A data ecosystem is a collection of infrastructure, analytics, and applications used to capture and analyze data. Data ecosystems provide companies with data that they rely on to understand their customers and to make better pricing, operations, and marketing decisions. The term ecosystem is used rather than ‘environment’ because, like real ecosystems, data ecosystems are intended to evolve over time.
If a data ecosystem is a house, the infrastructure is the foundation. It’s the hardware and software services that capture, collect, and organise 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 organised, such as a website’s total number of site visits exported into an Excel spreadsheet.
Unstructured is data that hasn’t been organised 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 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 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.