I have been in the tech industry for a long time. Since before Taylor Swift was born, even. Over the years, many innovations have struck me as interesting ideas that would never gain any traction, simply because there was no data infrastructure to support them, or no way to get real business leverage from “innovation.” (Remember smart appliances?)

IoTUntil recently, I would say that the Internet of Things (IoT) is only in its infancy from a business-promise perspective. Billions of things are becoming Internet enabled; Gartner estimates 4.9 billion in 2015, and 25 billion by 2020. This is intrinsically cool, and marketers are super-excited about the potential of IoT. But how can anyone capture and integrate a never-ending avalanche of IoT data into a rational enterprise view, where things such as purchasing velocity and share of wallet can be instantly improved? Net-net, how does a business actually do something with this plethora of interaction data?

The power of analytic ready, networked data

A “networked” form of analytics is the answer. It is a process whereby data is collected from distinct databases, both structured and unstructured, and used to create a new analytic-ready data tier. This new data tier enables the massive events associated with unstructured/IoT data to be instantly combined with more structured data that might come from eCommerce systems, customer profiling systems, ECR data, or other traditional business systems.

Networked data and business intelligence are fundamental to this effort because they can simplify and automate the recurring need to align and “operationalize” IoT data, so that it can be used for rapid and pragmatic marketing decision making.

It’s easy to integrate IoT data into the enterprise

Advance, enterprise “2-Tier” BI platforms plug into centrally managed data sources – data warehouses, data lakes, business applications (ERP, CRM, SCM, ECR aggregators), and big data – and seamlessly unify them with data generated by decentralized teams throughout the organization. The data is then automatically refined and prepared for analysis by overlaying a consistent set of business rules and definitions (what we refer to as the semantic or governance layer), creating a single view for business users.

All of this can be done repetitively through automation, so that as new data is added or changed, there is no need to change the underlying view or alignment of that data for analytics use.

Data silos no longer matter

These steps are incredibly powerful. With them, you can easily combine data from all manner of sources easily and flexibly – integrating what was previously siloed data. You no longer have to hardwire connections between data sources because you can now aggregate the data virtually and automatically prepare it for consumption by marketers and other business users.

Great, so now you’ve got all that IoT data blended into the enterprise. In my next blog, I’ll talk more about how marketers can use it to engage customers in incredibly personalized ways.

To learn more about data virtualization, download the white paper “A Platform for 2-Tier BI and Analytics.” And follow Birst on Twitter @Birst. Thanks.