Yurii Chaika: Data-Driven Management & Business Intelligence in PMO context (UA)

Yurii Chaika: Data-Driven Management & Business Intelligence in PMO context (UA)

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  Data-Driven Management & BusinessIntelligence In PMO context (UA)
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  ●Over 10 yearsof managerial experience, focusing on digital transformation, e-commerce, healthcare and CPG industries ●Sense of ownership as a mindset; ●Value-driven and data-driven decisions as a daily routine; ●Proven expertise in various methodologies (Scrum, Kanban, SAFe, Nexus); ●Extensive practical knowledge of PMI & ITIL standards and best practices; ●Keen of management 3.0 philosophy, people culture & proactivity enablement practices. About myself Yurii Chaika Kyiv, Ukraine LinkedIn
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  Agenda for presentation 0 1 0 2 0 3 0 4 0 5 0 6 0 7 BusinessIntelligence as a practice overview Operational,tactical and strategic data traceability Data-wise management as part of PMO Excellence Parameter specification (best/worst cases) Predict unpredictable,measure immeasurable Evolving landscape of BI within PMO context Conclusion, Q&A and useful links
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  Atlassian Jira Tableau Microsoft PowerBI Businessintelligence (BI) is the process of collecting, analyzing, and transforming raw data into actionable insights that can help businesses make data-driven decisions. It involves using technology, processes, and people to gain a deeper understanding of business performance, identify trends, and uncover opportunities for improvement. Practice tools Actions Data sources Systems Tables/ sheets Data collection Data analysis BI output/visualiz ation Charts Modelling,design ,etc ETL,API,scripts,etc Action plan BI common flow Business Intelligence as practice overview
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  Enhances the transparencyand data- driven communications Keeps focus on objectives Focuses on concrete measurable areas and clear data gathering Driven by operations demand Enables diverse and wise cross- functional communications Helps to visualize, compare and predict key trends Business Intelligence key benefits
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  Following bottom-top decomposeddata and ensuring BI is capable on transforming it to high-level insights Bottom-top vector Layer Supplied by Consumer Report/output examples Bringing strategic insights to key stakeholders Unification with account/cluster - driven inputs through data analyze Defines account/cluster-related metrics built based on particular inputs Enables project-layer data to be supplied and converted into tactical insights Billing effectiveness P&L reports BCP Top-tier decisions NPS/CSAT SWOT report Account P&L Project map Industry/solution New/acquired revenue Forecast Revenue leakage Delivery Health Risks management Compliance audits EVM Resource plan Profit margin Assignation/Forecast Leadership Cluster/BU/Account PMO Organization-wise BI Accounts/clusters insights PMO Monitoring/controlling tools Data merge / analyze (BI) Project/Delivery manager Project-based data Operational data Ongoing inputs/updates Organization - base BI Account/cluster - base BI Project-base BI Strategic layer Tactical layer Operational layer Operational,tactical,strategic BI traceability
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  When What ● Answering thequestion which metrics we are going to gather ● Making descriptions, formulas, requirements ● Clarifying the cadence of results revision upon taking decisions ● Adopting the cadence for inputs update in data sources Why ● Clarifying the reasons behind exact metric measurement ● Comparing the results and taking decisions How ● Deciding on data-related aspect ● Deciding on expected visualization Data-wise management main questions
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  Why Finance Delivery Risks People Finance forecast accuracy, EVM,etc Assuring project following defined standard Keeping on control high- severity risks/oppo, monitoring deadlines Track the size of engagement and potential turnover % What How Revenue plan/fact Allocation plan compared to actual invoices paid Delivery excellence Compliance audit results Risks, issues,opportunities Logged risks/issues/oppos Headcount,turnover Assignation,assignation requests Customer Monitor customer satisfaction/loyalty NPS,CSAT Survey results When Weekly/Monthly base, YTD Quarter-base, monthly-base (new project) Daily,compared to deadline/revision date Weekly-to-monthly base Daily, quarterly, depending on survey lifecycle Each section requires additional and detailed clarification/modelling before actual BI implementation Data-wise modelling (brief)
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  Recommended Not recommended Canbe simple transformed into tactical data (EVM, milestones accuracy,seniority pyramid) Maintained and related to concrete process (risk log, resource allocation) Helps to highlight/predict/argument some case (turnover,team health,NPS,delivery health, time spent on) Linked to events/ceremonies (estimation accuracy, committed/completed, DE compliance) Hard to transform into insights Not related to day-to-day operations Well-described and maintained (budget spent/burnout, reopened defects,etc) Metric for the sake of the metric In short - those, which can be converted into more tactical data and based not on concrete project specifics, but on overall delivery success Parameter specification best/worst cases Requires extra overhead to maintain Related to project mostly/hard to describe
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  Predict unpredictable, measureimmeasurable (BI) Delivery health Open risks Compliance audit report Milestones monitoring Revenue forecast Maintenance monitor Delivery ____
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  Predict unpredictable, measureimmeasurable (Jira)
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  CUSTOMER SUCCESS Evolving landscape ofBI within PMO context Some changes are Organizational demand from PMO requires to be made around value (converted to profit, customer success, diverse consumption Methodologies,pra ctices, capabilities variability AI-driven (predictability, simplicity, data capture) Various data channels and high demand on structurisation Continuous growing landscape of uncertainty
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  Q&A
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  ●PowerBI LinkedIN group ●PowerBIguides from Microsoft ●Tableau learning guides ●Jira EazyBI ●Jira EazyBI plugin ●Udemy courses on PowerBI Useful links
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  Empowering tech solutions! spd.tech connect withus [email protected] [email protected] +442039661640 Seattle, the US London, the UK Tel Aviv, IL Kyiv, UA