November 05, 2015
Google cloud dataflow for retailers
By: David Hoff
Cloud Data Flow

How making sense of data with Cloud Dataflow can help retailers operate more effectively?

One of the biggest impetuses for retailers today is to transition to a true digital business. But in doing so, many retailers find themselves overwhelmed with the data at their disposal. And who can blame them? In the digital age, data is now coming at us from a multitude of locations and in a multitude of formats. For example, we now have detailed information on customer activities like online ad views and clicks, mobile apps usage and browsing patterns, and this data can come from devices connected to the Internet of Things (IoT), CRM or ERP systems, etc. As a result, the issue is no longer capturing relevant data, but rather how to effectively make sense of and use this data.


So if you’re a retailer, what do you need to narrow in on? Answering these three questions is a good place to start:

  1. How can we effectively use historical sales trends to predict future behavior?

  2. How can we gather real-time feedback around new product or service launches?

  3. What do our customers even think of our products and, more importantly, our brand?

Of course, with all the big data that’s out there, finding the right information to drive business efforts often seems like finding a needle in a haystack. That’s where technology comes into play.


Relying on cloud technology to tackle these data issues just makes sense. To build your own high-capacity, highly scalable and resilient infrastructure would be prohibitively expensive to implement, let alone maintain. But that’s not the case for cloud providers like Google, who has a number of technologies as part of its Google Cloud Platform that help address many of these issues. Google Cloud Dataflow, in particular, combines several of these technologies to allow you to run many different types of analyses on your big data.

What is Cloud Dataflow?
Dataflow is a fully managed service that handles all of the back-end infrastructure needs such as storage, computing and processing. It pulls together different Google Cloud Platform resources, such as Compute Engine to execute code, Cloud Storage to store and recall data and BigQuery to process data query requests, in order to accomplish these data analysis activities. All of these resources are spun up or down in real-time based on demand.

How Does Cloud Dataflow Work?
From a usage standpoint, Dataflow provides a library of Development Kits (SDK) that allow you to write your data processing tasks without having to think about which service is handling particular tasks. This functionality lets you focus on your data analytics without having to manage any infrastructure tasks. Another unique feature of Dataflow is its ability to process both batch and streaming data using the same code base. This ability to process streaming data adds a whole new dimension for retailers and data analytics by allowing you to analyze sales or customer data in real-time as it enters the system.

It’s also important to note that Dataflow has a set of integration points that many third party vendors are tapping into in order to take advantage of its data analyzing capabilities in their solutions. Salesforce is one such vendor that has integrated with Dataflow to help its users better analyze their customer data, for example to measure sales and marketing effectiveness. 


What does all of this mean for retailers who want to use data to improve their processes and sales? Here are two of the top use cases I’ve seen in the industry:

Compare Real-Time & Historical Data to Forecast Demand
One way retailers can use Dataflow is to analyze data anomalies. For example, with Dataflow you can look at historical sales data (batch data) and compare it against real-time sales data (streaming data) to look for differences between the two. This comparison allows you to better forecast future sales by analyzing information as it comes in and then proactively managing your inventory pipeline to meet the demands as they happen.

Track Customer Sentiment to Determine Marketing Effectiveness
As retailers start to experiment more with beacons, mobile device analysis, etc., the amount of data to be analyzed is ever increasing, and Dataflow’s ability to stream this data and analyze it in real-time becomes critical. For instance, when you launch a new product, this streaming analysis allows you to tap into social media outlets like Twitter to analyze customer sentiment around both the product and your brand. This analysis is key to understanding the effectiveness of your marketing messaging in real-time, which in turn can allow you to adjust your messaging as necessary and engage with customers in a more targeted manner.


Whether it’s historical sales data, real-time sales data, marketing data, beacons, mobile devices, etc., the amount of data available to retailers today is staggering. But this data alone is not of any value; it’s how you look at and use the data that matters. That’s where Dataflow comes in by allowing you to very easily leverage your big data in a number of ways to make more effective decisions without having to worry about building out or maintaining an infrastructure to match those analytics needs.

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