Author: Deepak Jannu

  • Three Insights from the World’s Leading IT Organizations

    Three Insights from the World’s Leading IT Organizations

    IT is in a state of change on a consistent basis, but never more so than now. With the increasing importance of the digital customer experience across all industries, technology is at a new inflection point in how it can fundamentally impact executive priorities. And while nobody can speak for the direction of IT as a whole, there are a few resources so comprehensive that they’re worth reviewing when they’re released.

    Take, for example, the 2019 Harvey Nash/KPMG CIO Survey. It’s the largest global survey of CIOs with data from 3,645 respondents across 108 countries. IT leaders who participated in the survey controlled a whopping $250+ billion in annual IT budgets. The CIO survey examines how board priorities, technology adoption, skills gaps and IT leadership is changing in the age of digital business.

    Here are three insights that explain the tectonic shifts occurring in enterprise IT teams today.

    Digital Leadership: Three Steps to Becoming a Digital Native

    The 2019 Harvey Nash/KPMG CIO Survey shows only 30% of IT organizations are digital leaders. These organizations are unusually adept at “using digital technologies to advance their business strategy,” and can launch innovative solutions that generate new revenue streams, enable faster time to market and cut down operational costs. Here are three defining characteristics of what it takes to be a digital leader in 2019:

    • It’s All About Data. Data management and analytics is a core competency of leading IT organizations. Digital leaders are in a strong position to maximize the value of their organizational data by extracting, aggregating and curating the right information. This data is then used to train machine learning models, create high-value assets and deliver customer value on an ongoing basis.
    • Speed of Execution. Digital leaders are willing to either quickly double down on successful experiments or pull the plug on products that don’t deliver expected value. They are also more likely to leverage agile and DevOps practices to increase software release velocity and iterate rapidly on solutions using fast feedback loops.
    • Expansive Mindset. Digital leaders have transitioned from project thinking to product-centric innovation for creating enduring value from their software investments. They also build cross-functional teams across their business and IT silos for better collaboration and faster decision making for digital transformation initiatives.  

    What Does This Mean? CIOs will need to embrace big data analytics, agile delivery models and product-centric teams to create the right foundations for digital business. Without proper foundational investments, IT leaders will struggle to meet business demands, gain customer trust and withstand competitive pressures in the face of escalating customer expectations.

    Business-Managed IT: How Shadow IT Finally Became Respectable

    In the last decade, the adoption of cloud services led to the phenomenon of shadow IT where business units would directly buy cloud solutions without consulting IT teams. CIOs resented their growing irrelevance and tried their best to bring shadow IT under their control.

    The 2019 CIO survey confirms 64% of enterprises have made peace with business-managed IT investments. In fact, 10% of IT leaders actively endorse this approach. What factors have led IT teams to change their mind on shadow IT?

    The survey shows organizations with business-led IT scored better on key metrics than organizations without any mandate for business-managed technology spend. Business-led IT organizations delivered “better customer experiences and faster time to market for new products (52% more likely) as well as positive employee experiences (38% more likely).”

    For business-managed IT spending to be truly successful, CIOs will require the right guardrails in the form of central IT oversight. The 2019 CIO survey makes the case that organizations without IT team involvement are twice as likely to have multiple security issues and are also less likely to build customer trust with their technology investments.

    What Does This Mean? Instead of actively opposing shadow IT investments, CIOs should partner with their business counterparts to create effective governance and operating models for business-managed IT spending. Central IT oversight will ensure the right support for digital business initiatives, reduce the risk of unauthorized breaches and minimize wasted spend.

    Influencing Without Authority: What It Takes to Be an Effective Technology Leader

    Board membership for CIOs dropped this year, with only 58% of IT leaders being part of the executive team in 2019 as compared to 71% in 2017. Does this mean CIOs no longer have executive backing for orchestrating and executing digital business strategies in their enterprise?

    The 2019 CIO Survey indicates board membership, headcount or annual budgets are not the right indicators of the power and influence of technology leaders. Instead, CIOs should focus on building trust across organizational boundaries, creating partnerships with the right executive stakeholders and ensuring the success of critical initiatives. CIOs working for digital leaders are comfortable exerting influence without authority and are driving organizational growth, by being “three times more likely to collaborate strongly with business leaders.”

    What Does This Mean? Formal board membership or the size of the IT team is not the best way to assess CIO influence. The best technology leaders ensure successful enterprise transformation by garnering strong commitment from the board, building digital capabilities with cross-functional teams and creating a sense of urgency for rapid innovation.

