of improvement where each step builds on the last. --- 🔄 The Continuous Cycle of Analytics 1️⃣ Requirements Gathering Start with clarity. Define what the business really needs and what problems data should solve. 2️⃣ Data Ingestion & Processing Bring in raw data, clean it, and prepare it for analysis. Quality data = reliable insights. 3️⃣ Data Exploration Discover patterns, anomalies, and trends. Ask: what story is the data telling? 4️⃣ Data Analysis Apply statistical models, visualizations, or advanced analytics to answer business questions. 5️⃣ Deploy the Solution Deliver reports, dashboards, or automated insights that decision-makers can actually use. 6️⃣ Request & Process Feedback Engage with users and stakeholders. Are the dashboards intuitive? Are we answering the right questions? 7️⃣ Optimize the Solution Improve refresh speed Simplify visuals for clarity Align reports more closely with evolving business needs 👉 And then… the cycle begins again. --- 🟧 Key Questions for Assessment 🔹 Is the data product being used consistently? 🔹 Is it easily accessible to the people who need it? 🔹 Does the analysis truly solve the business problem? 🔹 What new questions does this analysis uncover? 🔹 Are there any hidden technical or data issues to fix? --- 💡 Why This Matters Every time you expose insights, new questions emerge. Every time you deliver a report, users ask for improvements. That’s the beauty of analytics—it’s not a finish line, it’s an ongoing conversation between data and business. The data team must stay in close dialogue with the business to ensure the solution evolves with changing needs. --- ✨ Takeaway: Analytics isn’t a project with an end date—it’s a continuous improvement loop that keeps growing in value over time. --- 🔵 Smash the LIKE button & REPOST if you agree analytics never really ends. 🟧 BOOKMARK this post—you’ll definitely want to revisit it later. 🔵 Follow @arunkinsights (Digital Infovision) for more practical Data Analytics insights. #DataAnalytics #ContinuousImprovement #BusinessIntelligence #DataDriven #AnalyticsLifecycle #DecisionMaking #DataScience #DataInsights #ProblemSolving #Innovation

The recording from our April #DataBytes webinar "Supporting #AI #RiskManagement in the #AnalyticsLifecycle" is now available. The event featured Sophia Rowland. #datascience #analytics #trustworthyAI #AIriskmanagement #bigdata #trustworthydata @SASsoftware youtube.com/watch?v=IfvFyFUO…

#DataBytes, a free #webinar series designed to showcase diverse innovative topics in #datascience, returns today. Join Sophia Rowland for "Supporting #AI #RiskManagement in the #AnalyticsLifecycle" 3:30 PM ET. #data @SASsoftware datascienceconsortium.org/ev…

#TheNCDS will host its next #DataBytes #webinar on April 30 at 3:30 PM ET. Sophia Rowland from @SASsoftware will present "Supporting #AI #RiskManagement in the #AnalyticsLifecycle." #analytics #datascience #dataanalytics #governance #trustworthyAI datascienceconsortium.org/ev…

🌟 Our analytics life cycle interview series continues with our third episode on data mining, covering all kinds of data science techniques 🚀 💡 Join me and Gary Spakes from Ridge Trail Ventures as we dig into the complexities of the data mining stage of the analytics life cycle. Whether you're just starting out or been practicing analytics for years, we've got something for everyone. We cover topics like… - Predictive vs. prescriptive models - Supervised vs. non-supervised models - Parametric vs. non-parametric models - Handling missing values - The bias-variance tradeoff - Model tournaments - Validating models on a holdout dataset - Automated insight detection 🔗 youtu.be/umXYsrlTkMc?si=Aoyx… ✨Don't miss out on this opportunity to expand your analytics expertise and take your data mining skills to the next level! #AnalyticsLifeCycle #DataMining #DataScience #AnalyticsCommunity

Not only is #AI boosting efficiency and accuracy—it can also help improve dynamic #DataVisualization. 📊 #AnalyzingData is only part of the #AnalyticsLifecycle; we also have to make sure we're effectively communicating the findings. @Spiceworks spiceworks.com/tech/data-man…

Get to Know SAS Viya and how it manages different workloads to support decisions. #SASGF #AI #analytics #datamanagement #datascience #dataliteracy #modelops #dataops #analyticslifecycle #datadriven #datascientists #dataengineers #sasusers

Reaching 3.6 million football fans via comprehensive #marketinganalytics #SASGF #AI #analytics #datamanagement #datascience #dataliteracy #modelops #dataops #analyticslifecycle #datadriven #CDO #CIO #CAO #datascientists #dataengineers #dataanalysts bit.ly/3uVYfqN

Creating a customizable agenda and experience at SAS Global Forum #SASGF #AI #analytics #datamanagement #datascience #dataliteracy #modelops #dataops #analyticslifecycle #datadriven #CDO #CIO #CAO #datascientists #dataengineers #dataanalysts #sasusers bit.ly/3tS5DCh

#SAS Global Forum 2021 kicks off in less than 24 hours! #SASGF has a few surprises up its sleeves, @Jennchase. #AI #analytics #datamanagement #datascience #dataliteracy #modelops #dataops #analyticslifecycle #datadriven #CDO #CIO #CAO #datascientists bit.ly/3uXrHN9

Our own Vegard, on stage presenting an end2end demo! 👏👍 #SASGF #analyticslifecycle #AnalyticsInAction

How is an analytical model like a wild salmon 🐟? Let's discuss further at our Nordic SAS Forums #SASNF 🐟 blogs.sas.com/content/hidden… #analyticslifecycle #analytics

Looking forward to this one! #GartnerDA @SAS_ANZ #sasanz. #ML #AI #AnalyticsLifecycle #ModelManagement My topic? In this session we will explore the key to successfully executing an analytics strategy, and how the right analytic…lnkd.in/fQd-Wqu lnkd.in/f9fNMXm

How is an analytical model like a wild salmon 🐟? Let's discuss further at our Nordic SAS Forums #SASNF blogs.sas.com/content/hidden… #analyticslifecycle #analytics

How is an analytical model like a wild salmon? #analytics #analyticslifecycle @JOchiaiBrown bit.ly/2TK3Y0z

#IoT projects are like building a house - Dr. Nicole Tschauder explains challenges and solutions @FFMDataScience @Meetup #ML #SensorData #EdgeAnalytics #AI #manufacturing #AnalyticsLifecycle #DataScience #FourierTransformation #RPCA #SVDD #FFMDataScience @DrKeil @UlrikeBergmann

A prediction engine that operated for over 1,100 years! I summarized some similarities between the Oracle of Delphi and #PredictiveAnalytics in my most recent blog. #analyticslifecycle #machinelearning tinyurl.com/yby3vzvc

Gerhard Svolba sagt in seinem neuesten Blogbeitrag Danke an alle, die am Analytics Lifecycle mitwirken. 2.sas.com/6018DL4OK #Analytics #AnalyticsLifecycle #AnalyticsX

TOP 10 requirements IT decision-makers should have for their analytics platform #analyticsplatform #analyticslifecycle #datascience @RnrStrnckr #DataScience #MachineLearning #DeepLearning #NLP #Robots #AI #IoT #BigData bit.ly/2Ngj7Dm

"We found that #SAS supports the entire series of steps in the #analyticslifecycle, offers high-speed processing of considerable amounts of data and has excellent forecasting accuracy." says Shouichi Yabe at @konicaminolta #AI #IoT sas.com/en_us/customers/koni…