10 Points for Succeeding in Advanced IT Analytics (AIA) Adoption

Dennis Drogseth

In two prior blogs, I described why I believe the time is right for AIA investment, and then provided some insights from real-world deployments on AIA benefits. But no technology that touches more than one IT stakeholder, no matter how good and how transformative, can deliver its potential without attention to leadership, process considerations and dialog. In this blog, I'd like to share effective strategies for AIA adoption based on EMA's research: "Advanced IT Analytics Adoptions: A Look at Real-World Deployments in the Real World," April, 2016, as well as the ongoing research we've done for "Leaders in Advanced IT Analytics: A Buyer's Guide for Investing in Innovation" — which has been supplemented by more than 20 deployment interviews.

1. Executive leadership, or at least executive commitment, is key

Why is this? AIA is, by its very nature, transformative. It helps to empower new ways of working across Operations, and in many cases, across most or all of IT — including IT service management teams (ITSM), development teams, and in some cases even security teams. It may also help to better align IT with business stakeholders who can also profit from AIA insights when business outcomes are integrated into service performance-related behaviors.

But getting everyone to work together in a new, more proactive way, will usually require new processes, and sometimes even organizational changes to optimize new ways of sharing data and the proactive insights that AIA can offer. Sharing data in a new way, just in itself, can arise suspicions and anxieties in IT stakeholders long accustomed to "owning" what they do in a narrow, siloed fashion. Their expertise remains needed. But their inflexibility in working with others will have to become a thing of the past if AIA investments are to reap their full benefits.

2. Support for an expanding number of stakeholders across domains should be the goal

While AIA adoptions should start with a well-focused team targeting critically relevant objectives, AIA adoptions should also be viewed as expansive. The data they collect and analyze can typically serve multiple use cases, and should categorically support multiple domains across Operations, IT service management teams, and executive IT and possibly Development, Security and business stakeholders as well.

3. Look for broad capabilities for accessing multiple data sources across the full application/infrastructure stack

This is a big part of the technology foundation for breadth of stakeholder support as just described. In assessing what AIA solutions to invest in, you should seek out capabilities that can bring many multiple data sources together. Moreover, many AIA solutions are architected for expansiveness in reaching out to everything from IoT to sentiment analysis as they evolve.

4. Don't forget the value of capturing interdependencies in dynamic topologies or service modeling

In our research on AIA adoptions, those who were "extremely successful" were far more likely to seek out a handshake between analytics and insights into application/infrastructure interdependencies. This was often done through service modeling accomplished either directly through their AIA tool, or by leveraging existing investments, such as application discovery and dependency mapping (ADDM) or configuration management systems (CMS). Our buyer's guide research also showed some surprising advances in enabling interdependency insights among the emerging crop of AIA innovators.

5. Look for toolset consolidation

As described in last-week's column, toolset consolidation is one of the biggest benefits of AIA adoption. This can often be achieved by integrating third-party monitoring and other tools into the AIA fabric, so that redundancies quickly become apparent. Or, it can be achieved as the AIA investment itself, through its eclectic data collection and advanced intelligence, directly replaces existing siloed tools with more limited capabilities.

6. Seek out a rich set of advanced AIA heuristics in your investment

In our research, those who were "extremely successful" in AIA had a much richer set of heuristics to work with — which paralleled what we saw in use among the vendors we assessed for our buyer's guide. These may range from advanced levels of event correlation, to more pervasive machine learning, to if/then analytics to support more effective change, to prescriptive analytics. In all cases, AIA helps IT to get beyond monitoring based on pre-defined rules in order to proactively discover what's relevant.

7. Don't rule out AIA in support of cloud, DevOps or SecOps needs

Each of these points could be its own blog, but AIA is already showing dramatic values in managing and assimilating public and private cloud resources more effectively, as well as in supporting DevOps teams and helping security and operations teams work more effectively together.

8. Don't forget to leverage ITSM integrations with your AIA advances

ITSM integration was another clear differentiator between the "extremely successful" and "marginally successful" in our research. It was also perhaps the single most prevalent across the many vendors we assessed for our buyer's guide. The reasons why ITSM is important here are manifold — but at the top of the list is superior workflow and process hardening to support better efficiencies across IT.

9. Metrics count

In our research, "extremely successful" AIA adoptions had more metrics in all categories — from performance metrics, to change-related metrics, to business impact metrics. Metrics become critical in sharing, communicating and prioritizing the insights derived from AIA, as well as in creating policies for taking action, or in measuring effectiveness, value, and potentially even cost.

10. Don't try to do it all on your own — the time is right for investing in third-party solutions

In our research, those who were "extremely successful" showed a clear preference for investing in third-party AIA capabilities versus those who were only "marginally successful." And the buyer's guide has reinforced this conclusion considerably. The challenge, much like CMDB deployments in the past, isn't just about getting the data in one place and figuring out how to use it. It's about getting dynamically current, relevant, actionable data in one place with ongoing, proactive analytic insights and visualization — something the vendors in our buyer's guide can deliver with unique levels of value and maturity.

For more insight on AIA adoption and the AIA landscape of options, don't forget to listen in on my on-demand webinar.

