Design Thinking and Agile for Data Analytics and Datawarehouse
Forbes predicts a 50 percent growth in predictive analytics software applications by 2018 to nearly $3.5 billion. Global spending on Big Data and predictive analytics is expected to grow at a rate of 30 percent or more per year and will hit nearly $120 billion by 2018.
Applying Design Thinking and Agile concepts and methodologies of the web/mobile software development world CANNOT be applied AS-IS to the Data Analytics space given the multi dimensional aspects that are considered when building DataMart, dashboards, ML algorithms, BI reports. However the underline paradigm and the principles of Design Thinking and Agile still applies.
This session will highlight the key differences that exists between the two worlds by showcasing some real life examples. It will also cover the relevant tools and frameworks and how the current design thinking and agile process can be tweaked for Data analytics and Datamart development.
Outline/Structure of the Case Study
The session will outline the key differences between the Data analytics projects and the Web/Mobile development.
- Huge Upfront Investment in Infrastructure
- Time to Market is Higher
- Specialized skills required within the team
- Data sanity, crunching, availability and accuracy is more critical than user experience
- Focus on Buy vs Build (Out of the Box solution over Custom-build applications)
- Large team size and multiple vendors
- DevOps is still not mature
Based on the Areas of Segregation, the Design Thinking and Agile process needs to be tweaked in the following areas
- User Personna : Identification of user personnas needs to be done based on complexity of analysis, granularity of data and security access.
- Journeys: Instead of focusing on ‘USER’ journeys one needs to focus on ‘DECISION-DRIVEN’ user journeys.
- Ideation Workshop: The ideation workshop needs to focus on Business KPIs and data driven decision making.
- Ideation to also focus on the selection of appropriate Analytics solution.
- Story definition and Backlog grooming
- Key scrum roles
- Weightage prioritization
- Sprint cycle time: 4 weeks sprints
- Automation Testing is a MUST
- Sprint Retro: Mid Sprint and End of the Sprint
- User Acceptance Testing
The session will help the participants appreciate the complexity and the challenges that are faced in a data analytics and Datawarehouse projects.
The session will specifically benefit the people who are working or planning to work in the Advance Data analytics domain. They will be able to resonate with the challenges and the key differences that exists. In addition they will learn about different tools and frameworks that they can use and how they can modify their current processes to address the challenges.
The session will help the participants appreciate the complexity and the challenges that are faced in a data analytics and Datawarehouse projects. The session will specifically benefit the people who are working or planning to work in the Advance Data a
Prerequisites for Attendees
Basic understanding of scrum and design thinking