What is a data ecosystem

Why create a data ecosystem?

Data ecosystems are for capturing data to produce useful insights. As customers use products–especially digital ones–they leave data trails. Companies can create a data ecosystem to capture and analyze data trails so product teams can determine what their users like, don’t like, and respond well to. Product teams can use insights to tweak features to improve the product. Ecosystems were originally referred to as information technology environments. They were designed to be relatively centralized and static. The birth of the web and cloud services has changed that. Now, data is captured and used throughout organizations and IT professionals have less central control. The infrastructure they use to collect data must now constantly adapt and change. Hence, the term data ecosystem: They are data environments that are designed to evolve. There is no one ‘data ecosystem’ solution. Every business creates its own ecosystem, sometimes referred to as a technology stack, and fills it with a patchwork of hardware and software to collect, store, analyze, and act upon the data. The best data ecosystems are built around a product analytics platform that ties the ecosystem together. Analytics platforms help teams integrate multiple data sources, provide machine learning tools to automate the process of conducting analysis, and track user cohorts so teams can calculate performance metrics. Here are a few common applications for analytics platforms:

• Increasing user engagement
• Using machine learning to identify hidden relationships in the data
• Sending alerts to notify teams of changes
• Messaging users directly
• Analyzing individual users or cohorts
• Tracking conversions and marketing funnels
• Increasing user retention
• A/B testing feature changes
• Integrating with other applications in the data ecosystem

How to create a data ecosystem

There are three elements to every data ecosystem:

Infrastructure

If a data ecosystem is a house, the infrastructure is the foundation. It’s the hardware and software services that capture, collect, and organize data. The infrastructure includes servers for storage, search languages like SQL, and hosting platforms. Infrastructure can be used to capture and store three types of data: structured, unstructured, and multi-structured. Like the name implies, structured data is clean, labeled, and organized, such as a website’s total number of site visits exported into an Excel spreadsheet. Unstructured is data that hasn’t been organized for analysis, for example, text from articles. Multi-structured data is data that’s being delivered from different sources in a variety of formats–it could be a combination of both structured and unstructured. If ecosystems hold a large volume of data, they’ll need additional tools to make it easier for teams to access it. Teams may use technologies like Hadoop or Not Only SQL (NoSQL) to segment their data and allow for faster queries.

Analytics

Analytics serve as the front door through which teams access their data ecosystem house. Analytics platforms search and summarize the data stored within the infrastructure and tie pieces of the infrastructure together so all data is available in one place. While infrastructure systems provide their own basic analytics, these tools are rarely sufficient. A dedicated analytics platform will always be able to dig much deeper into the data, offer a far more intuitive interface, and include a suite of tools purpose-built to help teams make calculations more quickly. For example, while an application server might inform a team how much data their application processes, an analytics platform can help identify all the individual users within that data, track what each are currently doing, and anticipate their next actions. Only analytics can segment users and measure them with marketing funnels, identify the traits of ideal buyers, or automatically send in-app messages to users who are at-risk for churn.

Applications

Applications are the walls and roof to the data ecosystem house–they’re services and systems that act upon the data and make it usable. For example, a product team might decide to port its analytics data into its marketing, sales, and operations platforms. This would allow the marketing team to score leads based on activity, the sales team to get alerts when ideal prospects engage, and operations teams to automatically charge customers based on product usage.

Things to consider when creating a data ecosystem

Data governance

In an age where IT no longer has clear, central data oversight, companies need to establish clear data governance rules, usually by publishing an internal guideline for how data can be captured, used, stored, safeguarded, and disposed of. Legislation like the European Union’s GDPR is forcing many product teams to be more transparent, but those that want to build trust with their users should get ahead of the trend. Every organization should publish and adhere to its own data governance guidelines.

Democratize data science

Most teams can benefit from customer information, but if there’s only one person who can access the data, that person will become a bottleneck. Many companies invest in analytics platforms that offer intuitive interfaces and allow anyone throughout the company to access data. DocuSign, for example, deployed Mixpanel and handed out licenses to over one hundred users across the company. “We’re building a data ecosystem now, gradually adding more data that we want people to have easier access to,” said DocuSign Senior Product Manager Drew Ashlock. With the increase in data access, DocuSign made changes that resulted in a 15 percent increase in new customer account creation.