Source: analyticsinsight.net
Today as the competition is at surge among tech organizations, agile principles and priorities are employed for greater productivity. Most of them could be leveraged for data science (DS) projects. Moreover, data scientists do not know how to schedule the project because it is impossible to determine a specific timeline for the type of “research” and exploratory work. Most data science projects require trial and error by going down different paths and trying different techniques. They do not have an element of certainty in the output, so Agile is most suitable to be adopted to direct the workflow.
On the other hand, DataOps in itself is an agile methodology for developing and deploying data-intensive applications, including data science and machine learning. A DataOps workflow supports cross-functional collaboration and fast time to value. With an emphasis on both people and process, as well as the empowering platform technologies that underlie it, a DataOps process allows each collaborating group to increase productivity by focusing on their core competencies while enabling an agile, iterative workflow.
Moreover, applying agile methodologies to analytics and machine learning lifecycle is a significant opportunity, but it requires redefining some terms and concepts. For example:
- Instead of an agile product owner, an agile data science team may be led by an analytics owner who is responsible for driving business outcomes from the insights delivered
- Data science teams sometimes complete new user stories with improvements to dashboards and other tools, but more broadly, they deliver actionable insights, improved data quality, Dataops automation, enhanced data governance, and other deliverables. The analytics owner and team should capture the underlying requirements for all these deliverables in the backlog
- Agile data science teams should be multidisciplinary and may include Dataops engineers, data modelers, database developers, data governance specialists, data scientists, citizen data scientists, data stewards, statisticians, and machine learning experts. The team makeup depends on the scope of work and the complexity of data and analytics required
Agility is going to be adopted by more data science and DataOps project teams soon. Many data scientists have reported that agility makes them more productive. This is not because the data scientists have become more skillful, but because agility can help them optimize their projects. Instead of spending time on models that are unlikely to reveal any productive results, it is better to spend that time for other result-driven purposes.
Being “agile” (flexible) means you need to adopt a dynamic approach in planning and be adaptable to the changing needs of the new situation when it arises. An agile environment appeals to quick action, fail quickly, evaluate and learn, then try again using a different approach or an improved method. It works great in dynamic environments where there is a potential for changing or evolving requirements.