Everyone is an Analyst: Opportunities in Operational Analytics

Digital transformation is accelerating via two core components:

• Digitization: Shifting manual/paper processes to digital forms (e.g., online loan applications) to increase observability and flexibility.
• Automation: Automatically collecting data (e.g., social security queries, credit history) and executing actions (e.g., pre-approvals) without human intervention, fundamentally altering work structures.

The structure of work is shifting from "roadwork" (tedious, rote tasks) to three new buckets:

• Decision making: Synthesizing data to plan actions and determine next steps.
• "Everything else": Creative work, communications, and coordination, which remain difficult to automate.
• Result: As roadwork diminishes, professionals spend more time on decision-making and data analysis, effectively becoming "operational analysts."

Concrete impact on roles:

• Product Managers: Transitioned from manual customer surveys to using tools like Mixpanel or Amplify to track customer journeys, identify bugs, and prioritize features based on CRM data and A/B testing.
• Marketing: Shifted from intuition/focus groups to "growth hacking," where half-engineers/half-marketers run data-driven experiments to prune failures and scale successes systematically.
• Scope: This trend affects operational staff across all roles (sales, customer success, engineering) and industries (construction, manufacturing, groceries), not just executives.

The "Tool Gap":

• Legacy tools (Hadoop, BI): Require specialized skills, armies of analysts, and provide answers with a lag of up to three months; these only serve executive strategic questions.
• New requirement: Operational analysts need immediate, self-service tools to answer day-to-day questions (e.g., real-time competitor response, specific customer segment behavior).
• Outcome: A new category of operational analytics tools is emerging to allow non-technical users to observe and act on their own data.

Market Opportunities in Operational Analytics:

• Opportunity 1: Tech-eats-phobic industries

  • Tech-focused companies (Uber, Lyft, Airbnb, Flexport, Samsara) are winning in traditionally manual industries by combining better UX with operational intelligence.
  • Winning recipe: Select a tech-phobic industry, provide a digital user experience, and use analytics to undercut incumbents via lower pricing/efficiencies.
  • Infrastructure examples: Facebook uses Scuba; Google uses Dremel/BigTable; Airbnb uses Superset; Uber uses AriesDB.

• Opportunity 2: Infrastructure Enablers

  • Goal: Build layers (ETL, storage, processing, access/presentation) that are operational, immediate, and self-service.
  • Key Challenges:
    • ETL: Solving issues with raw, messy, inconsistent data remains unsolved.
    • Access/Security: Needs automated policy enforcement to allow broad data access without privacy risks.
  • Growth Strategy: Target organic adoption by operational users (not executives) via easy-to-use "small bites" rather than top-down sales.
  • Primary KPI: User engagement (revenue is a lagging indicator).
  • Example: Implify uses open-source Druid for organic growth, then sells an application for streaming interactive analytics and real-time data troubleshooting.

• Opportunity 3: Industry-Focused Applications

  • Target Sectors: High CapEx industries (oil/gas, manufacturing, mining) and low-margin businesses (groceries, construction).
  • Value Proposition: Slight improvements in Return on Capital (ROCE) or gross margins result in massive revenue impact.
  • Winning Requirements:
    • Build "whole products" requiring minimal integration effort.
    • Develop deep domain expertise to justify consultative sales.
    • Explicitly link product outcomes to KPIs (ROCE or gross margins).
  • Examples: Doxil (construction progress mapping via ML), Kelvin (well optimization in oil/gas), Samsara (fleet optimization to improve utilization and margins), Afresh.

• Opportunity 4: Role-Focused Applications

  • Focus: Specific enterprise roles (sales, customer success, product) often underserved by top-down IT tools.
  • Strategy:
    • Select a role where tools were historically pushed down from IT.
    • Build a strong brand moat to defend against fast followers due to low barriers to entry.
    • Prioritize depth over breadth; solve 100% of problems for that specific role before expanding.
  • Examples:
    • Mixpanel: Product analytics.
    • People.ai: Records sales-customer interactions to build buyer graphs, guiding salespeople on whom to contact and how to close deals.