Here is the latest article in an eWEEK feature series called IT Science, in which we look at what actually happens at the intersection of new-gen IT and legacy systems.
Unless it’s brand new and right off various assembly lines, servers, storage and networking inside every IT system can be considered “legacy.” This is because the iteration of both hardware and software products is speeding up all the time. It’s not unusual for an app-maker, for example, to update and/or patch for security purposes an application a few times a month, or even a week. Some apps are updated daily! Hardware moves a little slower, but manufacturing cycles are also speeding up.
These articles describe new-gen industry solutions. The idea is to look at real-world examples of how new-gen IT products and services are making a difference in production each day. Most of them are success stories, but there will also be others about projects that blew up. We’ll have IT integrators, system consultants, analysts and other experts helping us with these as needed.
Today’s Topic: Needing more speed in getting analytics answers
Name the problem to be solved:
The marketing and analytics teams at Samsung had access to a wealth of dashboards and market reports, but digging even one level deeper into their complex customer data could take weeks to answer a single question. When the team needed to understand upgrade preferences across demographics, device profiles, carrier loyalty and more to support an upcoming product launch, they needed better answers, fast.
The questions they needed to answer were critical to inform marketing campaigns, messaging strategy and sales forecasting. Which customers are more likely to upgrade to a new device? Why? What factors influence an upgrade decision? How can we better target customers for a successful launch?
Not only did Samsung need to navigate these questions, they needed to do so quickly. Samsung Mobile traditionally plans two major launch events a year, and the fall launch was rapidly approaching. The Mobile and Internet marketing team wanted to know where to invest in customers, campaigns and programs to maximize the launch. Unfortunately, traditional business intelligence tools couldn’t keep up with the volume and complexity of Samsung’s data. Looking at these questions required investigating hundreds of variables, including customer demographics, location, device preferences and past interactions with other Samsung products. There was no way the team could reliably check every possible factor in the data.
Describe the strategy that went into finding the solution:
To find answers fast, the analysts at Samsung realized they needed a speedier data analytics platform. Prior to this launch, the Samsung team used a standard set of BI and dashboarding tools to query and visualize macro trends in sales, unit price, discounting and campaign performance. Their advanced analytics team was also using advanced data science tools to explore the structured data in the warehouse.
Unfortunately, these tools weren’t designed for the scale and velocity of the data flowing into the system from Samsung’s tens of millions of active devices. This led Samsung to seek out a new way to augment their team’s ability to quickly explore very wide data and test more hypothesis in an iterative way. This search led them to Sisu, a new diagnostic analytics platform designed to rapidly assess changes in KPIs–such as conversion rates and global sales data.
List the key components in the solution:
Samsung quickly realized that Sisu’s speed and ease of use made it possible for any vertical to get insights quickly. Millions of new records would be created on a daily basis as Samsung products were created, delivered, and sold. But the business value of the data never materialized.
Describe how the deployment went, perhaps how long it took, and if it came off as planned:
Samsung was able to garner recommendations to marketing leadership in hours that, in the past, would have taken weeks.
Describe the result, new efficiencies gained, and what was learned from the project:
As a result, the team changed the entire trajectory of the launch campaigns. To find answers fast, the analysts at Samsung turned to Sisu. They quickly found actionable faces in the data and were able to get recommendations by their marketing leadership in hours that normally would have taken weeks. It changed the entire trajectory of their launch campaigns.
After this initial investigation, Samsung quickly realized that Sisu’s speed and ease of use made it possible for any vertical to get insights quickly. Millions of new records would be created on a daily basis as Samsung products were created, delivered, and sold. But the business value of the data never materialized.
Today, Sisu is deployed globally at Samsung, serving critical insights to every part of the business. They’re continuously tracking changes in key metrics like customer upsells, retail sales and campaign performance.
Describe ROI, carbon footprint savings, and staff time savings:
Alongside its rich dashboards, Sisu provides the answers to Samsung’s most important questions on a daily basis. Even better, its analytics workflows are far more collaborative. Ad hoc questions are handled in real-time as analysts and their business partners use Sisu to test preliminary hypotheses and drill into the data together. It’s saving their team hundreds of hours of time every month and they are able to answer 10 times more questions than before.
The official case study can be found here.
If you have a suggestion for an eWEEK IT Science article, email cpreimesberger@eweek.com.
