We’ve transitioned the traditional and custom IT tools and features in Microsoft service-desk into ServiceNow ITSM. This has led to innovation in many areas of our IT help-desk management, including improving accessibility, incident management, IT workflows and processes, service level agreements (SLAs), use of AI/ML, virtual agents, automation, and knowledge across the IT help-desk organization data visualization, monitoring and reporting.

In short, our strategic partnership with ServiceNow is helping us improve the efficacy of our internal IT help-desk environment and for our mutual customers.

Working together to accelerate digital transformation

Our Microsoft Global Helpdesk team supports more than 170,000 employees and partners in more than 150 countries and regions. We deploy this new ITSM environment at enterprise scale, supporting more than 3,000 incoming user requests each day.

We collaborate with ServiceNow as a partner to accelerate our digital IT transformation and continually increase the effectiveness of our IT service management. Our Global IT Helpdesk recognizes potential improvements, provides feedback to ServiceNow, and tests new features. We receive accelerated responses to our ITSM solution requirements while ServiceNow gets valuable, large-scale feedback mechanism to continuously improve their platform.

[Explore how we’re streamlining vendor assessment with ServiceNow VRM at Microsoft. | Discover how we’re instrumenting ServiceNow with Microsoft Azure Monitor. | Unpack how we’re using Microsoft Teams and ServiceNow to enhance end-user support.]

Modernizing the internal support experience

In the past, when our internal support scale, business processes, or other factors demanded functionality that existing platforms and systems couldn’t support, our engineers would develop tools and applications to supply the required functionality. Many ITSM features at Microsoft were developed in this manner. With ServiceNow now providing the core ITSM functionality we need, we are working together to integrate our tools’ functionality into their platform, which provides a unified IT help-desk experience that is scalable with enhanced productivity and accelerated digital IT transformation.

ServiceNow enables Microsoft to integrate its digital environment with ServiceNow ITSM functionality and Microsoft uses out-of-the-box ServiceNow functionality whenever suitable. ServiceNow adds and improves functionality, often based on Microsoft feedback and development, and then Microsoft uses the resulting improved capabilities to replace internally developed tools and processes. This collaborative relationship on ITSM benefits both organizations and our mutual customers.

Collaborating to create rapid innovation

In some cases, Microsoft-developed tools are the starting point for new ServiceNow functionality, such as the recent implementation of ServiceNow ITSM Predictive Intelligence.

We initially built an experimental machine learning-based solution in our environment that automatically routed a limited number of helpdesk incidents in ServiceNow by using machine learning and AI. This reduced the amount of manual triage that our support agents had to perform and helped us learn about incident routing with predictive intelligence and identify innovation opportunities.

We then took those learnings and shared them with ServiceNow to help them make improvements to the ServiceNow ITSM Predictive Intelligence out-of-the-box platform tool. By progressing from our experimental solution to ServiceNow ITSM Predictive Intelligence, we benefitted from the out-of-the-box flexibility and scalability we needed to drive adoption of predictive intelligence within our helpdesk services landscape. We’ll use our work with ServiceNow ITSM Predictive Intelligence throughout this case study to highlight the core steps in our journey toward an improved internal support experience.

Establishing practical goals for modernized support

Predictive intelligence is one example among dozens of ITSM modernization efforts that are ongoing between ServiceNow and Microsoft. Other examples include virtual-agent integration, sentiment analysis of user interaction, anomaly detection, troubleshooting workflows, playbooks, and integrated natural-language processing. Enhancing our helpdesk support agent experience by using ServiceNow ITSM involves three key areas of focus: automation, monitoring, and self-help.

Automation

We’re automating processes, including mundane and time-consuming tasks, such as triaging incidents. Automation gives time back to our helpdesk agents and helps them focus on tasks that are best suited to their skill sets. Feature implementation examples include orchestration, virtual agents, and machine learning.

We’re using ServiceNow Playbooks for step-by-step guidance to resolve service incidents. Playbooks allow our agents to follow guided workflows for common support problems. Many playbooks, such as the password-reset process, include automated steps that reduce the likelihood of human error and decrease mean time to resolution (MTTR).

Monitoring

We use monitoring to derive better context and provide proactive responses to ServiceNow activity. Enhanced monitoring capabilities increase service-desk responsiveness and helpdesk agent productivity. Feature implementation examples include trigger-automated proactive remediation, improved knowledge cataloging, and trend identification.

Microsoft Endpoint Manager supplies mobile-device and application management for our end-user devices, and we’ve worked with ServiceNow to connect Endpoint Manager data and functionality into the ITSM environment. This data and functionality supplies device context, alerts, and performance data to ServiceNow, giving device-related details to support agents directly within a ServiceNow incident.

Self-help functionality

Self-service capabilities help our support incident requestors help themselves, by supplying simplified access to resources that guide them toward remediation. It frees up IT helpdesk agents from performing tasks that end users can do and lowers the total cost of ownership, as support-team resources can focus on more impactful initiatives. Feature implementation examples include natural language understanding, context-based interaction, bot-to-bot interactions, and incident deflection.

For example, the ServiceNow Virtual Agent integrates with Microsoft Teams for bot-to-bot interactions. Bot integration and bot-to-bot handoff enable us to continue using the considerable number of bots already in use across the organization, presenting self-help options for our users that best meet their needs. We have also collaborated with ServiceNow to create integration with knowledge and AI capabilities from Microsoft 365 support. Microsoft 365 service-health information, recommended solutions for Microsoft 365 issues, and incident-related data are available in ServiceNow to both end users and agents.

