Agile transformation metering

Why precise metering is essential for agile transformation success

Contemporary highly competitive and constantly changing business environment requires an ability of quick adjustment, pivoting or adopting or rolling-out of new services, products, business or commercial models. In this context agility becomes consequently a crucial attribute for organizations striving to stay relevant and responsive to ever-evolving customer needs. Agile transformation, both in terms of organizational culture and product development methodologies, offers a multitude of key benefits that can significantly enhance an organization’s ability to succeed and thrive.

Agility in essence enables companies to enhance flexibility and ability to quick change, adaptability, achieving faster time-to-market and increased customer satisfaction with customer-centric approach.

We believe that efficient metering and early warning systems based on precise AI-predictions can become a real game changer and can ensure your agile transformation journey stays on track towards desirable result and significantly improve the outcome ensuring competitive advantage for your product development providing full transparency on performance, outcomes, efforts allocation, release forecasts, etc on all levels and end-to-end.

Make your agile developments transparent and predictable

our focus areas

Metrics, reports, dashboards

Requirement analysis for agile reporting, process analysis
Definition key metrics and measure points of agile development process (process efficiency, flow, output and outcome metrics)
Creation of actionable reports and dashboards giving insights and real picture of the progress (Jira, MS PowerBI, SAP SAC, tableau, etc)
Definition of critical agile reporting areas and audiences / frequencies for consumption

AI-based forecasts on key metrics

Develop, train & deploy AI-models for accurate prediction of key metrics enabling of precise forecasts and decision making far in advance
Use AI-predictions and integrate it into agile processes like iteration planning, capacity or resource utilization planning, agile estimations, etc.
Build accurate AI-forecasts of scope completion towards next release/iteration date for stakeholder communication
Achieving preciser roadmap planning due to combination of forecasts with adopted estimation techinques (t-shirt sizing, story points etc)

Visualize progress

Building reports on team level: sprint burndowns, velocity, throughput,
Handling of scope – sprint velocity vs scope velocity, scope creep early detection
Reports on the level of Team of Teams (ARTs) – output metrics
Progress towards next release, identifying teams waste, etc.
Visualizing product roadmap (increment/release/sprint/iteration – results or plans
Visualizing effort distribution by issue types, CFDs, etc
Value delivery (features, value points)
Monitoring setup (backlog health-check)

Analytical tool selection & customizing

Requirements assessment and reporting tool evaluation
Customizing of reports and dashboards in agile operational tools (jira atlassian, etc)
Providing our own best practice report templates on inteltarget.com dashboards platform (from team up to ART and portfolio reportings)
Development of tailored dashboards on inteltarget.com or customer platform
Maintenance and ad-hoc reporting customizing (Microsoft Power BI, SAP SAC, tableau, plotly, own developments)
Operationalization of reporting & derivation of actions

insight areas

Discover areas which bring you insight & transparency in product development cycle

- How fast do the teams deliver their work? Number of delivered story points, user stories, features per sprint, release, increment
- Are there any trends / patterns in teams velocity over sprints?
- How fast are teams processing their scope? Focus & efficiency
- What is the ratio of work on scope/work on defects, operations/maintenance, waste?
- Do teams deliver what they commit, what is their predictability / sprint success ratio (committed vs. delivered)?
- What is our sprint burn-down?
- Which of the teams perform better and which are more efficient?

- What is the progress on iteration / release scope (sprint by sprint)?
- What is the current flow load?
- What is the distribution of my features across workflow stages (CFD)?
- What is the number of added/outscoped features in release/increment (spill-overs)?
- Status of the features in scope. Status tracking
- What is the iteration progress (burn-up)
- What is the progress on features/epics/user stories (sprint by sprint)

- What is team capacity for the next sprint/iteration?
- What is the forecast on iteration scope delivery given the average and forecasted velocity?
- Will teams manage to deliver the scope in time?
- If not what should be the actions?
- What is the forecasted increment burn-up?
- Which teams are overplanned, how heavily and which have free capacity?