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Operate to Transform: Driving Value Through a Proactive Service Model
Operate to Transform: Driving Value Through a Proactive Service Model

December 28, 2021

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Production business applications are on the edge. Support vendors have long focused on only keeping the lights on and fixing issues. This needs to change – over 25% of support costs can be saved every year by transforming the support process. Today we will share 3 best practices we have identified from our work reducing support costs for many of the world’s leading brands. 

Traditional reactive approach of support teams

Business applications in production are the lifeblood of organizations. They provide the right insights to the right stakeholders and enable the right decisions. Corporations invest billions in the design, development, and validation of these applications.

But once deployed in a production environment, support teams focus only on ‘keeping the lights on’ and fixing issues. This reactive approach means businesses are always operating on the edge. They cannot plan new technology and continue to run tasks in an obsolete manner. Something as trivial as a manual copy of worksheets into a master table could cost tens thousands of dollars across the lifetime of an application. They do not take advantage of automation and AI/ML to improve support tasks and do not see support as a driver of value.

Support teams are never noticed when applications run smoothly – but everyone is out for their heads when something breaks.

Flying under the radar

Support teams focus on two key criteria – the Service Level Agreement (SLA) and the Business Continuity Plan (BCP). Both the SLA and BCP aim to maximize stability.  The goal is to plan for all eventualities, maximize uptime, and identify and fix issues as soon as possible.

Organizations have focused on building support teams with a robust process mindset. They seek to meet SLAs using best practices from the project management and ITIL (IT Infrastructure Library) world. These teams are good at flying under the radar when everything is running fine. But they spend sleepless nights when something breaks – from a user unable to access an application, to a data pipeline failing, to the application itself going down for all users. In such cases, the support teams are subject to intense scrutiny until issue resolution and a return to steady-state.

A case for transformation

Present-day business processes are always evolving and need frequent updates. Applications are no longer composed of only code. They now include data pipelines and AI/ML models. They support myriad downstream business processes and reports. In such a world, it is no longer pragmatic to take a task-based approach to support. A recent survey asked 900 global CIOs, IT leaders, and decision-makers about obstacles they faced. 70% respondents felt that their technology provider did not help the organization innovate. 63% surveyed felt locked in a relationship with their current tech provider.

Continuous transformation will help support teams stay ahead of business needs and be ready for future challenges.

70% of surveyed IT leaders felt their technology provider did not help the organization innovate.

The transformation journey

The goal should be to maximize business value or bang for the support buck. We should seek to reduce the ‘Support cost/user’, by:

  • Improving support personnel productivity
  • Automating processes to improve efficiency
  • Bringing in new tools to streamline support and reduce the number of issues over time
Operate to Transform

Traditional support teams have been unidimensional – support engineers respond to and fix issues. But they go back to the data engineers and data scientists who built the process for deeper technical issues. This is counter-intuitive since it forces these personas to deprioritize their current tasks. Support teams should focus on skill diversity, enabling a more thorough response to the diverse range of issues.

Automation is the second lever for transformation. Organizations can automate routines such as process runs, access management, and resource provisioning. This will allow the support team to focus on more pressing issues. Enterprises should adopt AI/ML to check data, model, and code quality to respond to issues before they occur. 

Ticketing tools have been in place for years. But analyzing these tickets to identify patterns and mitigate the issues is much needed. Tracking tools can help review production business processes, and make the right changes to improve efficiency. Organizations also need a method to reduce the noise associated with monitoring. With monitoring come alerts, and a large volume of alerts lead to users missing important information. Ensuring alerts go to the right people at the right time is critical.

Smart people, better processes, and the right tools will improve the support experience. A decline in issues and faster issue resolution will create better user satisfaction over time.

In conclusion

The transformation journey will allow support teams to engage better with customers. The focus will shift away from SLAs (rather, SLAs not met) to ongoing value added to the business. This value is measurable in dollars saved per year, lower costs per user, and improved CSAT scores.  Such measurable improvements will enable support teams to become growth drivers.

Where are you in your support transformation journey?

The PoV was originally published on https://www.tredence.com/thought-leadership/

 

Author: 

Sanat Pai Raikar

Sanat Pai Raikar,
Head of Solutions Support, Tredence Inc.

 

 


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Tredence is a data science and AI engineering company focused on solving the last mile problem in analytics. The ‘last mile’ is defined as the gap between insight creation and value realization. Tredence is 1,000-plus employees strong with offices in Foster City, Chicago, London, Toronto and Bangalore, with the largest companies in retail, CPG, hi-tech, telecom, travel and industrials as clients.

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