Examining the modern support experience in context

We have a holistic approach to unifying its internal service-desk platform under ServiceNow. The functionality and health of our Global Helpdesk organization drives the experience for our support agents and the people they assist. To Identify opportunities for improvement, we examined all aspects of our support environment, making observations about tool usage, overall experience of support agents, and potential gaps in the toolset that our support agents use. When thinking about new capabilities, such as AI and automation, we needed to understand how our people work. Why and how we perform certain tasks or processes can lose relevance over time, and a deviation from the original way in which we do something can potentially lead to inefficiencies that we must regularly evaluate and address. We placed these observations into the following categories:

• Comprehensive best practices. We’re encouraging our Global Helpdesk team to be a strategic partner in business, design, and transition of support at Microsoft, rather than simply focusing on tactical ticketing and related metrics. Our internal support experience improvements in ServiceNow ITSM go beyond ticketing processes and require a holistic view of all aspects of the support-agent environment. Additionally, implementing new technologies is only one part of the bigger solution in which it’s critical to verify and keep people accountable for adhering to best practices. We’re transforming our Global Helpdesk operations to provide strategic value and achieve business goals alongside the more tactical elements of service-desk operation, such as incident management and resolution.
• Interaction management. Examining how our helpdesk agents and the people they support use ServiceNow ITSM and its associated functionality to drive interface improvements. It also helps identify new modalities to connect our support agents to the issues that our users are experiencing. Our goals include increasing virtual-agent usage and reducing use of less efficient interaction modalities, such as fielding IT support requests over the phone.
• Incident management. Incident management is the core of ServiceNow ITSM functionality and forms the basis for our largest set of considerations and observations. We examine how we create and manage support incidents, triage and distribute them, and then move them toward the final goal of resolution. In all of this, we assess how Global Helpdesk performs incident management and where it can improve. It’s important to understand the use of data to aid incident resolution, and how to better automate and streamline incident processes and consolidate other elements of service-desk functionality into the incident-management workflow. There are many incident-management factors that we evaluate including identifying incident origin, integrating virtual-agent interactions, increasing contextual data in incidents, automating incident routing, deflection and resolution, and improving incident search functionality.
• Knowledge management. We’re improving how our helpdesk agents and users access knowledge for IT support. Consolidating external knowledge sources into ServiceNow centralizes our knowledge management effort and makes the knowledge they contain available across other service-desk processes, such as incident management. Among the factors we’re focusing on are standardizing knowledge article content, supporting proactive knowledge creation, improving knowledge self-service capabilities, and including related knowledge content for incidents.
• Governance and platform management. The overall management of the ServiceNow ITSM platform and how it interacts with our environment and integrates into outside data sources and tools helps Microsoft use ServiceNow data to improve other business processes. We’re focusing on improving formal business processes and integrating with other processes and technology while aligning with Microsoft’s broader business strategies and standards.

Creating value within the helpdesk support experience

Microsoft and ServiceNow are intentionally and thoughtfully continuing to improve the ServiceNow environment, both from the organizational perspective here at Microsoft and from the product perspective at ServiceNow. For each feature and business need that we evaluate, we examine the feature from all applicable perspectives. Our general feature evaluation and migration process includes:

