The data-driven Operating Model – Driving innovation and value in the public sector

Effective Service Management relies on an understanding of the potential impact of change, and lack of visibility can lead to delays, performance issues and increased failure rates. In this article, I discuss the changing world of Operations, and look at a future, data-driven approach to service management.

Operational departments across government face a unique challenge. They manage sprawling estates comprising bleeding edge digital services and aging legacy platforms, whilst subject to reducing budgets, coupled with the threat that a significant outage or security breach will make front page headlines in the tabloids.

This issue is becoming increasingly problematic, and not just because of the tabloids. Departments typically have layers of legacy systems, of which the latest digital services, developed using agile and DevOps, sit atop. And, of course, legacy platforms are rarely, if ever, retired.

The challenge is that these complex systems are subject to constant revision. A former civil servant from a major government department I spoke with recently suggested that the volume of change within his portfolio had increased by more than five-fold in the last few years. This is not uncommon.

The changing world of Service Management

Widespread investment in ServiceNow and the implementation of comprehensive CMDBs has improved the situation; supporting troubleshooting, problem resolution, strategy planning and proactive change initiatives. However, building a CMDB and the related service maps is a resource intensive task, and ensuring it remains current, and therefore useful, is extremely challenging.

This is true in a traditional, physical world where devices, assets and configurations are largely static; in a cloud-first, dynamic world, the challenge becomes nigh on impossible. And significantly more is needed to understand the impact or potential impact of any change. 

The result is that costs have increased, change and release cycles have stymied innovation, and in some areas, quality has dipped, with significantly higher volumes of change failure - as high as 10% has been reported in some areas. 

Rather than reinventing the approach to meet the new challenge. Teams are working harder, not thinking smarter.

Data-driven Operations

The solution lies in the vast swathes of data you already have.

In addition to your CMDB, you have a plethora of data available across your estate, including code repositories, incident reports, performance logs, application logs, and cloud metrics. By using big data techniques, these disparate but federated data sources can be aggregated, harvested, and analysed real-time, using machine learning to identify correlations between events, identify anomalies, and learn and apply tolerance thresholds.  

It’s the smart thinking that bridges the gap between where you are now, and where you need to be.

In this way your understanding of your estate can remain up to date, real-time. Using machine learning you can gain a deep understanding of what’s really happening, correlating events, providing deeper insight into performance, enabling failure to be predicted before critical impacts occur, and incident resolution to be accelerated (and potentially automated).

Combining this understanding with strong orchestration techniques, you can give greater autonomy to your service (or product) teams to facilitate change. Moving away from Change Boards, authority can be delegated to product owners to make decisions within guiderails, codified into automated workflows and informed by AI Ops (for want of a better term). 

If you imagine a world in which change is orchestrated; automatically, integrating engineering, DevOps, and service management; and risk-based policy decisions are made by AI with a deep real-time understanding of your estate; then imagine no more. 

The future. Now.

Although the concepts described here are relatively novel, they are proven in a number of pioneering organisations; and there are a range of tools available to support the approach (including ServiceNow, which is heavily used in the Public Sector).

If you're interested in knowing more, please Connect with me or drop me a line through InMail.