Agile and scrum enable the rapid, iterative delivery of complex systems. With origins in lean manufacturing, they are well-suited to modern data-driven software projects. This comprehensive guide examines agile and scrum methodologies, clarifying their histories, values, practices and usage in analytics/AI initiatives.
History and Origins
Agile grew out of frustration with rigid, documentation-heavy software processes. In 2001, 17 software practitioners met in Utah to find better ways of developing complex systems. Their discussions led to the Manifesto for Agile Software Development, outlining 4 values and 12 principles that place the highest priority on satisfying customers through early and continuous delivery of valuable software.
Prominent agile approaches like scrum, Kanban and extreme programming took off in subsequent years. Of these, scrum with its focus on product ownership, self-organizing teams and accountable, empirical process control has become widely adopted.
Scrum traces its roots back to 1986 when Hirotaka Takeuchi and Ikujiro Nonaka published a Harvard Business Review article called the “New New Product Development Game”. They described how “scrum teams” and flexible,speed-driven processes used in manufacturing could inspire software creation. These techniques matured into the scrum framework for agile delivery.
The Agile Mindset
Several key ideas define the agile philosophy:
- Delivering working software frequently, from every couple of weeks to a couple of months
- Incorporating customer input and adapting to changes even late in development
- Empowering teams to self-organize and choose how to accomplish their goals
- Producing simple, valuable results over exhaustive documentation
- Regularly reflecting and tuning the ways of working
This mindset enables creative solutions even as needs shift and highly motived teams.
Many parallels can be drawn to the building of machine learning systems. Models must be iteratively trained and evaluated in tiny increments. Real-world data leads to continuous feature and architecture tweaks. The underlying code is refactored mercilessly while being kept simple. Data science and agile clearly share cognitive links!
Scrum Roles and Responsibilities
Scrum defines specific roles to deliver products using agile ideals:
- The Product Owner represents stakeholders, prioritizes features to work on next based on importance and value to users
- The Scrum Master guides the team, clears roadblocks, facilitates meetings and ensures the scrum process is followed
- The Team builds the product iteratively across a series of fixed-length sprints, owning collective responsibility
In data science terms, we can draw analogies to roles like the Analytics Product Manager, Data Coach and Machine Learning Engineers. Clarity on responsibilities allows scrum teams to stay aligned on goals.
Scrum Artifacts for Tracking Progress
Scrum also defines a few key artifacts visible to the whole team for coordination and monitoring:
- The Product Backlog contains an ordered, ever-evolving list of features needed in the product
- The Sprint Backlog details the backlog items committed by the team to complete in a single sprint
- The Increment represents the integrated sum of all user stories/functionality done in a sprint
These provide transparency on what the most important items are to work on next and track progress sprint-over-sprint.
Internal Events Driving Delivery
Time-boxed events promote regular inspection, synchronization and improvement:
- Sprint Planning Meeting – Entire team agrees on work for upcoming sprint
- Daily Standup – Short daily sync on accomplishments, next goals, blocks
- Sprint Reviews – Review increment with stakeholders, adjust backlogs
- Sprint Retrospectives – Reflect on people, process & technology and identify areas for growth
These fast, feedback-driven cycles inspect-and-adapt frequently for maximum value. AI systems similarly learn best when input datasets arrive in small batches.
Differences: Agile Values vs Scrum Practices
Agile provides overarching values and premises while scrum offers exacting rules and steps for execution:
- Agile development guidelines are flexible to suit teams. Scrum prescribes specific roles, artifacts and rituals to follow
- Agile recommends attitudes like quick releases or feature teams. Scrum gives formulas like 4 week sprints containing user stories
- Agile is open to interpretation. Scrum says the Product Owner ranks pending work and Team commits sprint goals
- Agile focuses more on mindsets. Scrum provides fixed workflows and metrics for accountability
Essentially agile governs direction while scrum governs execution.
Research Studies on Business Impact
Recent reports substantiate strong ROI from agile and scrum adoption:
- Teams working agile processes experienced a 50% increase in productivity over traditional methods per peer reviewed academic paper
- Agile transformation in a Fortune 100 company yielded cost savings of $75M+ attributed to faster release cycles and automation
- Forbes survey indicates 80% of companies using agile said it improved resource allocation and planning predictability
- Study showed only 2% negative responses on agile impacting performance metrics like time-to-market, quality, team morale or costs
The data convincingly associates agile values and scrum practices with speed, flexibility and competitive advantages for technology-driven organizations.
Worldwide Growth Trends
Industry research depicts rising agile/scrum permeation globally:
- Scrum adoption among agile practitioners stands at 86% as per State of Agile report with 95% satisfaction rate among scrum users
- 70% of companies follow agile-only delivery while 38% take a hybrid agile/waterfall approach per Digital.ai
- Over 80% of Indian IT sector has adopted agile over past decade, enabled by expanding skill pools
- In a VersionOne study, 95% of APAC organizations leverage agile improve collaboration and employee engagement
These metrics convey how agile initiatives now encompass virtually all technology sub-verticals.
Steps to Get Started
For teams starting their agile journey, I suggest:
- Securing senior management buy-in for necessary mindset shifts
- Participating in formal scrum training before kicking off development sprints
- Defining agile transition milestones with measurable targets and outcomes
- Piloting practices like time-boxed sprints and retrospectives with small project teams before scaling
- Allowing a period of learning, adjustment and course correction as proficiency develops
The path to agility requires patience! Begin by absorbing critical concepts, benchmarking against peers and customizing techniques to your environment.
In Summary
Born from similar philosophies of continuous improvement and customer focus, agile and scrum are driving lean, adaptive development well beyond traditional software teams.
As pillars for innovation, they enable data scientists, analysts and modern technology organizations to build fantastic data products faster. Approached strategically, they pave the road to becoming truly insight and metrics-driven businesses well into the future.
