The intelligence layer: The brains of Digital.ai's VSM platform
Discover the intelligence layer, part of Digital.ai’s powerful analytics platform backed by state-of-the-art AI and machine learning (ML).
Value stream management: The what and why
This webinar will show you the what and why of value stream management and a few best practices for adopting value stream management.
Today, we are focusing on the role the intelligence layer plays within the overall VSM framework. The intelligence layer is part of Digital.ai’s powerful analytics platform backed by state-of-the-art AI and machine learning (ML). Intelligence layer solutions come pre-integrated with all common systems of record, including tools and environments not made by Digital.ai. This data is represented in actionable dashboard views through individual orchestration layer products — but the data really shines when used as fuel for powerful AI/ML engines.
There are, currently, four main VSM intelligent analytics solutions currently available:
- Digital.ai Flow Acceleration
- Digital.ai Quality Improvement
- Digital.ai Change Risk Prediction
- Digital.ai Service Management Process Optimization
All engines use historical data to reveal patterns within your current organizational environment, especially within DevOps. By leveraging data and AI/ML, organizations glean insights to become more proactive, improving outcomes and processes in order to facilitate more efficient value creation while also freeing resources for innovation. The overall effect is the potential to earn greater market share in your vertical while running on a leaner, smoother operation run with fewer obstacles to value creation.
Digital.ai Flow Acceleration
The Digital.ai Flow Acceleration solution leverages Agile and DevOps process data to identify key factors affecting the rate of releases and the quality of those releases. Development leaders can then address these factors to improve release velocity.
Use your own DevOps data to model the factors that tend to lead to late software delivery. Identify trends in order to anticipate late releases while addressing workflow issues that can cause longer lead times. Every team's factors are different, and can include feature types, change failure rates, seasonal factors (e.g. time in the quarterly cycle), and on down to specific teams and individuals.
Reveal the bottlenecks and dependencies that tend to contribute the most to lagging lead times. Pinpoint which activities tend to consume the most resources while contributing the least to value creation. These insights allow IT leaders to streamline processes, improve automation, and employ other solutions to reduce the barriers to speedy release.
- Identifying bottlenecks and dependencies across teams
- Pinpointing the non-value-add activities that consume resources
- Standardizing and streamlining release processes
You can even learn how to predict sprint cadences and other cycle time metrics with better accuracy, even across a calendar with high levels of variability. This model allows your teams to impose more standardization, streamlining release processes while establishing a rhythm among teams, making releases more consistent and predictable.
Overall benefits of Digital.ai Flow Acceleration include:
- Increased throughput
- Reduce lead times
- Amplify business value
- Accelerated agility
- Reduce non-value-added work
- Improve team productivity
- Reduced risk
- Detect at-risk releases and dates early
- Improve release predictability and consistency
Digital.ai Quality Improvement
The Digital.ai Quality Improvement solution helps your development and quality teams address the factors that tend to have the highest impact on release quality. Example insights that could be uncovered include which areas of the product tend to have the most incomplete or ineffective test coverage.
Model release quality trends using historical data to understand which factors tend to impact quality or lead to escaped defects and release rollbacks. Use this data to predict poor quality releases, address key sources of quality compromise, and prioritize the quality improvements that will lead to the most added value for end customers.
Using the Digital.ai Quality Improvement engine, organizations can:
- Proactively find and fix systemic issues with software quality across the development lifecycle, before they have the chance to impact delivery.
- Eliminate bottlenecks and minimize testing inefficiencies, improving quality and the digital customer experience while lowering application support costs.
- Establish consistent quality improvement practices across the organization by measuring success factors in the best-performing value streams.
Detect test coverage gaps in production and during QA stages to identify sources of diminished value delivery. Analyze historic data on defects escaped into production, including the main drivers of defect risk. Drill down and analyze risk factors in order to determine systemic root causes. This will enable targeted action and learning. Identify pipelines that tend to generate quality issues and contribute to longer lead times, enabling targeted action to improve delivery cadence as well as quality.
Overall benefits of Digital.ai Quality Improvement include:
- Reduced rework
- Prevent defect leakage
- Increase test effectiveness
- Improved quality
- Improve pipeline quality
- Increase code coverage
- Improve the digital customer experience
- Reduced release risk
- Predict release quality risks
- Lower frequency of release rollbacks
Digital.ai Change Risk Prediction
The Digital.ai Change Risk Prediction solution helps you identify risky changes and proactively take steps to manage and reduce risk or prepare immediate remediation. Investigate and uncover systemic causes of IT change failure which could stem from either people, processes, and technology. Use the built-in AI/machine learning models to manage and reduce change failure rates and associated incident mean time to resolution (MTTR). Dive deep into all historical data to understand which factors contribute the most to change risk, including specific teams and tasks.
