Creating a Data Ecosystem to Guide Key Business Decisions

A data ecosystem is a structure of tools and platforms that connects data from all parts of the customer journey. It enables you to access, analyze, and use customer data to inform business decisions. A data ecosystem is a must-have for organizations interested in growth.

What is a data ecosystem?

A data ecosystem connects data from different parts of the customer journey. It houses and processes information from all customer touch points with your company and passes data between various tools and platforms. You can use data ecosystems to make data-backed changes that guide users to take specific actions.

A data ecosystem consists of three parts:

1. Infrastructure: Collects and transforms raw data from different sources
2. Analytics: Generates insights about customers and what drives their behavior
3. Activation: Uses insights to segment customers and encourages them to take specific actions

Let’s take an online shopping platform as an example. Your hypothetical ecommerce platform starts by collecting data in the infrastructure part of the system. You’re looking to track users for important information, like what items customers click on and how much money they spend per site visit.

You then use the analytics part of the data ecosystem to investigate which user types abandon their carts and discover that it’s a mix of repeat and new users. Because the analytics platform directly connects to your marketing tools in the ecosystem, you can quickly act on this information to drive revenue.

To do so, your marketing team segments repeat and new users and runs a different campaign for each one. They send a “20% discount off your first purchase” code to the new users. For the repeat users, they offer extra points on the rewards scheme if they buy the products that are in their cart.

Although all these processes are possible without a data ecosystem, having one increases efficiency and accelerates time to action.

For example, without a data ecosystem, you’d have to use a standalone CRM to collect user data, then use an analytics platform to pull insights, and finally run the campaigns from a marketing platform. But with a data ecosystem, everything is linked, so it’s faster and easier to manage data and run these processes.

Big data components to include in an ecosystem

The specifics of your ecosystem will depend on your organization’s goals and needs, but all effective data ecosystems include the same three key components.

1. Infrastructure

User datasets are typically unstructured and require processing into different formats before you can derive insights. The infrastructure section of a data ecosystem collects info from various data sources and pipes it into a data analytics platform.

You can collect data via SDKs or APIs, but doing so can be risky as you have many failure points. Another method is to use a customer data platform (CDP). Using a CDP for your infrastructure means there’s a single touchpoint with your system instead of several, as you would have with multiple API requests.

Our CDP, Amplitude Activation, is tightly integrated with Amplitude Analytics, so you can seamlessly gather and analyze high-quality data in your ecosystem.

2. Analytics

This section of a data ecosystem involves using an analytics platform to generate insights. An analytics platform democratizes your organization’s data so different teams can explore it—regardless of whether or not they’re comfortable writing SQL queries.

You can use your analytics tools to track different metrics and uncover how users behave. For example:

• Historical count analysis identifies the number of times a user performed a specific action.
• Pathfinder analysis looks at the route users take to conversion.
• Churn or retention analysis investigates how and why users churn.

When selecting your data ecosystem’s analytics platform, ensure that it’s easy to build on and integrate with so you can connect it to other tools in your system. For example, Amplitude can connect to any user data system.

3. Activation

In the activation part of the data ecosystem, you act on your insights to create more meaningful customer experiences and drive revenue.

The ecosystem connects analytics platforms to other tools, like marketing, customer service, or A/B testing solutions. Different teams can then activate customer insights. For example:

• Marketing teams: Segment customers to run targeted marketing initiatives.
• Product teams: Leverage user churn data to try out product changes to improve retention.
• Customer service teams: Use automation to update user information on support tickets in real time so they can prioritize the most urgent cases.

Who benefits from data ecosystems?

In an EY survey, business leaders reported that data ecosystems contribute an average of 13.7% of total revenues. Ecosystems also reduce operational costs because they help teams work more efficiently. The fact that ecosystems drive growth and increase revenue benefits all stakeholders of an organization.

