Three best practices for successful big data projects
Many people have asked me why only 27% of respondents in a recent consulting report believed their Big Data projects were successful.
I don’t know the particulars of the projects in the report, but I can comment on the key attributes of successful Big Data projects that I’ve seen.
Let’s look at an example. Intel recently published a case study about an entirely new Big Data analytics engine that Caesars Entertainment built on top of Cloudera Hadoop and a cluster of Xeon E5 servers. This analytics engine was intended to support new marketing campaigns targeted at customers with interests beyond traditional gaming, including entertainment, dining and online social gaming. The results of this project have been spectacular, increasing Caesars’ return on marketing programs and dramatically reducing the time to respond to important customer events.
Three ways that Caesars Entertainment got it right:
1. Pick a good use case
Caesars chose to improve the segmentation and targeting of specific marketing offers. This is a great use case because it is a specific, well-defined problem that the Caesars analytics team already understands well. It has the additional benefit that new unstructured and semi-structured data sources were available that could not be included in the previous generation of analysis.
Rizwan Patel, IT director, commented, “When it comes to implementation, it is … essential to select use cases that solve real business problems. That way, you have the backing of the company to do what it takes to make sure the use case is successful.”
2. Prioritize what data you include in your analysis
“We have a cross-functional team…that meets quarterly to prioritize and select use cases for implementation.”
This applies to both data and analytics. There is a common misconception that a data lake is like an ocean: Every possible source of data should flow into it. My recommendation is to think of a data lake as a single pool where you can easily access all the data that is relevant to your projects. It takes a lot of effort to import, clean and organize each data source. Start with data you already understand. Then layer in one or two additional sources, such as web clickstream data or call center text, to enrich your analysis.
3. Measure your results
“The original segments were not generating enough return on customer offers.”
It’s hard to declare a project a success if it has no measurable outcome. This is particularly important for Big Data projects because there is often an unrealistic expectation that valuable insights will magically bubble to the surface of the data lake. When this doesn’t happen, the project may be judged a failure, even when it has delivered real improvements on a meaningful metric. Be sure to define key metrics in advance and measure them before and after the project.
Your organization’s best odds
Big Data changes the game for data-driven businesses by removing obstacles to analyzing large amounts of data, different types of unstructured and semi-structured data, and data that requires rapid turnaround on results.
Give your organization the best odds possible for a successful Big Data project by following Caesars Entertainment’s good example.