1. Evaluating business needs for applications and features. For each identified feature, we assess the associated business need. This helps us prioritize feature implementation and understand what we could accomplish based on available resources. ServiceNow Predictive Intelligence, our example in this case study, reduced mean time to resolution (MTTR) for incidents and freed up support-agent resources. These factors both positively influenced support agent efficiency and satisfaction. We’d already been using machine learning-based functionality, so the business need was clear.
2. Determining product roadmaps, organizational goals, and support requirements. In this step, we examine a feature’s practical implementation. Understanding how we need to address a feature or feature gap often depends on product roadmaps and feature development in-flight within ServiceNow. Early access to ServiceNow roadmaps and the ServiceNow Design Partnership Program helps guide our decision making as we determine the evolution of features and how they align with our future vision for digital transformation. If ServiceNow is already developing a specific feature in ITSM space, we don’t worry about integrating or recommending our internally developed tools or functionality. However, we often contribute to the improvement of ServiceNow features based on our internally developed tools, as we did with ServiceNow Predictive Intelligence.
   It can be complex to understand the state of ServiceNow with respect to a specific feature and its requirements. We must examine where we’ve customized ServiceNow ITSM to accommodate an internally developed solution and how we can roll back those changes when we retire the internally developed solution in favor of out-of-the-box functionality.
3. Identifying risks, benefits, and effects of migration. Establishing required resource allocation and determining necessary skill sets for the migration process is critical to understanding how each feature migration might affect our service-desk environment and overall ServiceNow functionality. Specific factors we consider include licensing requirements and quality control checks, both of which greatly influence the speed and order of feature migration. We also assess the effects of retiring legacy/custom tools on the Global Helpdesk and other Microsoft teams. Many tools we use were widely adopted and instrumental to daily operations, so we must consider training and transition processes on a feature-by-feature basis. In some cases, a feature or tool’s addition or removal could cause a shift in business processes, so it’s critical that we understand the potential impact. We do this by examining feature migration in the context of organizational goals, standards, and best practices.
4. Obtaining organizational support. One of the most crucial steps is to garner organizational buy-in. Although Microsoft and ServiceNow are strategic partners, it’s critical to get support from key stakeholders here at Microsoft, including our Global Helpdesk and Microsoft Digital process owners. Communication is critical. When we involve all stakeholders, we ensure that we account for all business and technical considerations.
   Rather than getting approval at a high level for the entire ServiceNow support-improvement project, we instead obtain approval for small pilots that focus on fast delivery and high value. This demonstrates the potential for a feature’s broader adoption at the Global Helpdesk. In our predictive-intelligence example, we started by engaging the Global Helpdesk team that was using the experimental machine learning-based incident-routing tool. The existing experimental tool was only routing some incidents, so we proposed a pilot to route the remaining tickets using ServiceNow ITSM Predictive Intelligence. We worked very closely with our internal support team to ensure that the solution met their needs. The pilot demonstrated the tool’s effectiveness in daily operations and proved the tool’s capabilities in production use. This built confidence and trust in the tool and helped drive broader adoption across the organization.
5. Establishing plans for transition, deallocation, and retirement of legacy tools and systems. We had critical decisions to make about retirement and deallocation of existing tools. Many feature transitions involved identifying how we would move or transform data. Addressing data access and security is a common challenge.
   Additionally, with Predictive Intelligence, our team needs real incidents to train the Predictive Intelligence algorithms. This involves moving production data into a development environment, which has security implications. The feature team must proactively engage our Microsoft security team to provide appropriate information. ServiceNow supplies detailed platform-security documentation, which helps us obtain security-related approval. Also, transition often requires retraining. We must arrange training for users of legacy systems so they can use the new features in ServiceNow and understand how the transition might affect their daily activities and overall service-desk operations.
6. Engaging in feature implementation. We implemented features following specific plans, processes, and best practices that we established. Implementation scope and effort varies depending on the feature, and in the case of Predictive Intelligence, the Microsoft development team began by creating a pilot. This enables the team to confirm that ServiceNow ITSM Predictive Intelligence can achieve the required level of routing accuracy. It also provided a proof of concept that enabled us to quickly find gaps in functionality.
   Starting with a prototype means we then have a functional example that’s up and running quickly so we get early feedback on the out-of-the-box capabilities. We were able to start fast, iterate, and deliver a better solution more quickly. However, we also had to examine and account for scalability within the ServiceNow platform to ensure that the solution would work well when widely adopted.
   Predictive Intelligence went live with a small number of incident-routing destinations, which helped build the confidence of the service-desk team. We then expanded the number of assignment groups as we received positive feedback. The rollout required minimal organizational change management because Predictive Intelligence was automating an existing process and the service-desk team was already using an experimental AI tool for automated routing.
7. Measure progress and review results. We measure all aspects of the feature-implementation progress. Identifying and enabling key metrics and reports helps build confidence and trust in each feature’s effectiveness. As we iterate changes and work through a pilot process for any given feature, we keep stakeholders involved and use our results to contribute to the broader digital transformation. It’s also critical for adoption and is an effective way to illustrate benefits and bring other teams onboard.

Integrating ServiceNow ITSM and Microsoft products

In addition to feature enhancement and growth of ServiceNow functionality, Microsoft and ServiceNow are working together to integrate our products with ServiceNow. This enables us to capitalize on their capabilities and make it easier for our customers at Microsoft to integrate ServiceNow into their environment. For example, device-management capability and reporting data from Microsoft Intune, Microsoft’s mobile device management platform, can integrate directly with ServiceNow. This integration improves contextual data within ServiceNow and extends ServiceNow’s capabilities by using Intune and Microsoft Endpoint Manager functionality on managed devices.

Our Microsoft Global Helpdesk team has observed significant benefits from the continued ServiceNow ITSM feature implementations, and we’re still working with ServiceNow on an extensive list of features that we want to implement. Some of the best benefits we’ve observed include:

• Increased business value. We’ve been able to retire custom solutions and the infrastructure that supports them, reducing total cost of ownership and management effort for legacy solutions. Consolidating our service-desk functionality in ServiceNow ITSM makes licensing and maintenance much more simple and more cost-effective.
• Reduced service-desk management effort. The various automation features we’ve implemented have reduced the effort our IT helpdesk agents exert, particularly with respect to mundane or repetitive tasks. AI and machine-learning capabilities have improved built-in decision making, reduced the potential for human error, and given time back to our helpdesk agents so they can focus on the work that demands their expertise. For example, ServiceNow ITSM Predictive Intelligence is routing incidents with 80 percent accuracy, saving considerable time and effort.
• Improved helpdesk agent experience. Unifying our tools and features within ServiceNow ITSM enabled us to create a more simple, easier-to-navigate toolset for our support agents. They can move between tasks and tools more effectively, which increases overall support responsiveness and makes our service desk more efficient.
• Reduced mean time to resolution. We’re experiencing a continual reduction in incident resolution as we integrate features and modernize the agent support experience. For example, ServiceNow ITSM Predictive Intelligence reduced MTTR by more than 10 percent, on average in our pilot project. Based on these numbers, we’re deploying Predictive Intelligence at a broader scale for Global Helpdesk.

While we’ve successfully migrated many internally developed capabilities into out-of-the-box ServiceNow ITSM features and tools, it is an ongoing process and we’re continuing to learn lessons about the migration process and successfully transforming the IT help-desk environment for greater efficiency and a more productive IT-agent experience. Some key lessons we’ve learned and continue to explore include:

• Start small and expand scope as a feature matures. We typically start feature implementation small with a single team or use-case scenario. We use pilot projects to validate a solution, prove feature completeness, and gather proof of concept to gain support from stakeholders. Each pilot project contributes to a broader improvement to ServiceNow functionality.
• Get buy-in from stakeholders early. Establishing organizational support is critical to the overall success of every feature implementation. We work hard to understand who our stakeholders are within Microsoft and make them aware of how a feature implementation might affect them—and ultimately improve our organization.
• Test scalability and establish monitoring early. Starting small results in many quick wins and rapid feature implementation. However, we must ensure that any capabilities we implement can scale to meet enterprise-level requirements, both in functionality and usability. Tracking metrics and maintaining accurate reporting using ServiceNow’s reporting capabilities provides concrete assessment of feature effectiveness as it increases in usage and scale.
• Don’t accept feature requirements at face value. Specific features are easy to quantify and qualify, but we always consider the bigger picture. We ask what business questions or challenges the requirements are addressing and then ensure our perspective always includes holistic business goals. We don’t simply want a granular implementation of a specific feature.

