Maximizing Business Impact: A Strategic Approach to Investing in Analytics

In today's data-driven world, using data analytics strategically can be a game-changer for startups. While growth or revenue benefits may not be immediately evident, investing in data can provide invaluable insights and pave the way for future success.

Why Invest In Data?

Challenges in Data Investment

Investing in "data" can be challenging for founders because there is no direct return on growth or revenue (at least in the short term). It's difficult to get reliable data, metrics, and dashboards. So teams tend to quickly implement a solution and worry about analytics once they or their investors start getting curious about business performance.

Finding data talent can be difficult and costly. Great data professionals are in demand, costly, and hard to find. And most data professionals either have technical depth or strong business acumen—rarely both. Given that startups need T-shaped employees, it would be ideal to have data people with technical and strategic competency. This leads to shortfalls in understanding between business stakeholders and data professionals.

"85% of data experts struggle to interpret business stakeholders' data needs" — Sigma Computing

The Consequences of Delaying Data Strategy

The problem with delaying a data strategy is that—until you create a data strategy—information will always be misaligned in your business. When your team communicates metrics and insights, they will have a different underlying understanding of that information, which, in turn, leads to an inconsistent view of the business.

Tip: The earlier you establish a data culture and align metrics, the more compounding value you'll extract from your analytics investment as the business scales.

A Strategic Approach to Data Investment

Startups can invest in data in incremental, low-cost ways. And this approach will provide compounding gains as your business and data scale. To understand this, we should break "data investment" into three categories:

1. Strategic Investment: Time spent thinking about your business' data conceptually, and building frameworks to organize and align your data. Examples of this include: creating OKRs; creating event, data, and metric catalogs; creating a semantic data layer; and discussing data security and governance.

2. Technical Investment: Investment in analytical infrastructure. Technical investment is comprised of these groups:

   • Cloud Services: AWS, GCP, Azure

   • Web Analytics: Google Analytics, Google Tag Manager, Amplitude, Mixpanel

   • Modern Data Stack SaaS: Fivetran, dbt, Tableau, Looker, Mode, Hex

   • Data Sources: Nielsen, Acxiom, Bloomberg, Facebook Graph, Feedly

3. Human Capital Investment: Hiring data professionals on a part or full-time basis. Common roles include Engineers (Backend, Data, Analytical, or ML), Analysts (e.g. Business Analysts, Data Scientists), and Leadership (e.g. Analytics Manager, VP of Data Science). This can also include Consultants—part-time hires or agencies with subject matter expertise.

These categories are ordered from least to most expensive. The more time we spend on strategic investment, the less costly and more effective technical and human capital investments will be.

In the sections below, we will discuss three low-cost ways startups can invest in data strategy.

1. Think Early, Grow Incrementally

Think Early

There is little cost in thinking about your data. However, thinking about—and, more precisely, strategically defining and organizing—your data will have massive gains down the line. And the earlier you do this, the greater the benefit. When your data is conceptually aligned at its core, it will also be aligned in how it's stored, how it's queried, and how it's communicated across the organization. This is System 2 thinking about your business data.

Grow Incrementally

Data can get messy and complex fast. To prevent this from happening, we should take small, incremental steps when building analytical capabilities and assets in our organization. What are examples of analytical capabilities and assets?

• Data Assets: datasets, data and ML pipelines, metrics, dashboards, self-service tools, and presentations

• Data Capabilities: building a customer segment catalog, building a machine-learning model to predict customer churn, and sending customers personalized promotions.

These capabilities would be difficult to build without first making strategic and foundational technical investments.

Capability Stepping Stones

Strategic investments, such as:

• Defining customer segments and their underlying metrics

• Building data trust in the Marketing team

Foundational technical investments, such as:

• Semantically structured data with high quality and consistency

• Robust but flexible data pipelines

Notice these "capability stepping stones" are strategic investments or foundational data assets. We've already discussed strategic investments. However, foundational data assets are the company's most frequently and broadly used data assets. They are either downstream data sources that have many dependencies or important datasets, data pipelines, or dashboards that are used broadly across the organization.

