How can you avoid the pitfalls of agile data analysis frameworks?

This is perhaps the most important part. This answers the question - WHY we are doing this analysis? The core group ( business and Technology teams) needs to know the long term goals and short term value additions that the agile sprints will be adding in phases. There will be a number of challenges- data availability, quality of data and system integrations that might not exist , data infrastructure that might meet the business expectations.. only when we have a clear purpose, the core group can focus on how to overcome the short term challenges to accomplish the overall goal of the program.

Linkedin asked me to provide a perspective since apparently, according to AI, I'm an expert even though I'm not interested in the topic or know anything about it. Having tried to mute the topic several times to no avail I guess the AI must be right. Without further ado, my expert perspective and advice on this topic is keep doing the things that are working and stop doing the things that aren't working. You're welcome and thanks.

Ensure that the problem you're addressing is well defined and aligned with the objective of the project. This will help in focusing efforts and avoiding unnecessary detours. Also, it's important to define the scope of the analysis upfront. Identify the specific data source, variables, and parameters that will be considered. And through out the entire process, endeavor to involve relevant stakeholders, including clients and team members.

1.Avoid Scope Creep by sticking to the Scope defined.Ensure that all stakeholders have shared understanding of project goals. 2.Maintain Realistic time frames for the analysis tasks. 3.Provide detail update to the stakeholders on progress of tasks and challenges. 4.Solicit feedback from end users and stakeholders regularly. Ensure that the analysis results align with their needs and expectations. Be open to adjusting the analysis based on their feedback.

?? Defining the problem is really like the very first major boss battle in the game of innovative solutioning. Diving into data analysis without defining the problem? That's like entering a game blindfolded! Pinpoint your quest's objective. Is it battling inefficiencies or conjuring insights? ??? Power-Up: Clear Definition A clear problem definition is your game's mini-map. It guides your journey, highlighting challenges and treasures (insights) to discover. ??? Achievement: Stakeholder Engagement Assemble your co-op team! Identify key stakeholders, understanding their unique skills (expectations). They're your allies in this epic data quest. With them, you're set to unlock the ultimate insights and avoid those pesky agile pitfalls! ??

Framework also needs to be supported by the data infrastructure. Most organisations have moved to a lake house concept that provides the benefits of data lake and data warehouse. There are also a number of Extract Transform Load ETL tools and reverse ETL tools have significantly reduced the time taken to get the data from different systems to the reporting layer. Concepts like metrics layer have also reduced the time taken for business logic calculations.

In keeping with my love of gaming??choosing a data framework is akin to selecting your avatar and gear. Think of CRISP-DM as your robust warrior, DataOps as the nimble rogue, and the Data Science Lifecycle Process as the sagacious mage, each offering a unique gameplay style. Before venturing into data dungeons, equip yourself with the essential tools and techniques of your chosen framework - your arsenal in this data quest. Adhere to your selected framework's guidelines, your questline in this adventure, crafted by veteran players to steer you towards victory, bypassing common pitfalls. Gear up with the right framework, and you're on a winning streak!

Agreed. This also allows you to catch the errors early, hence allowing you to refine the approach based on the feedback before moving down a potentially wrong path/outcome.

Let's level up our strategy with some more game-inspired wisdom ?? In the grand RPG of data analysis, we can think of agile frameworks as a game with multiple levels. Each sprint or iteration is a level you must conquer, with its own challenges and loot (insights). ??? Save Points: Just as gamers save their progress to avoid losing hard-earned achievements, regularly evaluate your data quests. Checkpoints (reviews) ensure you're on the right path, allowing for course corrections. ??? Power Boost - Incorporate Feedback: In-game tutorials and player feedback refine your gameplay. Similarly, in data analysis, feedback is gold! It sharpens your strategy, helping you adapt and deliver epic results faster. Keep grinding, and victory is yours!

Outliers are very common in data analysis. Adding this as source of technical debt helps improve data quality in future. Thank you

?? Boss Alert: Technical Debt ?? Technical debt mirrors those game glitches that disrupt play. Often stemming from rushed tasks (tight deadlines) or quick fixes without tackling core issues. The Consortium for IT Software Quality noted that in 2020, poor software quality cost the US about $2.08 trillion, underscoring the hefty price of unchecked technical debt. Regularly "patch" your projects, addressing these glitches. Prioritize them, not just based on severity but on their impact on the overall game (project). By actively managing and reducing this debt, you ensure smoother gameplay (workflow) and a better gaming experience (project outcome) for all??

?? Co-op Mode: Communicate & Collaborate ?? In the gaming world, a team that doesn't communicate is bound to lose. Similarly, in data analysis, communication is your power-up. Regular chats with team members and stakeholders ensure everyone's on the same quest page. Collaborate with data pros like engineers and scientists to unlock special abilities and insights. Quick Fact: According to a study by the Project Management Institute, ineffective communication leads to a 56% increase in project risk. Just as in co-op games, clear communication in projects reduces missteps and boosts success rates.