Agile Trends is an event that aims to push the boundaries of the Software Industry with modern themes, trends and real-world use cases. And of course, Avenue Code was there to sponsor it. In addition to sponsoring, we sent some of our best and brightest to learn from the speakers and fellow attendees. Here's what we learned.

Harnessing the Potential of Big Data


Kairala SP.pngVinicius Kairala
- Usually, companies try to implement a winning strategy to transform all their data knowledge into a profitable model using select practices based on Advanced Analytics, Big Data and Data Science. Despite the well-deserved hype around these terms, many of the resulting practices can be overly complex and are not a good fit for every enterprise, depending on the variables. Marcos Nyssens and Daniel Scalli Fonseca of IBM gave a fantastic talk during Agile Trends on transforming Advanced Analytics into real money in large corporations. Here are my takeaways:

First of all, you need to know what problem you are trying to solve. Asking yourself whether you HAVE a problem is a good place to start in order to pinpoint what you need to tackle. An extremely common mistake many companies make is collecting massive amounts of data without knowing what they'll do with it. This approach nearly always jeopardizes the quality of the information extracted.

Secondly, you should whether your company's data is significant enough to merit analysis. Again, not all data is going to be a useful source of knowledge. In fact, the wrong data can increase complexity and muddy the waters when it comes time for analysis.

So, what's the best way to begin modeling and gathering big data? The best way to begin working with analytics is to work closely with data experts. These specialists will know not just how to extract data, but also understand its significance. This means they will be able to advise you on the meaning of the information available, and help you to process it in a way that is truly beneficial for your company. 

Last but not least, gathering good data by itself is not sufficient to succeed. You must know how to absorb and process this information. Two good practices that companies should follow when dealing with data are: 1) Have clear metrics to monitor progress and define whether data science is efficiently solving the previously identified problem, and 2) Commit to short cycles of analysis in order to ensure quick feedback about progress and forecast future results. 

Sharing Best Practices

Mariane.pngMariane Ferroni -  In the Agile community, we talk about communication all the time, but the importance really cannot be overstated. As much as projects are disparate and differ in the specifics, you will always find someone who has experienced or is in the middle of a similar situation to yours. Exchanging experiences and ideas throughout the lifecycle of a project is not only critical to avoiding pitfalls, but is also fundamental to discover new ideas and share best practices.  

Enterprise-Wide Adoption

Michel SP.pngMichel Duarte - The approach of the customer to the delivery team is a recurring theme of the methodology, but this year I heard multiple speakers greatly reinforcing the approach of the design and development team,  with a focus on mutual learning and more effective delivery of value. Regardless of how simple or complex the scope of the project, the agilists at this year's conference reinforced the importance of Sprint 0, Pre-Game, Inception, the pre-project phase to align all the participants of the team and establish the primary objectives. 

It is important to reinforce the participation of top corporate executives in agile events. Passing the information to those who already know the methodology is easy, but without the support of top management, progress is hampered and the real benefits of the methodology are imparied. 

Implementing agile methodology cannot be a forced process. Getting buy-in from all participants is absolutely critical, which can often take time. It's also important to acknowledge that you will always need to adapt to your company's existing internal processes. 

 Behavioral Economics

Ricardo SP.pngRicardo Luiz- The application of behavioral economics in information architecture can guide us to different decisions depending on the way the information is presented (more on this here). The Spotify model has served as the inspiration for the deployment of agile at scale, but large corporations like Itaú often encounter difficulties in replicating it. When implanting a model, we also need to adapt our culture and needs to this model in order to achieve continuous improvement.

 Results Over Recipes

Finx.pngLuis Alves - For high performance teams, certain stages are always necessary (many choose to follow the Tuckman model: forming, storming, norming, and performing). However, although the stages are common to all high performing teams, there's no single recipe for success within them. Instead, several techniques are available such as the RACI matrix and Feedback Canvas for help at each stage.  

What's Next for Agile?

Were you in attendance at Agile Trends São Paulo? What were your thoughts? Let us know in the comments!


Author

Sthefanie Mingall