2. Get Event Tracking Right

The Foundation

"Garbage in, garbage out. And when your initial source is garbage, then you're in deep trouble."

Instrumentation is the process of tracking events in your application. This is our company data's point of inception and all the data our company creates and uses will be based on our instrumentation. Therefore it is critical we track our events accurately.

There are several tools you can use to track events, but the processes you use to track events are more important than the tools. Specifically, you should use these approaches:

1. Have an event-tracking workshop

2. Create an event catalog

Event Tracking Workshop

Some cringe when they see the word "workshop." You can call it a meeting or run it digitally. Regardless, we should gather key stakeholders from Finance, Product, Marketing, Engineering, and Analytics to identify the events we want to track. During this process, we should consider all the ways we'll use our data—these may include:

• Setting OKRs

• Creating quarterly reports

• Creating KPIs for each department

• Tracking product performance

• Providing content to customer support

Creating an Event Catalog

At the end of our Event Tracking Workshop, we should have a comprehensive list of the events we want to track in our application. We will document this in an Event Catalog, which will provide a single view of our data across the organization. Our Event Catalog should contain the following information for each event:

• Event Name

• Event Description

• Event Trigger

• Event Pages or Screens

• Entity (e.g. users, products)

• Metrics

• Data Source

Completing these two steps will set your team up for instrumentation success, regardless of the tool you use.

3. Create a Semantic Layer

A semantic data layer is a structured and standardized way of organizing and representing data within an organization to ensure that data is consistently interpreted and understood by both humans and computer systems.

Creating a complete semantic data layer requires considerable investment on both the technical and human capital sides. But there are several strategic investments we can make that will have lasting impacts on how we understand and discuss our business. And meeting and thinking time are the only costs.

Here are three strategic steps we can take to build a semantic layer:

1. Identify objectives and use cases

2. Create a logical data model

3. Define business terminology

Identifying Objectives & Use Cases

Identifying Objectives: What business goals can be supported by data, and how can data support them? By clearly articulating this, we'll have a unified view of why we're doing what we're doing with our data.

Identifying User Cases: We should identify how and why each employee will use data. That way we can build data products to best serve them. Think of this as a Product Manager would customer journeys.

Create a Logical Data Model

A logical data model is a high-level representation of an organization's data, focusing on its structure, organization, relationships, and business rules. It provides a conceptual view of data that is independent of any specific technology, database management system, or physical data storage. Building a logical data model is a crucial step for startups to ensure that their data is well-structured and aligned with business objectives.

Some of the key steps to creating a logical data model include:

• Identifying Data Entities

• Determining Entity Attributes

• Identifying Entity Relationships

Creating a conceptual map of your data will help technical stakeholders as they transform and analyze your data.

Define Business Terminology

Giving specific business-oriented names and definitions to your data assets is a critical step in connecting your data team to the business. In particular, we should define:

• Events

• Entities

• Metrics

• Segments

Conclusion

We started by asking, "Why invest in analytics?" There are clear risks and challenges. However—by making early, strategic investments in analytics—we can reap short and long-term business gains with almost no cost (only meeting and thinking time).

Three ways we can make low-cost, strategic investments in analytics are:

1. Thinking early, growing incrementally: Define and organize conceptual data maps early in the business' existence. Then build foundational data assets and incrementally produce value-added outputs from that foundation.

2. Getting event tracking right: Event tracking is our data's point of inception. If it's inaccurate, all other business information will be incorrect. Track and document your events carefully.

3. Creating a semantic layer: The semantic layer translates your data assets into a business-comprehensible format.

These steps will provide compounding gains as your data team scales. By prioritizing strategic investments early, you position your startup for long-term analytical success and better decision-making as you grow.

The Data Strategist helps startups and scaleups become data-driven. Get a data scientist on-demand, or advice on building your analytical data stack.