    — Deepak Jannu

  • 5 Trends Transforming Digital and IT Operations Management

    5 Trends Transforming Digital and IT Operations Management

    In 2018, digital transformation ushered in a radical shift in how enterprises harness customer insights, technology capabilities and rapid experimentation to drive revenue growth, profitability and market leadership. Enterprises spent $1.3 trillion in 2018 on digital transformation technologies such as public cloud platforms, microservices and containers, edge computing, machine learning and artificial intelligence to improve customer experiences, business agility and employee engagement.

    Heading into 2019, digitalization will accelerate business model innovation and drive the adoption of these emerging technologies for sustainable competitive advantage. Here’s a closer look at five technology trends that will change the way enterprises approach digital operations management in 2019:

    Multi-Cloud Adoption Goes Mainstream

    In FY18 Q3, the three leading cloud platforms (Amazon Web Services, Microsoft Azure and Google Cloud Platform) accounted for 65 percent of the market share of public cloud infrastructure and platform services. Enterprises are increasingly relying on multiple cloud providers to avoid lock-in and harness innovative product offerings, global presence and flexible pricing strategies. When it comes to multi-cloud adoption in 2019:

    • Lift-and-Shift Will Give Way to Rebuilding. In the initial phase of enterprise cloud adoption, CIOs preferred “lift-and-shift” strategies for migrating complex, legacy applications to public cloud services. In 2019, enterprises will rebuild legacy portfolios to truly take advantage of the cloud’s elasticity, security and utility pricing. Rebuilding is not only the right approach to modernize legacy systems, but also a viable solution for reducing technical debt and unleashing enterprise agility.
    • The Rise Of Managed Private Cloud for On-Premises Requirements. While analyst firm Gartner predicts that 28 percent of enterprise spending will shift to public cloud offerings by 2022, the reality is that the vast majority of enterprise workloads still run inside the corporate firewall. Public cloud providers are taking aggressive measures to conquer the final bastion of data center gravity with private cloud offerings. Managed private cloud infrastructure will become essential for enterprise services that will need data sovereignty, latency or compliance, yet still want the flexibility and agility of cloud. Some recent managed private cloud announcements from the big three cloud platforms show how these providers are taking a pragmatic approach to meet customer demand:
      • In 2016, Andy Jassy, CEO of Amazon Web Services, declared, “Whether it’s 10 years or 20 years, very few companies will own their own data centers, and those that do will have a smaller footprint than they have now.” During his 2018 re:Invent keynote, Jassy sang a different tune by launching AWS Outposts to bring the power of cloud-native services to enterprise data centers for hybrid interoperability and seamless management.
      • During the 2018 Cloud Next conference, Google introduced its Google Kubernetes Engine (GKE) On-Premises that delivers an abstraction layer for running container clusters either on the public cloud or in a corporate data center.
      • In 2017, Microsoft announced the general availability of Azure Stack, which offers the same programmable interface and deployment tooling of Microsoft Azure in a private cloud environment for a consistent hybrid cloud experience.

    The Cloud-Native Skills Gap Widens

    Software containers have fast emerged as the standard deployment vehicle for agile, reliable, scalable and highly available enterprise services. The transition from virtual machine management to container orchestration and monolithic development patterns to agile microservices will transform the way enterprises build, deploy and optimize cloud-based applications. IT operations teams will need to develop a new set of competencies to manage the health and performance of containerized services. IT practitioners can longer rely on manual scripts to manage large, distributed systems but will need to embrace infrastructure as code to provision, deploy, monitor, alert and auto-scale cloud-native services.

    Commodity Monitoring Evolves to Strategic Observability

    DevOps teams will use multiple data sources (metrics, logs, and traces) to understand, verify and debug cloud-native systems. Modern observability requires the ability to consolidate and extract relevant insights from observability pipelines using tools such as Prometheus for monitoring, Jaeger for distributed tracing and Fluentd for logging. While IT teams will need new tools to manage autonomous cloud-native services, it is still critical to understand relationships between individual components and the overall system to effectively troubleshoot microservices in production.