We’re working on a thorough list of feature integrations that include extensive use of AI and machine learning. This will simplify and strengthen predictive and automation capabilities in ServiceNow. We’re also investigating deeper integration between ServiceNow ITSM and Microsoft products including Microsoft 365, Microsoft Dynamics 365 and Azure.

We are excited that our joint efforts have introduced a rapid iteration of feature capability into the ServiceNow platform and the impact this brings to the ITSM industry.

• Read more about Predictive Intelligence with ServiceNow.
• Explore how we’re streamlining vendor assessment with ServiceNow VRM at Microsoft.
• Discover how we’re instrumenting ServiceNow with Microsoft Azure Monitor.
• Unpack how we’re using Microsoft Teams and ServiceNow to enhance end-user support.

The post Modernizing Microsoft’s internal Help Desk experience with ServiceNow appeared first on Inside Track Blog.

Like many enterprises, we use SAP—the global enterprise resource planning (ERP) software solution—to run our various business operations. Our SAP environment is critical to our business performance, and we integrate it into most of our business processes. SAP offers functionality for enterprise services at Microsoft, such as human resources, finance, supply-chain management, and commerce. We use a wide variety of SAP applications, including:

• SAP S/4HANA
• ERP Central Component (ECC)
• Global Trade Screening (GTS)
• Business Integrity Screening (BIS) on S4
• Master Data Governance (MDG) on S4
• Governance, Risk, Compliance (GRC)
• Revenue Management, Contract Accounting (RMCA)
• OEM Services (OER)
• SAP SaaS (Ariba, IBP, Concur, SuccessFactors)

Since 2018, Microsoft’s instance of SAP is 100 percent migrated to Microsoft Azure. This project entailed moving all SAP assets to more than 800 Azure virtual machines and numerous cloud services.

We approached the migration by using both vertical and horizontal strategies.

From a horizontal standpoint, we migrated systems in our SAP environment that were low risk—training systems, sandbox environments, and other systems that weren’t critical to our business function. We also looked at vertical stacks, taking entire parts of our SAP landscape and migrating them as a unified solution.

We gained experience with both migration scenarios, and we learned valuable lessons in the early migration stages that helped us smoothly transition critical systems later in the migration process.

[Unpack how we’re optimizing SAP for Microsoft Azure. | Discover how we’re protecting Microsoft’s SAP workload with Microsoft Sentinel. | Explore how we’re unlocking Microsoft’s SAP telemetry with Microsoft Azure.]

Operating as Microsoft Azure-native

At Microsoft, we develop and host all new SAP infrastructure and systems on Microsoft Azure. We’re using Azure–based cloud infrastructure and SAP–native software as a service (SaaS) solutions to increase our architecture’s efficiency and to grow our environment with our business. The following graphic represents our SAP landscape on Azure.

The benefits of SAP on Microsoft Azure

SAP on Microsoft Azure provides several benefits to our business, many of which have resulted in significant transformation for our company. Some of the most important benefits include:

• Business agility. With Microsoft Azure’s on-demand SAP–certified infrastructure, we’ve achieved faster development and test processes, shorter SAP release cycles, and the ability to scale instantaneously on demand to meet peak business usage.
• Efficient insights. SAP on Microsoft Azure gives us deeper visibility across our SAP landscape. On Azure, our infrastructure is centralized and consolidated. We no longer have our SAP infrastructure spread across multiple on-premises datacenters.
• Efficient real-time operations and integration. We can leverage integration with other Microsoft Azure technologies such as Internet of Things (IoT) and predictive analytics to enable real-time capture and analysis of our business environment, including areas such as inventory, transaction processing, sales trends, and manufacturing.
• Mission-critical infrastructure. We run our entire SAP landscape—including our most critical infrastructure—on Microsoft Azure. SAP on Azure supports all aspects of our business environment.

Identifying potential for improved monitoring

As we examined our SAP environment on Microsoft Azure, we found several key areas where we could improve our monitoring and reporting experience:

• Monitoring SAP from external business-process components. External business process components had no visibility into SAP. Our monitoring within individual SAP environments provided valuable insight into SAP processes, but we needed a more comprehensive view. SAP is just one component among many in our business processes, and the owners of those business processes didn’t have any way to track their processes after they entered SAP.
• Managing and viewing end-to-end processes. It was difficult to manage and view end-to-end processes. We couldn’t capture the end-to-end process status to effectively monitor individual transactions and their progress within the end-to-end process chain. SAP was disconnected from end-to-end monitoring and created a gap in our knowledge of the entire process pipeline.
• Assessing overall system health. We couldn’t easily assess overall system health. Our preexisting monitoring solution didn’t provide a holistic view of the SAP environment and the processes with which it interacted. The overall health of processes and systems was incomplete because of missing information for SAP, and issues that occurred within the end-to-end pipeline were difficult to identify and problematic to troubleshoot.

Our SAP on Microsoft Azure environment was like a black box to many of our business-process owners, and we knew that we could leverage Azure and SAP capabilities to improve the situation. We decided to create a more holistic monitoring solution for our SAP environment in Azure and the business processes that defined Microsoft operations.

