Analytics-driven approach to agile software product delivery
Date
2020-09-05
DOI
Authors
Boncheva, Bogdana
Ivanov, Penko
Version
Embargo Date
2021-09-23
OA Version
Citation
Boncheva, Bogdana and Ivanov, Penko. 2020. Analytics-driven approach to agile software product delivery. Computer Science and Education in Computer Science 16th Annual International Conference CSECS 2020, Sept. 5th 2020, online, ISSN 2603-4794
Abstract
Two key factors drive the software product
delivery - the ideas for new products, and the latest approaches
for optimized development. This paper focuses on the software
development process and shows how data analytics enable
innovation and efficiency in the delivery of a new product.
The authors recommend the tools and techniques they have
tested and proved successful in an international product
organization within one of the leading media companies in the
world. The presented analysis addresses the challenges of the
standard practices in agile software development - continuous
incremental product delivery and integration. This iterative
approach implies developing and delivering features before a
product, or even a product vision, are entirely complete. The
method gains continuous feedback from the customer and
adjusted revenue projections from the organization. The success
of the approach relies on frequent and prompt decision-making
by stakeholders from various backgrounds and with different
skill sets.
These decisions need to be well-informed as they drive rapid
changes in the work prioritization and scope, and in the focus of
the software development team—those frequent shifts in
direction impact the delivery time and the quality of the
product. Decisions on affecting the different elements of the
engineering teams’ effectiveness rely on cumulative information
about the teams’ capacity, lead time and throughput.
This paper showcases how data analytics can drive prompt
decisions and enable the necessary flexibility and improved
efficiency. The authors demonstrate adapting the data
visualization to the different audiences according to their
interests and levels of expertise: customers, senior management,
engineering teams. The paper advises how to choose the right
data sets and make the correct assumptions for the data
interpretation. The authors’ extensive practice shows these are
the prerequisites to making the right decisions and delivering
the impactful products that make an organization stand out.