Software development cycles and data analytics

This executive summary will discuss the synergy between software development cycles and data analytics and explain why data analytics should be applied to any software development life cycle (SDLC). Our example SDLC chosen for this assignment is Waterfall Methodology. This method follows a sequential order which progresses from one phase to another in an orderly fashion. The phases include Requirements Analysis, Design Specifications, Implementation & Coding, System Testing, Deployment & Maintenance. We recommend applying data analytics at each stage of the SDLC process in order to measure success metrics including timeline adherence and quality assurance issues such as bug tracking for faster resolution times. Additionally, it provides stakeholders with visualizations such as Gantt charts or burn-down charts that can be used during critical decision-making points throughout the project lifecycle. Data analytics enables teams to identify areas where improvement can occur while providing visibility on tasks that have been completed or are still outstanding thus allowing them to adjust resources accordingly. Further use of data analysis techniques such as predictive modeling can help anticipate risks associated with changes made during different stages of a project’s lifecycle before they actually occur thus enabling proactive risk management strategies by decision makers instead of reactive ones once issues arise – saving time & money in the long run. The application of data analytics within Software Development Life Cycles is essential for successful project management as it helps visualize complex processes over different spans of time; track objectives; identify gaps; provide early warnings about potential bottlenecks; allocate resources more efficiently; reduce costs associated with implementation delays; improve product quality & customer experience; streamline operational efficiency through automation opportunities – all resulting in greater ROI than prior methods traditionally used such as manual observations & inspections by project teams leading towards accelerated delivery timelines while sustaining high levels customer satisfaction due increase predictability margins achieved via deep dive analyses conducted thanks leveraging power big datasets generated along entire SDLC approach used nowadays present day organizations around world eager keep pace latest developments industry without ever compromising either security integrity company's IT products services provided its customers partners alike., Various types open source proprietary tools available market specifically designed cater needs modern day businesses seeking apply advanced predictive machine learning models commercial grade applications could also employed facilitate said goals being pursued favor enhance overall maturity corporate performance standards set forth respective boards directors higher level executives tasked managing overseeing daily activities firm stakeholders entrust their trust reputations welfare hands suitable professionals capable carrying out requested functions utmost degree accuracy precision expected results desired end user experience outcomes desired outcome ultimately reached satisfactory manner ensuring sustained growth profitability organization concerned adopting even better practices near future hopefully bring tangible benefits cost savings well raise bar customer expectations bar already set place start today enjoy fruits labor going reap over next couple years come alike helping win crucial race survival staying top game competition continuously intensifying these days cut throat industry dealing technology based products solutions possible emerge victorious every coming round challenge worthwhile pursuing slightly daunting yet exciting prospect wait see what comes ahead!