Creating a telemetry solution for SAP on Microsoft Azure

The distributed nature of our business process environment led us to examine a broader solution—one that would provide comprehensive telemetry and monitoring for our SAP landscape and any other business processes that constituted the end-to-end business landscape at Microsoft. The following goals drove our implementation:

Integrate comprehensive telemetry into our monitoring.

• Move toward holistic health monitoring of both applications and infrastructure.
• Create a complete view of end-to-end business processes.
• Create a modern, standards-based structure for our monitoring systems.

Guiding design with business-driven monitoring and personas

We adopted a business-driven approach to building our monitoring solution. This approach examines systems from the end-user perspective, and in this instance, the personas represented three primary business groups: business users, executives, and engineering teams. Using the synthetic method, we planned to build our monitoring results around what these personas wanted and needed to observe within SAP and the end-to-end business process, including:

• Business user needs visibility into the status of their business transactions as they flow through the Microsoft and SAP ecosystem.
• Executives need to ensure that our business processes are flowing smoothly. If there are critical failures, they need to know before customers or partners discover them.
• Engineers need to know about business-process issues before those issues affect business operations and lead to customer-satisfaction issues. They need end-to-end visibility of business transactions through SAP telemetry data in a common consumption format.

Creating end-to-end telemetry with our Unified Telemetry Platform

The MDEE team developed a telemetry platform in Microsoft Azure that we call the Unified Telemetry Platform (UTP). UTP is a modern, scalable, dependable, and cost-effective telemetry platform that’s used in several different business-process monitoring scenarios in Microsoft, including our SAP–related business processes.

UTP is built to enable service maturity and business-process monitoring across MDEE. It provides a common telemetry taxonomy and integration with core Microsoft data-monitoring services. UTP enables compliance with and maintenance of business standards for data integrity and privacy. While UTP is the implementation we chose, there are numerous ways to enable telemetry on Microsoft Azure. For additional considerations, access Best practices for monitoring cloud applications on the Azure documentation site.

Capturing telemetry with Microsoft Azure Monitor

To enable business-driven monitoring and a user-centric approach, UTP captures as many of the critical events within the end-to-end process landscape as possible. Embracing comprehensive telemetry in our systems meant capturing data from all available endpoints to build an understanding of how each process flowed and which SAP components were involved. Azure Monitor and its related Azure services serve as the core for our solution.

Microsoft Azure Application Insights

Application Insights provides a Microsoft Azure–based solution with which we can dig deep into our Azure–hosted SAP landscape and extract all necessary telemetry data. By using Application insights, we can automatically generate alerts and support tickets when our telemetry indicates a potential error situation.

Microsoft Azure Log Analytics

Infrastructure telemetry such as CPU usage, disk throughput, and other performance-related data is collected from Azure infrastructure components in the SAP environment by using Log Analytics.

Microsoft Azure Data Explorer

UTP uses Microsoft Azure Data Explorer as the central repository for all telemetry data sent through Application Insights and Microsoft Azure Monitor Logs from our application and infrastructure environment. Azure Data Explorer provides enterprise big-data interactive analytics; we use the Kusto query language to connect the end-to-end transaction flow for our business processes, for both SAP process and non–SAP processes.

Microsoft Azure Data Lake

UTP uses Microsoft Azure Data Lake for long-term cold-data storage. This data is taken out of the hot and warm streams and kept for reporting and archival purposes in Azure Data Lake to reduce the cost associated with storing large amounts of data in Microsoft Azure Monitor.

Constructing with definition using common keys and a unified platform

UTP uses Application Insights, Microsoft Azure Data Explorer, and Microsoft Azure Data Lake as the foundation for our telemetry data. This structure unifies our data by using a common schema and key structure that ties telemetry data from various sources together to create a complete view of business-process flow. This telemetry hub provides a central point where telemetry is collected from all points in the business-process flow—including SAP and external processes—and then ingested into UTP. The telemetry is then manipulated to create comprehensive business-process workflow views and reporting structures for our personas.

Common schema

UTP created a clearly defined common schema for business-process events and metrics based on a Microsoft-wide standard. That schema contains the metadata necessary for mapping telemetry to services and into processes, and it allows for joins and correlation across all telemetry.

Common key

As part of the common schema for business process events, the design includes a cross-correlation vector (XCV) value, common to all stored telemetry and transactions. By persisting a single value for the XCV and populating this attribute for all transactions and telemetry events related to a business process, we can connect the entire process chain related to an individual business transaction as it flows through our extended ecosystem.

Multilayer telemetry concept for SAP

For SAP on Microsoft Azure, our MDEE team focused on four specific areas for telemetry and monitoring:

1. SAP Business Process layer
2. SAP Application Foundation layer
3. Infrastructure layer
4. Surrounding API layer

The result was holistic telemetry and monitoring across these layers, a structure that leverages Microsoft Power BI as the engine behind our reporting and dashboarding functionality.

Our MDEE team created reporting around business-driven monitoring and constructed standard views and dashboards that offer visibility into important areas for each of the key business personas. Dashboards are constructed from Kusto queries, which are automatically translated in the Microsoft Power BI M formula language. For each persona, we’ve enabled a different viewpoint and altitude of our business process that allows the persona to view the SAP monitoring information that’s most critical to them.

Microsoft Azure Monitor for SAP Solutions

Microsoft previously announced the launch of Microsoft Azure Monitor for SAP Solutions (AMS) in public preview—an Azure-native monitoring solution for customers who run SAP workloads on Azure. With AMS, customers can view telemetry of their SAP landscapes within the Azure portal and efficiently correlate telemetry between various layers of SAP. AMS is available through Microsoft Azure Marketplace in the following regions: East US, East US 2, West US 2, West Europe, and North Europe. AMS doesn’t require a license fee.