A well-designed ecosystem accessible to different teams helps employees because they no longer have to defer to data scientists to get user information. For example, engineers, salespeople, and marketers all benefit from an effective data ecosystem by gaining easier access to data, which helps with decision-making.

Ecosystems also benefit customers. Segmenting users enables you to provide a more integrated and personalized experience. Many customers now expect personalization. Tailored experiences also help to keep users engaged with your product or service.

Ecosystems help reduce risk, which benefits internal stakeholders and customers. Organizations can easily see what’s happening and track patterns, getting alerts when something breaks or issues arise. For example, banks and financial organizations can pool data to track unusual behavior and identify fraudulent transactions and accounts.

Key differences between a data ecosystem and data platform

A data platform is software for ingesting, analyzing, and exporting data. It’s only one part of an ecosystem. In a data ecosystem, the data platform takes care of the analytics in a data ecosystem. It receives data from your infrastructure and allows you to track users, analyze data, and draw meaningful insights. The platform then exports data to other tools so that you can activate the insights you gather.

A platform can do a lot to gather user insights, but requires stitching with other tools to be fully effective. A data ecosystem, in contrast, covers the entire customer journey. The ecosystem encompasses all the different systems and platforms you use to gather, analyze, and activate data.

How to create and implement an enterprise data ecosystem

A solid data ecosystem plan starts with defining the long-term vision for your company. Shape your ecosystem’s development to align with your vision to ensure the tools and processes you implement will support your organization in the long run.

The way you design your ecosystem will also depend on your goals. Every ecosystem exists to solve a business problem or add value, so define your objectives and design accordingly. Data ecosystems are typically used as part of a growth strategy, for example, to:

• Increase user engagement.
• Improve conversion rates.
• Support non-core customer use cases.

Data governance is another essential factor you should consider from the start. Security is a big concern when handling lots of user data and potentially sharing it with third-party organizations. Collaborate with your legal team and choose between:

• A closed ecosystem where data doesn’t leave your organization
• An ecosystem where you share data with strategic partners
• An open ecosystem where data is available to the public

Carefully decide who you’ll be sharing data with and where you’ll be storing it. Ensure you comply with GDPR cybersecurity laws and communicate clear data-sharing rules for your partners.

To avoid disrupting operations, start your ecosystem small and scale up. Build on the technology you already use by linking your organization’s different tools. Fill in the gaps with extra technologies so that tools can pass data up and down the entire length of the system.

Your ecosystem should evolve, so regularly review and analyze how it performs. When you need to extend and scale your ecosystem, add new tools and technologies—for example, more process automation. Revise your ecosystem by consolidating and streamlining the existing structures so it operates more efficiently.

A thriving data ecosystem gives team members agency to pull data insights and make an impact. When people understand data, they drive growth. If each department can optimize its operations with data-backed decisions instead of acting based on assumptions or intuition, the whole organization succeeds.

Consider prioritizing self-service. Giving your teams direct access to your analytics platforms eliminates the bottleneck of sending requests to data analysts and empowers quicker and better decision making.

But giving access isn’t enough. Provide training and support so team members can improve their data literacy and get the most out of the ecosystem.

Incorporate Amplitude into your data ecosystem

A strong data ecosystem is critical to your organizational success, enabling you to understand the customer journey across all touchpoints and empowering you to act. And Amplitude can serve as the corner of your data ecosystem.

Amplitude Activation is the only customer data platform natively integrated with analytics. Activation:

• Unifies customer data across your entire data ecosystem and delivers the right insights to grow your business.
• Delivers trusted, governed data between your customer data platform and analytics to unlock and build customer lifetime value.
• Reduces costs and drives business efficiency with single system implementation and management for customer data.

Additionally, Amplitude’s Data Connections provide an adaptable way to bring customer data into Amplitude from anywhere in your digital ecosystem. So stop creating the spaghetti mess of point-to-point integrations and complicated data pipelines, and start using Amplitude to streamline your data ecosystem with configurable and no-code integrations.