From Reactive to Proactive: Fortune 100 Firm Eliminates Reporting Bottlenecks with Always-on Decision Intelligence

tellius decision intelligence

Fast facts

Who: IT ops team of multinational, Fortune 100 consumer goods brand

Number of active Tellius users: 200+ IT application and systems managers

Use case: AI powers always-on application monitoring to continuously improve IT operations

Before and after:

Disparate BI tools and dashboards ➡️ Converged platforms, reducing technology costs $1M-$2m/year

Application risk assessments completed in 10 days ➡️ Completed in hours

Limited visibility ➡️ Continuous risk assessment for 100% of applications

Inefficient IT operations ➡️ $6M-$10M FTE savings across roles

Costly, unplanned application downtime ➡️ Proactive maintenance based on predictive insights

The opportunity

An IT operations team of a multibillion-dollar consumer goods company supports 8,000 software applications—things like corporate payroll, HR, pack/move/ship technologies, Outlook, you name it—for more than 100,000 global employees. Application managers and change management teams monitor change request tickets to identify risks of potential application failures and future incidents.

Monitoring IT applications is critical to the organization because downtime and incidents of apps can expose the company to write-offs, remediations, and business delays. To ensure its applications operate to spec and are in compliance with internal and external requirements, the team saw an opportunity to develop an always-on IT ops monitoring system, lowering risk and effort while gaining audit confidence and efficiency. 

Being able to proactively identify risk before incidents occur enables the teams to save time, avoid costs, and, ultimately, bring more products to market.

The challenge

To complete annual risk assessments for apps, the IT ops team had to collate data across disparate data sources (e.g., static spreadsheets and PowerBI dashboards), bringing it all into a singular view, and aggregating it in a way in which they could digest the information and report on it. This process was highly manual and time-consuming, resulting in reduced productivity and limited business outcomes. 

What’s more, because this process happened only once a year, the team lacked a continuous view into the status of applications, leaving critical issues unaccounted for until it came time to conduct the annual assessments. 

Wasted time: The process of manually gathering an abundance of data across separate sources—and then aggregating it in a meaningful way—often took application managers 10 business days to complete.

Limited visibility: Even once all the data was collected, managers still lacked a clear understanding of insights, largely due to breadcrumb trails of dashboards, as well as restricted and inconsistent access across users. Without being able to adequately analyze the data in order to assess the future risk of failure, their decision-making and recommendations for change were delayed.

Increased risk: Without a clear and timely understanding of risk, teams were not able to efficiently govern their change management responsibilities, including reviewing and actioning tickets. Unable to be proactive in avoiding future incidents, they were forced to be reactive in managing change ticket failures.

Higher costs: With increased risk comes increased costs related to fines, write-offs, remediations of failed change requests, and operational expenditures. The financial cost of not keeping applications running was significant—not only was there business impact if applications weren’t delivered on time to the target stakeholders, but business units were subject to fines for non-compliance from regulatory bodies, investments were susceptible to financial write-offs, and the cost to remedy urgent application failures was high.

decision intelligence

Why Tellius

The consumer goods giant went with Tellius’ decision intelligence platform for several reasons:

Proactive, always-on monitoring: Tellius’ continuous risk assessment capabilities enable the company to always have a clear view into descriptive analytics (what happened?) and diagnostic analytics (why did it happen?), meaning they can identify, control, and address critical incident compliance prior to ticket closure.

Predictive insights into potential risk: The organization can easily create a predictive model that identifies increased risk of application failure that’s based on incidents as they’re reported. Models are created and updated without writing code, and application managers can continuously monitor and visualize and be alerted to risk of failure. Because no coding is required—just a conceptual understanding of machine learning—the non-technical users at the business can leverage this automated discovery of insights to surface important findings hidden in their data.

Unified views into past, current, and future challenges: Tellius’ Vizpads are integrated within the platform’s AI, insights, and search capabilities, enabling the IT ops team to conduct ad hoc analysis on past tickets and quickly identify the top drivers leading to application failures, all within one unified platform—i.e., no more data across several sources.

Easy access to identify answers: Anyone—not just the data experts—can use Tellius’ Google-like search interface (natural language processing) to ask questions of their data and find answers. To achieve deep and efficient analyses of their data, they can simply type or speak their query into Tellius. Typing something as simple as “show me tickets by risk type monthly since 2022” rapidly allows users to uncover trends in the types of risks that are occurring across the application ecosystem.

Bringing data together: Through various data connectors, Tellius extracts data across multiple sources and brings it into one platform. For example, the customer uses Tellius’ Data module to bring in data from ServiceNow and This data feeds into Tellius’ Predict capabilities as training data, and the predictions are ultimately served to end consumers in Vizpads, unifying insights across previously disparate datasets.


The results

Increased efficiency: It now takes the IT ops team mere hours to complete an application risk assessment, versus five days previously, translating to $6 million to $10 million FTE savings across multiple roles. This can then be invested into other areas of the business. 

Lower risk: With predictive insights and always-on monitoring through Tellius, the customer has gained newfound visibility into current and recent change requests, the ability to follow up on older changes that had not been closed, and early warnings of upcoming changes that will require attention. Instead of reacting to incidents when they occur, the teams can proactively assess and remediate risk through automated alerts.

Higher audit confidence: With Tellius, the customer is confident that 100% of open and closed tickets are being reviewed and actioned—no more missing change requests.

Cost avoidance: Fewer fines, write-offs, and remediations of failed change requests means significant avoided costs and less business downtime. Converging thousands of apps into one platform also saves the customer an estimated $1 million to $2 million on reduced technology costs.


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