Our MDEE team worked in close collaboration with Microsoft Azure product teams to build and release SAP NetWeaver provider in Microsoft Azure Monitor for SAP solutions.

• The SAP NetWeaver provider in Microsoft Azure Monitor for SAP Solutions enables SAP on Microsoft Azure customers to monitor SAP NetWeaver components and processes on Azure in the Azure portal. The SAP NetWeaver provider includes default visualizations and alerts that can be used out of the box or customized to meet customer requirements.
• SAP NetWeaver telemetry is collected by configuring the SAP NetWeaver provider within AMS. As part of configuring the provider, customers are required to provide the host name (Central, Primary, and/or Secondary Application server) of SAP system and its corresponding Instance number, Subdomain, and System ID (SID).

For more information, go to AMS quick start video and SAP NetWeaver monitoring-Azure Monitoring for SAP Solutions.

Our telemetry platform provides benefits across our SAP and business-process landscape. We have created a solution that facilitates end-to-end SAP business-process monitoring, which in turn enables our key personas to do their jobs better.

Persona benefits

Benefits for each persona include the following:

• Business users no longer need to create service tickets to get the status of SAP transaction flows. They can examine our business processes from end to end, including SAP transactions and external processes.
• Executives can trust that their business processes execute seamlessly and that any errors are proactively addressed with no impact to customers or partners.
• Engineers no longer need to check multiple SAP transactions to investigate business-process issues and identify in which step the business process failed. They can improve their time-to-detect and time-to-resolve numbers with the correct telemetry data and avoid business disruption for our customers.

 Organization-wide benefits

The benefits of our platform extend across Microsoft by providing:

• End-to-end visibility into business processes. Our Unified Telemetry Platform (UTP) provides visibility into business processes across the organization, which then facilitates better communication and a clearer understanding of all parts of our business. We have a more holistic view of how we’re operating, which helps us work together to achieve our business goals.
• Decreased time to resolve issues. Our visibility into business processes informs users at all levels when an issue occurs. Business users can examine the interruption in their workflow, executives are notified of business-process delays, and engineers can identify and resolve issues. This activity all occurs before our customers are affected.
• More efficient business processes. Greater visibility leads to greater efficiency. We can demonstrate issues to stakeholders quickly, everyone involved can recognize areas for potential improvement, and we can monitor modified processes to ensure that improvement is happening.

We learned several important lessons with our UTP implementation for SAP on Microsoft Azure. These lessons helped inform our progress of UTP development, and they’ve given us best practices to leverage in future projects, including:

• Perform a proper inventory of internal processes. You must be aware of events within a process before you can capture them. Performing a complete and informed inventory of your business processes is critical to capturing the data required for end-to-end business-process monitoring.
• Build for true end-to-end telemetry. Capture all events from all processes and gather telemetry appropriately. Data points from all parts of the business process—including external components—are critical to achieving true end-to-end telemetry.
• Build for Microsoft Azure-native SAP.  SAP is simpler to manage on Azure and instrumenting SAP processes becomes more efficient and effective when SAP components are built for Azure.
• Encourage data-usage models and standards across the organization. Data standards are critical for an accurate end-to-end view. If data is stored in different formats or instrumentation in various parts of the business process, the end reporting results won’t accurately represent the business-process’ state.

We’re continuing to evaluate and improve as we discover new and more efficient ways to track our business processes in SAP. Some of our current focus areas include:

• Machine learning for predictive analytics. We’re using machine learning and predictive analytics to create deeper insights and more completely understand our current SAP environment. Machine learning also helps us anticipate growth and change in the future. We’re leveraging anomaly detection in Microsoft Azure Cognitive Services to track SAP business service-health outliers.
• Actionable alerting. We’re using Microsoft Azure Monitor alerts to create service tickets, generate service-level agreement (SLA) alerts, and provide a robust notification and alerts system. We’re working toward linking detailed telemetry context into our alerting system to create intelligent alerting that enables us to more accurately and quickly identify potential issues within the SAP environment.
• Telemetry-based automation. We’re using telemetry to enable automation and remediation within our environment. We’re creating self-healing scenarios to automatically correct common or easy-to-correct issues to create a more intelligent and efficient platform.

We’re continually refining and improving business-process monitoring of SAP on Microsoft Azure. This initiative has enabled us to keep key business users informed of business-process flow, provided a complete view of business-process health to our leadership, and helped our engineering teams create a more robust and efficient SAP environment. Telemetry and business-driven monitoring have transformed the visibility that we have into our SAP on Azure environment, and our continuing journey toward deeper business insight and intelligence is making our entire business better.

• Unpack how we’re optimizing SAP for Microsoft Azure.
• Discover how we’re protecting Microsoft’s SAP workload with Microsoft Sentinel.
• Explore how we’re unlocking Microsoft’s SAP telemetry with Microsoft Azure.

The post Monitoring Microsoft’s SAP Workload with Microsoft Azure appeared first on Inside Track Blog.

Microsoft Intune makes it convenient to bring your own device to work. Watch to learn how simple it is to enroll your employees’ personal mobile devices in Intune, giving them secure access to corporate resources and applications. Our Microsoft Digital Employee Experience team uses Intune to help ensure that personal devices, such as iOS devices, adhere to corporate security policies without accessing personal files.

