Everyone is an Analyst: Opportunities in Operational Analytics

TL;DR

Digital transformation is accelerating, leading to the automation and digitization of work processes, resulting in a shift towards more decision-making and data-driven analysis for information workers, creating new opportunities for startups in operational analytics.

Transcript

my name is Jed Mouse I'm a partner at a 16z on the enterprise deal team prior to that I was an architect at AppDynamics and I've noticed this trend since my time there and its really accelerated more recently and it's a second-order effect but it's an important one of the digital transformation oh yeah boy digital transformation well it's happening... Read More

Key Insights

• 🧑‍⚕️ Digital transformation involves both digitization and automation, leading to changes in work processes for information workers.
• 🧑‍⚕️ The structure of work is shifting towards more decision-making and data analysis, requiring operational workers to use data to drive their actions.
• 💗 There is a growing need for operational analytics tools that provide immediate access to data and insights for information workers to make real-time decisions.
• 🧑‍💻 Startups have opportunities in building vertically integrated tech companies, providing operational analytics infrastructure, creating industry-specific operational analytics applications, and developing role-focused operational analytics tools.
• 😘 Industries with high capital expenditure or low margins can benefit greatly from operational analytics, improving returns or reducing costs.
• 🏛️ Successful operational analytics companies focus on depth in solving specific problems for a particular role, building brand recognition as a moat against competition.

Questions & Answers

Q: What are the two major components of digital transformation?

The two major components of digital transformation are digitization, which involves replacing manual processes with digital forms, and automation, which involves automatically collecting and processing information.

Q: How is the structure of work changing due to digital transformation?

The structure of work is shifting from rote work to more decision-making and data analysis. Information workers will spend more time making decisions and using data to drive their actions.

Q: What is the need for operational analytics tools?

Operational analytics tools are needed to provide information workers with immediate access to data and insights so they can make informed decisions in real-time.

Q: What are some examples of industries that can benefit from operational analytics?

Industries such as oil and gas, manufacturing, mining, groceries, and construction can benefit from operational analytics, as it helps improve returns on capital expenditure or reduce costs to increase gross margins.

Summary

In this video, Jed Mouse discusses the concept of digital transformation and its impact on information workers in enterprise. He explains that there are two major components to digital transformation: digitization and automation. Digitization involves moving from paper or manual processes to digital processes, while automation involves using technology to collect and analyze data and make decisions without human intervention. Mouse then dives into how the structure of work is changing, with a shift towards more decision-making and data analysis tasks for information workers. He explains that this shift requires new tools to support operational analytics, which can provide immediate and self-service access to data and insights. Mouse also explores various opportunities that arise from the need for operational analytics, such as building vertically-integrated tech companies, developing infrastructure for easy data access, creating industry-specific analytics applications, and focusing on role-specific analytics solutions.

Questions & Answers

Q: What are the two major components of digital transformation?

The two major components are digitization and automation. Digitization involves moving from paper or manual processes to digital processes, while automation involves using technology to collect and analyze data and make decisions without human intervention.

Q: How is the structure of work changing?

The structure of work is shifting towards more decision-making and data analysis tasks. This means that information workers will spend more time making decisions and using data to inform those decisions. Rote and manual tasks will become a smaller portion of the work, and there will be a greater focus on creative work, communication, and coordination.

Q: What impact does digitization have on data collection?

Digitization makes data collection much easier and more observable. For example, moving from paper loan applications to digital loan forms allows for faster processing and the ability to add new fields or make changes to the process more easily. This means that information workers can have better access to the data they need to make good operational decisions.

Q: How has automation changed the work of product managers?

In the past, product managers would spend a lot of time doing customer surveys and talking to customers to gather information about problems and feature requests. However, with automation, tools like Mixpanel or Amplify can now provide insight into the customer journey within a product. This allows product managers to identify bugs or features that need to be addressed without directly talking to customers. They can prioritize changes based on data from CRM systems, and even roll out changes to specific customers using continuous delivery and A/B tests.

Q: How has automation impacted marketing?

