In the digital age, the adage "knowledge is power" has evolved into "data is power." It reflects the immense value of high-quality data and a strategic approach to data management.
At the heart of any successful modern enterprise lies a robust data strategy coupled with stringent data quality standards.
For an industry titan like Georgia-Pacific – a company that has integrated its vast array of building materials, paper products and chemicals into the fabric of global infrastructure – establishing such a strategy is paramount. However, the complexity and scale of their data operations made this seem like a high mountain to climb.
In this Q&A, we chatted with Chalamayya Batchu, a Senior Enterprise Architect at Georgia-Pacific, about the significance of data quality and a global data strategy at the corporate giant. He shares Georgia-Pacific’s journey toward a unified, robust data framework and reveals the pivotal role of SAS® Viya® in standardization and automation, fostering enhanced decision-making and operational excellence.
Q: Who sets the data strategy at Georgia-Pacific?
Batchu: Data strategy is like the blueprint for building your home, whereas data quality is your home’s foundation. Both are intertwined: You need a high-level plan for how your organization processes, stores, manages and consumes data. Data quality is how well your data is prepared for the various stages of your plan.
At Georgia-Pacific, the data strategy is a collaborative effort by a cross-functional team called the “data architecture group.” We create a data strategy based on the problems, issues and changes in the business and present it to our leadership.
Q: What challenges did you face in creating a strong data strategy at Georgia-Pacific?
Batchu: Like many organizations, Georgia-Pacific has siloed data environments because we are a collection of business units with their own data teams creating data. So pulling that together into one unified approach was not an easy job: There were cultural issues, and people were connected to legacy tools and technologies.
So it took some time to develop a data strategy that would work at an organizational level – not just at the individual business unit. We started by identifying issues that are common across the businesses, asking questions such as: How do we take a common approach to the issues, frameworks and technologies? Where do we combine? Where do we merge? Where do we find cost savings?
Related: Georgia-Pacific VP on building resilience in the era of disruption
Q: What challenges prompted your search for a solution? Why choose SAS?
Batchu: When I started at Georgia-Pacific, there was no single approach or platform to develop advanced analytics. There were scattered pockets of development — some teams were using cloud tools, some were using desktop tools, and some were using open source. We didn’t have a uniform way of creating, deploying or managing models, but we wanted to transform our organization using advanced analytics.
We faced two data challenges:
- Data quality – who owns the data, who governs it, and how do we authorize users and control access?
- And the volume and variety of data. Everything is a data point. How do we manage the massive volume of data produced by the various sources (i.e., databases, APIs, IoT devices, mainframes, etc.)? How do we integrate into a unified platform and standardize data quality practices?
Many things about SAS Viya stood out: its data management and predictive modeling capabilities and the ease with which citizen data scientists can use the UI. We also loved that SAS Viya allows different types of users – those who load the data, the ones who manage the data, and those who use the data – to work together in the solution.
As a manufacturing shop with IoT devices, real-time predictive modeling was a must. So a game changer for us was SAS’ real-time event-stream processing.
Q: What value has SAS brought to your organization?
Batchu: As part of our move to a centralized approach with SAS, we instilled global standards and improved our overall data quality processes, which resulted in better decision making using better analytics and better analytical models. The value of SAS goes beyond analytics – it’s a complete package, including cost savings, process efficiencies and avoided security hazards.
Q: You mentioned that SAS helped you deploy models faster. Can you explain?
Batchu: Automation is the key. Establishing a data quality process is good, but if it's not automated, you can’t accelerate data delivery. Our data scientists and machine learning engineers had to do foundational data quality work before they could create algorithms, make predictions or introduce new use cases.
We automated most of the data quality processes with SAS Viya and refocused the data scientists and machine learning engineers on deriving value from the data. Now, before the data is sent to them, it is qualified as gold, silver or bronze. That designation gives them confidence in the amount of data prep needed before they can give the data to the model, train the model and deploy it.
Automating data quality processes with SAS Viya helps us create models much more quickly: The time to deploy a model was reduced from days to minutes.
This is no small feat
Navigating the complexities of data in a conglomerate like Georgia-Pacific is no small feat. “Everyone has data – getting insights is what takes a lot of work,” says Batchu. “That is where SAS helps us. For Georgia-Pacific, SAS is the standard for AI development and data management.”
Automation and streamlined processes have expedited model deployment from days to minutes, underscoring the profound impact of high-quality data and a cohesive data strategy. Georgia-Pacific's journey is a testament to the power of innovation and the vital role of advanced analytics in shaping the future of the industry.