    AIOps Emerges as the Way to Tame Alert Storms

    Artificial intelligence for IT operations (AIOps) is a clear necessity for modern IT infrastructure teams to keep up with the diversity, velocity and volume of IT operational and event data. IT teams can proactively prevent service outages, analyze real-time incidents using contextual knowledge and remediate problems using AIOps tools. Here’s how AIOps will help enterprises run and optimize mission-critical systems:

    • Improve Response Time For Digital Interactions. AIOps has the power to boost key metrics for incident management including mean-time-to-detection, mean-time-to-response, mean-time-to-restoration and incident volume handled within a service window. The combination of machine learning and data science techniques in AIOps not only delivers faster incident coordination and response, but also reduces the human time spent per alert with advanced analytics and probabilistic root cause analysis.
    • Eliminate Siloed/Redundant Processes. AIOps offers the ability to consolidate event and incident insights from different IT management tools across on-premises and public cloud environments. A shared AIOps platform offers centralized visibility, faster impact analysis and improved collaboration for a diverse set of stakeholders, including application owners, infrastructure teams and business sponsors.

    Business-Aware, Service-Centric Delivery Can Optimize Customer Experiences

    To help the business remain competitive, IT teams simply must transition from managing hybrid IT infrastructure assets to prioritizing and supporting business-critical services. Service-centric operations management combines business insights, customer knowledge and relevant metrics to improve digital performance, enhance customer interactions and support business goals. IT teams will need to focus on the business value of IT services while offering holistic infrastructure visibility and control at the business process level.

    Clear strategy and focused execution will separate the digital winners from losers in 2019. How enterprises embrace and respond to these key trends will be key to unlocking disruptive innovation, operational transformation and business value.

    — Deepak Jannu

  • Splunk Acquires VictorOps: The End of the Road for Standalone IT Alert Management Tools?

    Splunk Acquires VictorOps: The End of the Road for Standalone IT Alert Management Tools?

    This week, Splunk, the leader in machine data intelligence, announced the acquisition of IT alerting solution VictorOps for $120 million. My congratulations to the VictorOps team for building a solid product for alert management and collaboration, demonstrating a wicked sense of humor and creating some cool T-shirts.

    While the acquisition makes sense on many levels, it does raise broader questions about the need for standalone IT alerting tools. Let me explain why IT alert management as a category faces strong headwinds and how these tools will need to evolve if they are going to remain relevant in a world driven by digital transformation.

    What Do IT Alert Management Tools Actually Do?

    When an IT outage or service disruption occurs in an enterprise, the first order of business is to restore operations back to normal. IT outages can cost you a lot (an hour of downtime can cost you upwards of $100,000 per hour), disrupt your customer experiences and derail employee productivity. To quickly troubleshoot an IT incident, you’ll need to notify the right subject matter experts who can collaborate together to pinpoint root cause for an outage.

    Every day, enterprise network operations center (NOC) teams receive hundreds of alerts from different IT management tools for monitoring, service desk, event correlation and configuration management. A certain percentage of these alerts demand urgent attention from NOC teams who are on-call and readily available for incident support.

    Enter IT alert management tools, whose job it is to manage the notification of messages to personnel based on predefined rules regarding who to notify for a given categorization of event or incident. Tools from PagerDuty, VictorOps, xMatters, OpsGenie and Everbridge alert the right team members using on-call schedules and notify them using a variety of channels including email, text, voicemail and chat. IT alerting tools help you manage the entire incident life cycle, using on-call schedules, alert routing rules and collaborative war rooms.

    Embedded Alert Context: The Missing Piece of the Alert Puzzle

    The fundamental challenge with scaling enterprise alert management is about establishing the right context for a flood of alerts that you’ve just received. Instead of forwarding every single alert that you receive, can you share actionable alerts that your NOC teams can immediately act upon?

    Your IT alerting tool needs the ability to correlate a bunch of alerts into a single event for faster acknowledgment and troubleshooting. Alert consolidation and correlation call for intelligence that can extract signal from the noise and compress alert volumes with the right inferences. Artificial intelligence for IT operations (AIOps) helps you do just that, by applying big data and machine learning techniques to event management data. With AIOps tools, you can handle events and minimize alert noise by delivering timely and actionable insights for incident support. This agility and insight, coupled with intelligent correlation to stay on top of major outages, helps NOC teams by focusing their attention on the incidents that truly matter.

    AIOps + Alert Management = Smarter Incident Management

    Without the ability to apply machine learning algorithms to alert floods, alert management tools act as “dumb forwarders” of IT alerts, inducing alert fatigue and adding to the overall noise. It’s time for alert management to get smarter and contextual with machine learning insights.

    Splunk’s acquisition of VictorOps is a clear sign that alert management tools as a standalone category can’t truly scale without a solid AIOps foundation. With artificial intelligence, IT teams can stop wasting valuable time figuring out incident triage and root cause analysis and ensure faster recovery from crippling outages with the right event analytics.

    — Deepak Jannu