Hannah Smalltree, director of product marketing at ParAccel, writes on best practices to kicking off an analytic project for Information Management.
The star power of analytics has burned even brighter in the last few years, as more success stories emerge.
But while many professionals are happy to see analytics move out of the dusty back rooms to take center stage, it’s also created a new conundrum for those tasked with implementing the programs: It’s still not easy to deliver analytics capabilities, and business expectations for success are at an all-time high. On one hand, the trend has given valuable exposure and funding to some programs. On the other, the data challenges of analytics – accessing, cleansing, integrating, analyzing and acting on results – are arguably greater than ever before, thanks to big data, social media data and sophisticated new analytic requirements. This is uncharted territory for many organizations, demanding new approaches and best practices.
This dilemma inspired industry expert Wayne Eckerson’s new book “Secrets of Analytical Leaders,” which profiles seven leaders from forward-thinking analytic organizations. In it, early adopters share advice from the trenches about running a successful program. The book also inspired informal discussions with other analytics leaders at TDWI’s March 2013 Big Data Analytics Summit in Savannah, Ga. Here are the secrets they shared.
1. Understand the business decisions the new project will support.
Collecting requirements is uniquely difficult in analytics, many said. Many business leaders now know that analytics can help them but aren’t exactly sure how – or perhaps worse, have skewed expectations about what analytics can do for them. This demands a different approach to requirements gathering, explained Stephen Robinson, director of Online Analytics and BI at HomeDepot.com and long-time analytics professional. One technique he’s used throughout his career is focusing on the decisions that the business needs to make that could benefit from analytics. It harkens back to the term applied to this industry nearly 20 years ago -- decision support systems – and still rings very true today, said Robinson. Understanding the business decisions involved can directly connect requirements with a specific business process and measureable business outcome, defying the ambiguity that can plague analytics projects.
“I often ask the business lead to tell me the top six decisions they make that they’d like to support with analytics,” Robinson said. “If they can’t tell me, it’s a clue that we need to go back to the drawing board about the goals of the project.”
2. Evaluate available data, especially unique data, and policies for using it.
Many analytics projects start with the nebulous notion that there simply must be some insight buried in the mountains of data a company interacts with. But before getting too deep into the project, analytics leaders agree that it’s critical to understand exactly what data the organization has, where it is and how it’s structured, with particular emphasis on unique data that’s not available to competitors. Even more important is understanding exactly how that data can be used.
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Source: Information Management.