Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

Agile Case Study Evaluation In Middle Size Project

In the last few years Agile methodologies appeared as a reaction to traditional ways of developing software and acknowledge the need for an alternative to documentation driven, heavyweight software development processes. This paper shortly presents a combination between Rational Uni ed Process and an agile approach for software development of e-business applications. The resulting approach is described stressing on the strong aspects of both combined methodologies. The article provides a case study of the proposed methodology which was developed and executed in a successful e-project in the area of the embedded systems

Analytics-driven approach to agile software product delivery

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

Analyses of an agile methodology implementation

In the last few years Agile methodologies appeared as a reaction to traditional ways of developing software and acknowledge the need for an alternative to documentation driven, heavyweight software development processes. This paper shortly presents an agile approach for software development of e-business applications. The approach, named eXPERT, is applicable to small teams developing projects characterised by often changing requirements, tight schedules, and high quality demands. The present article describes a case study about eXPERT approach implementation at software developing company. Experiment results based on preliminary defined series of metrics are presented and analyzed