I’m sometimes labeled a “pterodactyl” because I’ve been a Teradata employee since the very early days.  It has been a life full of constant change and innovation. From our earliest days, we provided plenty of sparks to what has become a multibillion dollar industry of data management and business analytics.  

While it is fun looking back at all that Teradata has accomplished, it is even better looking forward at all we are poised to achieve.  We have built a solid legacy that gives us a unique ability to drive the next game-changing innovations in the analytics arena.

The Original “Big Data” Database

The initial vision of Teradata was to provide business analytics through efficient data management and parallelization of computation.  Back then, if you had a hard drive it was 5 MB and we were talking about harnessing terabytes of integrated data to drive complex business analytics and accelerate outcomes.  This was Big Data before it’s time!

By allowing customers to collect, and more importantly, integrate their enterprise data, Teradata opened up the breadth and depth of analytics.  Customers could now not only get reports but also start the iterative exploration to understand why events occurred and what actions should be taken to respond in the appropriate manner.

But as time went by and the customer base grew, it became clear that Teradata was not just another database.  Teradata was the first database geared towards business analytics at scale. Scalability, accessibility, and manageability were deeply embedded in our foundation and continued to blossom.  We showed that a larger volume of data does not have to mean more data management, and that tighter integration should not lead to more confusion trying to navigate your data.

There are numerous customer stories about how Teradata helped companies accelerate their business, and even drove change in their industries:  Retail moved to real time replenishment; airlines had full understanding of delays and their impact on passengers; banking allowed customers to quickly and easily access and analyze their accounts, just to name a few examples.

From a Database, to a Data Warehouse, to an Architecture

As Teradata customers moved their analytics from “after the fact” to “during the event” they were able to bring more timely and targeted actions to bear on their business.  This meant that the environment was not only “nice to have” but “critical to operations” and, as such, demanded the rigor of such an environment.

This is where Teradata moved from being the “database company” to a “Data Warehouse provider”.  This meant that all the processes to ingest, manage, and deploy analytics had to be included into the total solution.  From the technology side we created tools to help with the movement of data between production and DR platforms, other tools to manage multiple systems as single environments, and worked with BI tool vendors to help optimize and deploy their analytics across entire companies, not just the few back-end support personnel.

As the Teradata solutions successfully delivered on end-to-end enterprise capabilities, new analytic tools and data management solutions were hitting the market.  Most notable among them was Hadoop, which offered a way to inexpensively store massive amounts of data coming from new data sources, such as web and sensors.

Rather than try to retrofit the Teradata Database to this challenge, we simply expanded our data warehouse solutions to embrace and include Hadoop as well as the advanced analytics being developed by Aster, a company Teradata acquired to complement our traditional solutions.

Staying true to the Teradata vision of integration and ease of use, we further extended our toolsets to include massive ingest with Listener, cross platform analytics with QueryGrid, and wide scale deployment with AppCenter.  These worked together to move us beyond the database to the Unified Data Architecture, a blueprint to drive our customer’s success in an increasingly interconnected world.

From an architecture to a total analytic platform

Today presents even more options for customers to extend their analytics.  There are new data storage options, new programming languages, new open source toolsets, and perhaps most importantly, new communities of users, such as data scientists, that need access to data to drive more advanced analytics across the enterprise.

But through this flood of innovation, the critical success criteria for a production data warehouse are still the same: Integration of data and analytics and the deployment at scale to maximize business outcomes.  The sad fact is that most innovation found by data scientist through advanced data analytics rarely make it into a production world due to the inability to operationalize what was discovered.

Teradata again leans on it rich heritage and experience of unifying ecosystems and simplifying data management with the Teradata Analytics Platform.  We have evolved from the “sql database on hardware in my datacenter” to “any analytics deployed throughout a myriad of platform or cloud choices with “as-a-service” options.  By combining the Teradata Database SQL engine with other processing engines, such as machine learning, graphical processors, and soon available advanced compute engines such as spark and tensorflow, we have once again changed the game within the industry.   

Cementing this leadership, Teradata is broadening the source of data that its rich analytics can operate on.  Native access from the Teradata Database to foreign data storage systems such as S3 will soon be supported . This bold step forward means you can develop and run integrated complex analytics across a wide variety of data sets all within a single optimized environment.  More importantly, once insights that you gain prove to have value they can easily be deployed as scale with the optimization, workload management, and ease of use for which Teradata has earned well-deserved fame.

So while looking back with pride, I also look forward with great excitement.  It has been a fascinating journey so far I can’t wait to see what comes next, and how it will further enable business outcomes for our customers to create the next wave in the evolution.

Rob Armstrong

Starting with Teradata in 1987, Rob Armstrong has contributed in virtually every aspect of the data warehouse and analytical processing arenas. Rob’s work in the computer industry has been dedicated to data-driven business improvement and more effective business decisions and execution.  Roles have encompassed the design, justification, implementation and evolution of enterprise data warehouses.

In his current role, Rob continues the Teradata tradition of integrating data and enabling end-user access for true self-driven analysis and data-driven actions. Increasingly, he incorporates the world of non-traditional “big data” into the analytical process.  He also has expanded the technology environment beyond the on-premises data center to include the world of public and private clouds to create a total analytic ecosystem.

Rob earned a B.A. degree in Management Science with an emphasis in mathematics and relational theory at the University of California, San Diego. He resides and works from San Diego.

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