Data and data analytics have always been the foundation to drive actions for IT operations. Analytics has been instrumental in supporting capacity planning, resource optimization, workload rebalancing, cost projections, and security predictions. But now, there are new demands on IT operations to deliver more inclusive data intelligence that can support decision-making at large across IT and the business. ITOps teams have a growing mandate to positively influence business and customer-related outcomes; their ability to collect, analyze and use data to support many types of decision-making is now top-of-mind.
IT operations teams are being asked to cut expenses, migrate to cloud, improve productivity and innovate at the speed of business while being more proactive to resolve operational issues —basically, do more with less. While dealing with these challenges, they have to ensure their customers are happy and renewing.
At the same time, innovation through convergence across hardware and software; cloud, on-premise and edge; and DevOps/ITOps has facilitated rapid and agile software development with open source tools and technology. There is an imperative for every I/O leader to enable an always-on customer experience, zero marketplace disruption and leading competitive differentiation.
ITOps: the struggle of catching up to digital business acceleration
Yet the fallout of convergence on IT operations can be brutal. Team members struggle to understand how the polyglot development organization is working in this high velocity environment in order to manage change and reduce risk appropriately. There’s also the opacity of managing heterogeneous environments and hybrid infrastructure where numerous containerized or virtualized layers are dynamically reconfiguring.
Here are the pain points of traditional data analysis methods:
- IT operations can’t understand the holistic needs of the customers along with short and long-term impacts on customer experience. This means a potentially large disconnect between the business (product or service) and the market (customers).
- Broken visibility also leads to poor investments. The organization will under or over-invest in key areas such as development, security, infrastructure and monitoring, without an accurate, holistic picture of where the important gaps actually exist and how everything ties together.
- ITOps must carry on with a reactive approach– operating blindly and moving from solving one issue to the next without a big-picture view of priorities and needs.
- Reactivity curtails innovation and marketplace competitiveness. IT staff is continually hampered by fixing things versus optimizing the entire environment for the customer. We see daily examples of enterprises going out of business because they didn’t adapt to changes from digitalization–a stark reality in 2020. As just one example, take ClassPass, a leading fitness and wellness subscription service/app which allows customers to sign up for in-studio or digital live streaming fitness events from boutique studios and spas all over the country. The company has raised $549 million and booked 100 million reservations since its founding in 2013. Meanwhile, traditional gym chains without viable digital offerings such as 24-Hour Fitness and Gold’s Gym, fared poorly in 2020, shuttering locations and filing for bankruptcy. It’s easy to find similar examples across restaurants, hospitality/lodging, retail and banking among others.
A new technology framework for holistic data analytics in IT
Fortunately, technology has caught up to support this new role for IT operations. The advancement in affordable technology innovation on compute, storage, AI and ML have made it possible to derive meaningful insights from disparate, discrete and federated data sources supporting the core functions of an enterprise.
To keep pace with constant change, IT operations teams must build a data-driven view of the organization from the customer perspective: cross-cutting different integrated and siloed functions of business and technology. Having the right information at the right time to make the right decision will drive efficiencies throughout the organization. Gaining this data-driven operating environment, however, requires fundamental changes in technology and process.
This begins by leveraging the discovery, monitoring, APM, networking and AIOps tools in a cohesive and integrated way. The goal is to gain end-to-end contextual visibility in near real-time and apply data analytics to drive the best decisions and actions. IT leaders will need to develop an overarching strategy for tools evaluation and justification. They will also need to tailor skill sets and create new organizational roles necessary to get the ROI. Domain-specific processes must go away as part of this effort.
Industry examples
Modern day ITOps analytics based on real-time data collection, ingestion and analysis across multiple domains brings intelligence which can predict IT issues affecting customers. The data-centric operation also can inform broader strategies on improving customer experience and business outcomes. This is the promise of DataOps, AIOps and operational analytics. Here’s how this can work in practice:
Scenario 1: Ecommerce. Retailers live and die upon website experience. They need to track metrics such as shopping cart abandon rates and transaction time. It’s IT’s job to understand how to collect, analyze and correlate the right data from infrastructure resources to those business metrics. The goal is to use analytics to identify website bottlenecks caused by poor-performing systems or where a user process could be better by, for instance, reconfiguring the cloud architecture.
Scenario 2: Healthcare. Healthcare delivery has been pushing toward better outcomes and waste elimination for years now. Add Covid-19 restrictions to that equation, and you have the perfect storm for digital disruption. Healthcare providers have been investing in telehealth services like never before, and introducing easier ways for patients to interface with staff, such as by scheduling appointments online and tracking and submitting vitals electronically. Those digital services need to be operating so flawlessly that patients won’t hesitate to try them and use them again and again. AIOps will be a game changer here, by predicting and even fixing application website and app issues before they become real problems. New ITOps tools and the data they produce will connect the dots between financial health and technology implementation at leading healthcare organizations.
Scenario 3: Banking. In financial services, metrics for success could include the number of electronic transactions and interactions customers conduct per week and correlation of that number to lower attrition rates or increased revenue per customer. ITOps would need to gather data from those different transactional systems over time, understand usage patterns and incidents, and then optimize the underlying configurations and remediation workflows accordingly to support the business KPIs.
Moving to a data-driven operation requires top-down support and sponsorship by the senior IT executive team and investment in new roles such as a CDO (Chief Data Officer). Many IT leaders have already been doing this for a while, but understanding business KPIs by leveraging customer and transactional data will help the organization create the right data strategy and select the best analytics and automation tools.
About the Author
Bhanu Singh is SVP of Engineering and DevOps at OpsRamp. He is an accomplished and decisive leader in the software technology industry with extensive experience in product strategy, disruptive innovation and delivery to grow market share, revenue, and improve customer experience. Singh is an expert in large-scale global engineering management, software development life-cycle, operational process optimization, transformation projects, global talent development and customer engagement. He is a change-agent focused on the product life cycle process for faster time-to-market while driving effective global organizational transformation through budget rationalization and execution efficiency. He has a passion for building products, challenging status-quo and developing high performing teams.
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1
Speak Your Mind