Seeing The Forest For The Trees: The Taming Of Big Data

0aSanjay Sidhwani SynchronyThe quandary of big data in recent years is similar to looking at a rainforest. There is so much of it, it is not an issue of seeing where and what it is, it is the fear of not seeing the forest for the trees. A rainforest has so many important ecosystems and tiny elements that may be hugely important, similar to big data. Many businesses have the challenge of seeing the thousands of types of data and identifying which elements of the data are important, and what to do about those elements.

At Synchrony Financial, we are a consumer finance company with a deep heritage in the retail sector. As such, we have a very large quantity of data, from several sources, which could include SKU data on purchase transactions, marketing touch points, channel interactions, payment history, etc. Our data is not only credit card data normally gathered from an issuer perspective, it is also data we gather to provide value to a retailer. As such, our data tools must be top notch — both scalable and flexible — in order to provide greater insights.

And with the accumulation of data comes the responsibility of safeguarding the storage, access and transfer of data, and ensuring the proper usage of key data elements. The security and protection of private customer data also needs to be a top priority.


In our experience, one strategy that is very helpful in identifying the important elements of the data available is data visualization. Data visualization tools can be crucial in identifying important factors, trends and outliers in data. After these important factors are uncovered, the question becomes how to create programs that address the important items that can positively impact a business. This can be done with agile methodology. We have found that programs using agile methodology (created using the partnership of IT and Analytics), can have a large impact on business success, as described in more detail below.

Data Visualization – Translating Data Into Actionable Insights

Data visualization can be a powerful tool to quickly observe trends and take action on the data observed. These tools make it easier for leaders across all disciplines to access key data without having to dig through thousands of data points and charts. It is more helpful to let the data tell a story through visual formats. These can include heat maps, infographics and a combination of pictures and graphs. Four types of tools are especially helpful:

  1. Executive dashboards. By translating data into a visual format, dashboards help users more clearly identify business insights, trends and performance gaps, and to more easily share the results across the company. Once the dashboards are created, business leaders and analysts know what to look for and can easily interpret the data presented.

  1. Pictures and graphs. Using pictures and graphs to portray data can sometimes be the differentiating factor in observing an insight that could otherwise go unnoticed. Paying attention to outliers and unique patterns can help highlight potential opportunities and areas of improvement.

  1. Sensitivity modeling. Data visualization software can be used as an interactive tool for running sensitivity models on a particular variable. For instance, the impact of price changes on profitability can be assessed, or the impact of weather changes on sales. Once these models are put into place, the risk of uncertainty can be reduced.  

  1. Heat maps. Another example of an effective way to display data is heat mapping. Individual values are represented in a tabular or graphical format in various colors to denote a range of performance from low to high. This visual representation allows users to hone in on where performance is strong, and where opportunities exist.

Data visualization tools are valuable to help organizations simplify large amounts of information into insights through a visual format. Letting the numbers tell the story often results in bringing insights to life and communicating them across the organization. And now that they see the data and understand its implications, the organization can impact change by using the agile process, as described below.

The Agile Process – Using The Partnership Of IT And Analytics To Impact Change

Creating a partnership between the Analytics and IT teams is extremely important. Working together with a common vision and goal, the two departments can use agile process to effectively produce workable solutions quickly and efficiently. By simplifying and speeding up the process of analyzing big data, companies are able to improve their marketing efforts and build better customer relationships.  

Let’s take a look at the traditional data model. When a customer engages with a business, whether to make a purchase, pay a bill, or make an inquiry, the interaction and the resulting data are recorded in one of its operational systems.

Traditionally, analytics processes have been separated from operational systems, because these processes demand considerable resources that can slow down the system and impact business. Consequently, businesses move data to a data warehouse platform so analysts can study the information without impacting the operational system. These commercial tools can be difficult to use and result in long cycle times.

The agile approach can solve these issues. With an agile process, the IT and Analytics teams can work together toward a common business goal from the start. The analytics team works with IT to develop insights from big data and then use the data in a timely manner — yielding improved customer personalization and more impactful marketing programs. 

The agile process also allows for:


  1. Minimization of data movement. The goal of the process is to engage the customer at the moment of decision. To react with that kind of speed, you need a platform that minimizes the number of times you move the data. A data lake provides a scalable platform where data is ingested from the operational system very quickly, without moving to the analytics environment.


  1. Availability of the tools. Open source tools are simpler and more affordable. Analysts run the data in real time and leverage tools in parallel to perform analysis.


  1. Shorter cycle times. Performing analytics at scale requires a platform that is integrated with customer channels. This moves analytics closer to the customer, resulting in shorter cycle times and greater meaningful engagement.


Once an agile infrastructure is in place, there are essential steps for helping to harness the power of that data. First, make the implication of the data clear — not just to the analysts, but also to key stakeholders. A data platform can be used for both “push” and “pull” reporting on key business metrics so performance of your business can be tracked.


Data in today’s world is ubiquitous. Some is clear and definable — like a specific tree in a forest. Others are more unstructured and free flowing — the ecosystem and co-relationships, for instance. In order to interpret the data and have an impact, data visualization can be used to see specific issues or trends, and the agile process can be used to provide the solutions and immediacy required to provide the solutions.


Please see our white papers on both topics:


Data Visualization: translating data into actionable insights for retailers; and Taming Big Data: using the partnership of IT and Marketing Analytics to maximize marketing impact



Sanjay Sidhwani is Senior Vice President of Marketing Analytics for Synchrony Financial, responsible for creating the overall data/analytics strategy, developing data platforms and generating actionable insights to drive profitable growth for all sales platforms across the business. Prior to assuming the analytics leader role in 2011, Sanjay was the targeting and segmentation leader for GE Capital Retail Finance. In this role, he was responsible for developing enterprise-wide advanced analytics tools and targeting strategies for all retail card clients. Previously, Sanjay was the analytics leader for several specialty retail clients. Prior to joining GE in 2007, Sanjay held multiple analytics and consulting roles at Merkle Inc., Ernst & Young and FedEx Corporation.

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