ATLANTA — Conference swag is a given for any industry event. Here at the Teradata Partners 2016 user conference, some attendees can be seen with a red baseball cap adorned with a simple phrase: “Make Data Great Again.”
It’s a cheeky and obvious reference to U.S. Republican candidate Donald Trump’s “Make America Great Again” campaign sloganeering. But in the non-partisan context of Teradata Corp.’s renewed focus as an analytics company, it highlights the notion that managing enterprise data is often far from perfect. Indeed, the organizations that are able to harness IT tools to quickly make sense of their structured and unstructured data points will be the ones better positioned for success.
The Ohio-based company has traditionally been known for data warehousing and business intelligence offerings; according to Teradata’s recently appointed president and CEO Gordon Lund in a keynote — in May of this year he replaced the ousted Mike Koehler, who had been in the position since the company spun off from NCR Corp. in 2007 — its focus on big data, the cloud, and improving the customer experience will be key for the company’s financial success.
As an IT vendor, Teradata made mistakes along the way but is now looking to course correct: “Somewhere along the way, we lost where we started…somewhere along the way we became very technology-focused. But what the technology actually does is solve problems for our customers,” said Lund.
Moving forward, said Lund, the company will be more business focused; this includes tighter integration between real-time analytics and performance-based targets to help businesses make better decisions based on the data available.
To say that Teradata customer eBay Inc. generates a lot of data would be an understatement. The company’s challenge is in managing an ecommerce platform that features more than 800 million active listings with more than 164 million active buyers.
According to David Brohm, senior manager, Teradata operations at eBay, the company uses Teradata hardware and data connectivity along with Apache Hadoop to store the millions of transactions that happen daily on the platform.
Specifically, the company is using analytics platforms in multiple locations and zones with multiple Teradata and Hadoop instances; this involves a large ETL/ingest, BI infrastructure and private cloud-type virtual machine instances for applications.
This end result, noted Brohm, is a big data architecture that leverages multiple data platforms to provide an optimal customer experience whether on the browser or mobile device.
Data integration in real-time
Also at the conference the company announced the Teradata Customer Journey Analytic Solution, a set of capabilities for discerning the behavioural paths of each individual customer, determining the next best interaction, and delivering a more consistent brand experience through channels and touch points.
With an eye on improving the user experience, the company said the framework will enable real-time customer data integration, advanced behavioural analytics and multi-channel marketing automation.
According to research firm Gartner Inc., businesses that have fully invested in all types of online personalization will outsell companies that have not by 30 per cent by 2018.
In a cross-platform world, the Teradata capabilities are intended to help organizations access a complete view of each individual customer by integrating their data regardless of where the data is generated or stored.
The Customer Journey Analytic Solution is available immediately, the company said.
Chris Twogood, vice president of product and solutions marketing at Teradata, said the company is working with several Canadian companies, including McCain Foods Ltd., Canadian Tire, Royal Bank of Canada, and Bell Canada.
“In general, we see the demand for data analytics as a global demand and supply. (Our Canadian customers) all have the same issues — data is growing and getting more complex,” said Twogood.
How these companies use sensor, text, and sentiment data sets in a way that is not just reviewing transactions but analyzing customer behaviour will be important, he said.
“Whether you are doing behavioural analytics to understand patterns, graph types of analytics to understand relationships, or machine learning — the data has grown so large that people need to analyze and scale with multiple different techniques against that integrated dataset.”
Make data great again, indeed.