Read the fourth blog in the series about AIA: If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 1: Cost Advantage

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10 Points for Succeeding in Advanced IT Analytics (AIA) Adoption

Dennis Drogseth

In two prior blogs, I described why I believe the time is right for AIA investment, and then provided some insights from real-world deployments on AIA benefits. But no technology that touches more than one IT stakeholder, no matter how good and how transformative, can deliver its potential without attention to leadership, process considerations and dialog. In this blog, I'd like to share effective strategies for AIA adoption based on EMA's research: "Advanced IT Analytics Adoptions: A Look at Real-World Deployments in the Real World," April, 2016, as well as the ongoing research we've done for "Leaders in Advanced IT Analytics: A Buyer's Guide for Investing in Innovation" — which has been supplemented by more than 20 deployment interviews.

1. Executive leadership, or at least executive commitment, is key

Why is this? AIA is, by its very nature, transformative. It helps to empower new ways of working across Operations, and in many cases, across most or all of IT — including IT service management teams (ITSM), development teams, and in some cases even security teams. It may also help to better align IT with business stakeholders who can also profit from AIA insights when business outcomes are integrated into service performance-related behaviors.

But getting everyone to work together in a new, more proactive way, will usually require new processes, and sometimes even organizational changes to optimize new ways of sharing data and the proactive insights that AIA can offer. Sharing data in a new way, just in itself, can arise suspicions and anxieties in IT stakeholders long accustomed to "owning" what they do in a narrow, siloed fashion. Their expertise remains needed. But their inflexibility in working with others will have to become a thing of the past if AIA investments are to reap their full benefits.

2. Support for an expanding number of stakeholders across domains should be the goal

While AIA adoptions should start with a well-focused team targeting critically relevant objectives, AIA adoptions should also be viewed as expansive. The data they collect and analyze can typically serve multiple use cases, and should categorically support multiple domains across Operations, IT service management teams, and executive IT and possibly Development, Security and business stakeholders as well.

3. Look for broad capabilities for accessing multiple data sources across the full application/infrastructure stack

This is a big part of the technology foundation for breadth of stakeholder support as just described. In assessing what AIA solutions to invest in, you should seek out capabilities that can bring many multiple data sources together. Moreover, many AIA solutions are architected for expansiveness in reaching out to everything from IoT to sentiment analysis as they evolve.

4. Don't forget the value of capturing interdependencies in dynamic topologies or service modeling

In our research on AIA adoptions, those who were "extremely successful" were far more likely to seek out a handshake between analytics and insights into application/infrastructure interdependencies. This was often done through service modeling accomplished either directly through their AIA tool, or by leveraging existing investments, such as application discovery and dependency mapping (ADDM) or configuration management systems (CMS). Our buyer's guide research also showed some surprising advances in enabling interdependency insights among the emerging crop of AIA innovators.

5. Look for toolset consolidation

As described in last-week's column, toolset consolidation is one of the biggest benefits of AIA adoption. This can often be achieved by integrating third-party monitoring and other tools into the AIA fabric, so that redundancies quickly become apparent. Or, it can be achieved as the AIA investment itself, through its eclectic data collection and advanced intelligence, directly replaces existing siloed tools with more limited capabilities.

6. Seek out a rich set of advanced AIA heuristics in your investment

In our research, those who were "extremely successful" in AIA had a much richer set of heuristics to work with — which paralleled what we saw in use among the vendors we assessed for our buyer's guide. These may range from advanced levels of event correlation, to more pervasive machine learning, to if/then analytics to support more effective change, to prescriptive analytics. In all cases, AIA helps IT to get beyond monitoring based on pre-defined rules in order to proactively discover what's relevant.

7. Don't rule out AIA in support of cloud, DevOps or SecOps needs

Each of these points could be its own blog, but AIA is already showing dramatic values in managing and assimilating public and private cloud resources more effectively, as well as in supporting DevOps teams and helping security and operations teams work more effectively together.

8. Don't forget to leverage ITSM integrations with your AIA advances

ITSM integration was another clear differentiator between the "extremely successful" and "marginally successful" in our research. It was also perhaps the single most prevalent across the many vendors we assessed for our buyer's guide. The reasons why ITSM is important here are manifold — but at the top of the list is superior workflow and process hardening to support better efficiencies across IT.

9. Metrics count

In our research, "extremely successful" AIA adoptions had more metrics in all categories — from performance metrics, to change-related metrics, to business impact metrics. Metrics become critical in sharing, communicating and prioritizing the insights derived from AIA, as well as in creating policies for taking action, or in measuring effectiveness, value, and potentially even cost.

10. Don't try to do it all on your own — the time is right for investing in third-party solutions

In our research, those who were "extremely successful" showed a clear preference for investing in third-party AIA capabilities versus those who were only "marginally successful." And the buyer's guide has reinforced this conclusion considerably. The challenge, much like CMDB deployments in the past, isn't just about getting the data in one place and figuring out how to use it. It's about getting dynamically current, relevant, actionable data in one place with ongoing, proactive analytic insights and visualization — something the vendors in our buyer's guide can deliver with unique levels of value and maturity.

For more insight on AIA adoption and the AIA landscape of options, don't forget to listen in on my on-demand webinar.

Read the fourth blog in the series about AIA: If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 1: Cost Advantage

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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