For a transcript, please view the video on YouTube: https://www.youtube.com/watch?v=eyk19T1OXy8, select the “More actions” button (three dots icon) below the video, and then select “Show transcript.”

• Explore verifying device health at Microsoft with Zero Trust.
• Unpack managing Windows 10 devices with Microsoft Intune.
• Discover evolving the device experience at Microsoft.

The post Microsoft Intune makes it easy to bring your own device to work appeared first on Inside Track Blog.

Sometimes these failures happen sporadically within the application framework and often go undetected. Even if a user detects an anomaly, they need to decide how to react, which is time consuming.

To do this, we’re using Microsoft Azure Anomaly Detector to examine transactions across our SAP environment, which helps us identify issues before they become problems. In turn this enables us to proactively improve the performance, consistency, and reliability of our entire SAP landscape.

[Unpack how we’re optimizing SAP for Microsoft Azure. | Discover how we’re protecting Microsoft’s SAP workload with Microsoft Sentinel. | Explore how we’re upgrading Microsoft’s core Human Resources system with SAP SuccessFactors.]

Understanding the need for anomaly detection in SAP

At Microsoft, our SAP environment comprises many complex processes across multiple lines of business. To avoid having disparate environments and isolated monitoring and reporting data, we wanted to build a single codebase solution for monitoring and anomaly detection that each line-of-business can use with minimal code implementation.

We wanted to build intelligence to detect anomalies and inconsistencies in business process flow to improve platform health. Improved platform health improves engineering service-level agreements (SLAs) and reduces revenue loss by being proactive rather than reactive.

There were hundreds of areas that could benefit from anomaly detection in our SAP portfolio, but we wanted to identify a single area for our pilot project. In the Master Data Management (MDM) space, we create thousands of objects representing business entities such as customers and business partners.

Most of these objects are created by using an application programming interface (API), and no human interaction is needed. However, it’s extremely difficult to identify if issues related to MDM are occurring in upstream systems, so we needed a way to capture issues in advance, proactively and quickly.

In the MDM space, we have SAP Master Data Governance (MDG) background processes, such Customer Master data creation, which run without any user interaction. Across various batch and scheduled jobs, process runtime varies based on data volume, time of day, time of year, and resource availability.

Understanding the potential for issues in each process and the larger process environment involves several challenging questions, including:

• Is the transaction supposed to run that long?
• Is there a problem in an upstream system?
• Are there resources that reached their maximum capacity or that are creating a performance bottleneck?

Assessing Microsoft Azure Cognitive Services and Microsoft Azure Anomaly Detector

Detecting these issues by using human triage was difficult and time and resource intensive. Many issues went undetected, resulting in poor customer experience and the loss of potential revenue, in addition to lost capacity that could have been used for more productive purposes.

To solve this problem, we required a solution that was reliable, scalable, and easy to integrate with our SAP systems. The solution that we wanted would be process agnostic, implemented as a single codebase, and require no human intervention to detect issues.

The Microsoft Azure Anomaly Detector service, available within Microsoft Azure Cognitive Services, fits all our requirements.

The Anomaly Detector API enabled us to monitor and detect abnormalities in our data without having to know machine learning. The Anomaly Detector API’s algorithms adapt by automatically identifying and applying the best-fitting models to data, regardless of industry, scenario, or data volume, which greatly reduced our development efforts. Our primary steps were quite simple:

1. Provision a service instance for Anomaly Detector in Microsoft Azure Cognitive Services.
2. Start using the REST APIs in application code and interactions.

Using time-series data and data anomalies

For Anomaly Detector to identify anomalies, it requires time-series data, which is a series of data points indexed in time-based order.

For example, your car might have embedded sensors that send information regarding engine health, speed, tire pressure, and gasoline capacity. This information about your car is constantly updated over time and, as such, it can be used as time‑series data.

Most data received throughout time can be manipulated to be time‑series data if it’s a consistent data sequence with a time stamp. Time-series data with a single variable is considered a univariate series, while time-series data with more than one variable is considered a multivariate time series. Anomaly detector supports both univariate and multivariate series.

A data anomaly is outlying data that doesn’t fit within expected boundaries. The graphic below depicts the visual pattern of the time-series data with highlighted anomaly points in the time-series data. The graphic contains each of the time‑series data on the plot.

Data should be within minimum and maximum boundaries. In the figure, the boundary is filled with a light color. Most of the data points are within the expected boundaries. However, some data points that exceed the expected boundaries are highlighted in red in the figure, are data anomalies.

For example, a stock price that drops below the expected limit is a data anomaly. If the temperature reading of a power plant core exceeds the acceptable limit, the reading is a data anomaly, and the technicians at the power plant should be immediately notified so that they can act based on the anomaly.

Not all data anomalies are negative.

For example, if you have an article on your website that’s trending and experiencing larger traffic volume than normal, you likely want to be notified about the anomaly.

Or, if you have an e‑commerce website and receive a sudden spike in product demand, you, as the product supplier, should be notified so that you can act immediately. The graphic below contains examples of inputs and results for the Anomaly Detector service.

Using Microsoft Azure services to create a business solution

To enable integration with our SAP portfolio, we’ve implemented several decoupled software components. Each component has a specific use case, and we decouple business logic and the presentation layers to the extent possible. All application code is committed to a Microsoft Azure DevOps repository and is built as a Microsoft Azure-native solution.

