Data-Driven Automation: The key to successful digital leadership

The constant call for IT leaders to deliver ‘digital transformation’ are almost deafening. Everyone from customers to staff to the board of directors expect IT leaders to respond to the digital mandate with new and innovative use cases, using new technology assets to deliver new business outcomes.

Of course, IT has always been digital, so there is more than a touch of hype to this wave. Nevertheless, there are new aspects to IT that underpin this buzzword. For example:

• New technologies for engaging and understanding customers, like mobile devices, Internet of Things, analytics, machine learning, and AI are putting exponentially more demand on technology leaders and their teams.
  
  
• New platforms and delivery methods, like cloud computing, containers, server-less computing, open source, and composable applications are giving technology leaders exponentially more moving parts to manage.
  
  
• New service delivery approaches, like Agile development, ‘design for failure,’ minimum viable product, continuous integration/continuous delivery, and DevOps demand a faster and more accurate response from technology leaders.
  
  
• New ecosystems built from microservices and APIs are massively complex and infinitely scalable, with exponentially more components for IT leaders to manage, across more broadly distributed providers and interconnected services.

These show that digital transformation is both new and real, despite the hype, and it is creating unprecedented challenges for IT leaders.

However, there is a light at the end of the digital transformation tunnel, and it is powered by data-driven automation.

Benefits of automation are clear

IT leaders have leveraged automation for decades, and (notwithstanding some challenges) the results are clear. Systems and activities are faster, more accurate, and more reliable. Online self-service accelerates customer and user experience while reducing costs and increasing margin. Enabling and even enforcing ‘known good process’ reduces errors and improves audit and control. Fewer manual processes improve repeatability for mundane activities while allowing staff to focus on intelligent human work.

However, traditional ‘rule-based’ automation is not enough for the new world of digital transformation. It is hard to implement, and requires deep knowledge to define decision trees, workflows, and activities that power automated processes. It relies on simple triggers, like a user request, a file or flag update, a service desk ticket, or a date/time event. It assumes static environments with pre-defined configurations issuing well-formed and recognizable events. Traditional rule-based automation simply does not adapt well to the rapidly changing environment typical of new digital architectures.

What is data-driven automation?

Digital applications instead rely on data-driven automation to tackle the unprecedented scale, complexity, rate of change, and breadth of digital systems.

For example:

• Collaborative service delivery teams (DevOps, CI/CD, etc.) use data-driven automation for faster, more accurate development, testing, provisioning, release, and governance, so Dev and Ops teams can deliver and update new digital services faster than ever before.
  
  
• Modern IT Ops and site reliability engineering (SRE) teams use deep insight and observability metrics to feed machine learning and predictive analytics to detect, diagnose, triage, and resolve problems faster, often automatically, and even prevent new incidents entirely.
  
  
• Systems of engagement leverage data-driven automation to personalize customer experiences by connecting activity across multi-channel platforms and mapping customer behaviors to predict demand, uncover conversion opportunities, or get ahead of service issues.

Data-driven automation helps deliver the agility and visibility required for digital success. While not a simple undertaking, three high-level steps will help you adopt a data-driven automation approach:

• Discover – use data from Operational Intelligence (OI) systems to gain visibility into digital technologies, platforms, delivery processes, and ecosystems. Data-driven systems for IT Operations Analytics (ITOA) collect relevant information and identify target events much faster than rule-based legacy tools, even across scaled, dynamic, distributed digital environments.
  
  
• Analyze – extend OI with machine learning and next-generation analytics. Systems that leverage Artificial Intelligence for IT Operations (AIOps) enable your systems and teams to analyze the massive volume of real-time ‘digital exhaust’ to rapidly find actionable insights, and make better decisions on anything from problem remediation to proactive customer outreach.
  
  
• Act – use these rapid, reliable, action-oriented insights to safely and effectively automate actions. Start with simple tasks, manual approvals, and gated workflows, identifying and encoding ‘known good patterns’ of response. Chain simple tasks together into complex processes to enable data-driven automation to free staff from mundane activities.

It is essential to get beyond the buzzword bingo of digital transformation and take concrete actions to increase the speed of service delivery, manage quality, mitigate risk, and engage customers in new and creative ways to drive better business results. New architectures, platforms, methodologies and ecosystems are all creating new challenges for digital transformation.