In the past, marketers relied on intuition and ran focus groups to determine what creative strategies might work. However, the rise of growth hackers, who combine engineering capabilities with marketing creativity, has transformed marketing. Growth hackers use data analysis and experimentation to figure out what works and what doesn't. They can run experiments, expand successful strategies, and cut off unsuccessful ones. This data-driven approach leads to systemic success and is being adopted across various roles and industries.

Q: What kind of tools are needed for operational workers to observe and act on their own data?

Operational workers need tools that are operational, immediate, and self-service. These tools should provide real-time access to data and insights, allowing workers to make quick decisions based on current information. Traditional tools like Hadoop and BI are not sufficient, as they require specialized skills and are often only affordable for executives. New operational analytics tools are needed to serve the needs of operational workers.

Q: How is the world changing with the rise of operational analytics?

The rise of operational analytics is changing the world in several ways. Firstly, software is eating into traditionally tech-phobic industries, with technology companies like Uber and Airbnb competing successfully against incumbents. Secondly, there are opportunities to enable other companies to win with operational analytics by providing infrastructure, industry-specific insights, and role-specific tools. Finally, every layer of the operational analytics stack, from ETL to presentation, needs to be reconfigured to be operational, immediate, and self-service.

Q: How can companies win in tech-phobic industries using operational analytics?

Companies can win in tech-phobic industries by building vertically-integrated tech companies. The recipe for success involves choosing a tech-phobic industry, providing a digital user experience, utilizing operational analytics to create efficiencies, undercutting existing players with lower pricing, and building an operational moat to continuously stay ahead of competitors. This requires a focus on operational analytics to gain an edge in traditionally resistant industries.

Q: What opportunities exist in operational analytics infrastructure?

There is a need for new infrastructure in operational analytics, particularly in the areas of data access and presentation. Tools that make it easy for non-technical users to access and understand data are needed. Additionally, there is a need for automatic policy enforcement around data security, allowing companies to control who can access what data without compromising privacy. Operational analytics infrastructure companies need to focus on creating self-service and user-friendly tools for operational workers.

Q: How can companies succeed in industry-focused operational analytics?

To succeed in industry-focused operational analytics, companies must build end-to-end domain-specific products that are easy for customers to adopt. Building domain expertise and providing strong professional services are also crucial. Success in this space involves speaking to the key performance indicators (KPIs) of the industry, such as improving the return on capital expenditure for capital-intensive industries or reducing labor costs for low-margin industries. Offering tangible paths to these KPIs is important for convincing customers of the value of operational analytics solutions.

Q: What is important to consider in role-focused operational analytics?

Role-focused operational analytics targets specific roles within an enterprise, such as sales, customer success, or engineering. To succeed in this area, it is important to find roles that have been traditionally underserved by technology or where tools have been top-down rather than organically adopted. Building a strong brand as a moat is crucial in this space, as there is often low entry barriers and competition. Going deep and solving specific problems for a role is important before expanding into adjacent roles or spaces.

Takeaways

Operational analytics is changing how people work, and it presents various opportunities for startups and established companies alike. The shift towards digitization and automation is impacting information workers in enterprise, requiring new tools to support their decision-making and data analysis tasks. Vertically-integrated tech companies can compete in traditionally tech-phobic industries by providing digital user experiences and using operational analytics to create efficiencies. Infrastructure, industry-focused applications, and role-focused tools are all needed to enable companies to win with operational analytics. Key factors for success include user engagement, industry-specific expertise, and addressing the KPIs that matter to the target audience. The world is changing, and operational analytics is at the forefront of this transformation.

Summary & Key Takeaways

• Digital transformation involves two components: digitization, which replaces manual processes with digital forms, and automation, which involves automatically collecting and processing information for tasks such as loan applications and credit history checks.

• The structure of work is changing, with a shift from rote work to more decision-making and data analysis. This means that information workers will need to spend more time making decisions and using data to drive their actions.

• The need for operational analytics tools is growing, as information workers require immediate access to data and insights to make informed decisions in real-time.