• Microsoft Azure Web Apps. We host the front-end (presentation layer) application in an Azure Web App, from which the user can call the anomaly-detection service by using the prepared time-series data. Microsoft Azure Web App Service gives our developers the option to work in their preferred language, which can be .NET, .NET Core, Java, Ruby, Node.js, PHP, or Python. We protect the application endpoint with Microsoft Azure Active Directory for user authentication and authorization.
• Microsoft Azure Function Apps. We host all business-logic functionality in Azure Function Apps. We use two Azure Function Apps. The first is used to connect to Microsoft Azure Application Insights and capture SAP telemetry, such as customer or business-partner processes that need anomaly detection.
  The Function App transforms the data into JavaScript Object Notation (JSON) format with time-series subformatting. The second Function App captures the precompiled time-series data from the first Function App, makes a call to the Anomaly Detector service, and then retrieves the result. The Web App presentation layer displays the results in a graph format. Function App endpoints are protected with access tokens.
• Application Insights. We store all SAP log data in Application Insights. This log data is posted from various business processes, including Customer Master Data creation, Business Partner Creation and updates, and batch program logs. These logs are the source for all anomaly detection.
• Microsoft Azure Anomaly Detector. Anomaly Detector uses the Anomaly Detector API to detect and return all anomaly points based on time-series data that the Function Apps send. While there are two options for interacting with Anomaly Detector, our developers chose to call the HTTP REST API directly for the Anomaly Detector rather than use the client SDK to integrate Anomaly Detector directly with their application. Using the API removes the limitation of using a single codebase and enables simple integration with any modern language that supports calling REST APIs through HTTP.

Implementation architecture

As depicted in the graphic below, various SAP applications post their business-process logs into the Application Insights instance. The Web App hosts the core application, including the presentation layer and user interaction. The two Function Apps perform extract and process data from the Application Insights service and control interaction with the Anomaly Detector service. The Function Apps send the final results from the Anomaly Detector service for display and consumption in the Web App.

Business implementation and benefits

One of our key business processes that we onboarded to the Anomaly Detector–based solution was the Master Data Management (MDM) business-partner creation that uses SAP Master Data Governance (MDG).

We constantly create and update business-partner data in our SAP system via API calls from various upstream tenants and front-end systems. Based on incoming telemetry sources, the Anomaly Detector solution detects if there is a sudden drop in creation or update processes because of API failure or network issues.

The detection algorithm can detect these issues automatically, in real time, which helps our system users to take corrective action. This simple addition to the issue-detection process helps us supply a better customer experience and eliminates major negative effects on revenue.

We’re planning to implement the same solution design across many other business processes, such as batch-job monitoring.

Currently, we have several hundred batch jobs that range from a runtime of a few seconds to several hours. It’s extremely difficult to monitor them manually and individually.

Sometimes, due to system issues or transaction locking, these jobs take more time, further affecting downline processes. Anomaly detection will play a critical role in detecting those issues, creating automatic alerts, and reducing manual monitoring.

This application has many potential use cases across multiple business scenarios. We’re planning to explore several of these use cases, including:

• SAP batch job monitoring, evaluating long running jobs and triggering alerts.
• Business-document processing and creation, such as sales orders, purchase orders, financial postings, and work orders.
• Any set of data that has time-series patterns. Data sets such as these can be evaluated and monitored for anomaly detection on a case-by-case basis.

Using Microsoft Azure Anomaly Detector has enabled us to quickly and efficiently build a solution to detect abnormalities in our SAP processes without having to know machine learning. The Anomaly Detector API’s algorithms help us to identify issues before they become problems, thereby proactively improving the performance, consistency, and reliability of our entire SAP landscape.

• Unpack how we’re optimizing SAP for Microsoft Azure.
• Discover how we’re protecting Microsoft’s SAP workload with Microsoft Sentinel.
• Explore how we’re upgrading Microsoft’s core Human Resources system with SAP SuccessFactors.
• Learn more about our best practices for using Anomaly Detector API.

The post Examining Microsoft’s SAP transactions with Microsoft Azure Anomaly Detector appeared first on Inside Track Blog.

If you’re in IT, you know device management is a challenge all companies face. It’s especially difficult to make sure devices are up to date and in compliance within an enterprise environment of thousands or even hundreds of thousands of devices. Another complication is that employees use a mix of personal and corporate-owned devices, which might run several possible operating systems, each with unique needs.

At Microsoft, we’ve experienced and met these challenges. Daniel Manalo shares how with Gabe Storment, our senior business program manager, in this Inside Track Spotlight interview.

Manalo, a principal service engineer in Microsoft Digital Employee Experience (MDEE), Microsoft’s IT organization, explains that we overcame many hurdles to reach a modern management state with our devices.

“Some of our build mechanisms have hard dependencies on on-premises or Active Directory—a traditional environment,” Manalo says.

To handle these needs, we in MDEE worked with the Microsoft Intune product group to implement co-management. With co-management, a device can be simultaneously managed by both the traditional managed environment of Configuration Manager and the modern environment of cloud-based Microsoft Intune.

Zero Trust principles are another aspect of device management at Microsoft.

“At Microsoft, we ensure that a set of zero trust policies are enforced using conditional access policies,” Manalo says. “An example of some of these device health checks are minimum operating system, anti-malware installed, the device is malware-free, application control, and other conditional access checks.”

Watch our interview to learn more about device health checks in the video as Manalo describes how compliance is verified over time, what happens to non-compliant devices, and how users are kept informed.

Try Microsoft Intune at no cost.

• Learn more about co-management and how you can use it at your company.
• Unpack evolving the device experience at Microsoft.
• Discover making device management frictionless at Microsoft.
• Explore verifying device health at Microsoft with Zero Trust.
• Check out verifying identity in a Zero Trust model internally at Microsoft.

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The post How modernization is helping Microsoft along its internal device management journey appeared first on Inside Track Blog.