This solution helps organizations understand the main drivers of change failures in order to reduce their risk. At the same time, accelerates CAB review and QA stages by revealing which change factors contribute to risk and which do not. In turn, deployment teams can better understand which types of change approvals can be automated, and which should be separated into their own releases to enable more efficient review.
The Digital.ai Change Risk Prediction engine maps complex and constantly evolving IT environments. The result is that the most accurate depiction of the pipeline is understood. The model helps both development and operations highlight all of the important CIs to the production environment. It also allows them to better understand numerous and complicated dependencies
Predict IT change risks to correct them before deployment or prepare for their presence in production. Analyze change impacts, upstream and downstream, to pinpoint sources of performance degradation or change failure and rapidly remediate. Formulate hypotheses about the upstream causes of IT change failure during application development, and correlate which changes have specific downstream impacts, as directly measured by user experience and reported incidents.
Overall Benefits Include:
- Increase efficiency
- Reduce rework and recovery costs
- Focus resources on truly risky changes
- Lower MTTR of IT change-related incidents
- Accelerate agility
- Shorten change lead times
- Make changes more frequently
- Be more responsive to business needs
- Increase CAB efficiency
- Reduce risk
- Identify and fix systemic causes of change failure
- Predict ITIL change management failure and mitigate risk
- Make data-driven decisions on resource allocation
Digital.ai Service Management Process Optimization
The Digital.ai IT Service Management Process Optimization ITSM solution helps organizations go beyond in-app reporting and adopt proven analytics-driven best practices for improved incident, problem, and IT service request management.
IT leaders can use the generated models and insights to break down silos across fragmented internal and external teams. They can then make data-driven decisions on how to best accelerate IT service delivery while improving service satisfaction.
Break through siloed in-app reporting by aggregating all relevant data into one repository. Next, apply advanced AI/ML analytics. Predict major incidents by modeling their historic risk factors. Use artificial intelligence and machine learning models to provide an early warning system, highlighting which applications have the highest risk of a major incident. Identify and operationalize opportunities to prevent service impact and improve restoration time.
Improve IT service customer experiences by looking to the issue/incident types that generate the most pain at the highest volume. Make use of technology like natural language processing-driven topic clustering to identify monitor trends, uncover opportunities for automation, manage problems intelligently, and reduce mean time to restore (MTTR) by revealing likely root causes and then prescribing next-best actions. By combining process mining with factors like incident reassignment analytics, IT teams can improve the service experience of end customers while shifting left service responses, lowering MTTR and eliminating sources of ITSM process inefficiencies.
The Digital.ai Service Management Process Optimization engine is also capable of tracking vendor performance and SLA adherence, ensuring service satisfaction remains high by accounting for the most important internal and external factors. By providing a single source of truth for all ITSM operatives, performance and KPI transparency can drive improvements towards the highest priority organizational objectives.
Benefits of the engine include:
- Increase efficiency
- Improve MTTR and incident resolution efficiency
- Hold service providers accountable
- Find and eliminate the true incident root cause
- Accelerate agility
- Proactively identify opportunities for automation
- Align vendor relationships with strategic objectives
- Improve collaboration between IT and service teams
- Reduce risk
- Predict and prevent IT service disruptions
- Identify and fix underperforming and problematic configuration items
- Rapidly remediate SLA breaches
True intelligence, powered by your own data
Digital.ai Intelligence Layer solutions deliver on the promises offered by big data and analytics for the past decade.
- Move from opacity to transparency with aggregated data analytics
- Move from reactive to proactive by leveraging predictive and prescriptive data modeling
- Move from inconsistency to consistency across organizational IT practices
Overall, the engines allow teams to deliver more value more consistently across DevOps and the entire organization, thanks to powerful capabilities that reveal more than siloed in-app environments could ever hope to on their own. These technologies represent the next level of AI advancement and show the capability of what your data can do when it is harnessed, all in one place, using some of the most sophisticated descriptive, predictive, and prescriptive modeling techniques available.
The end result is an organization that is smarter about what factors tend to positively or negatively impact release quality, service delivery, and other key performance areas — enabling them to seek the outcomes of continued improvement based on the metrics that matter most.
For even more about the Intelligence Layer see the full breakdown here.