5 Secrets to Successful Analytics
Good analytic projects dont come easy, but there are a few truths sometimes hiding beneath the surface. Here are five secrets to setting up a program for successful business analytics.
Hannah Smalltree compiled this refined list of expert insight from a recent TDWI conference.
Many business leaders now know that analytics can help them but arent exactly sure how or perhaps worse, have skewed expectations about what analytics can do for them. This demands a different approach to requirements gathering, according to Stephen Robinson, director of Online Analytics and BI at HomeDepot.com and long-time analytics professional. One technique hes 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. 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.
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 its critical to understand exactly what data the organization has, where it is and how its structured, with particular emphasis on unique data thats not available to competitors. Even more important is understanding exactly how that data can be used.
It turns out that the cliché of a picture being worth a thousand words is true even more so if that picture is interactive. Jennifer Lim, research scientist at Sprint Nextel, recommends a plan that starts with a manageable data set and delivers visual POCs, either a mockup or an interactive version using an off-the-shelf visualization tool. Seeing the data in action, especially when there is a geospatial or mapping element, can help business users understand what they can get from the data, Lim said.
With the explosion of interest in analytics has come new methods and tools, such as R and Hadoop. While new technologies may work well in some parts of an analytics ecosystem, they may present challenges for many analysts and business users who are more familiar with SQL, the lingua franca of analytics. Use tools that support SQL to remove any language barriers from analytics adoption.
Instead of tackling the project in the standard linear fashion progressing from requirements to project plan to technology selection collect information and considering all of the options up front. Analytics workloads are becoming more varied, with different functional requirements across BI tools, ad hoc analytics, big data analytics and data science programs. New analytics projects are increasingly delivered outside of the enterprise data warehouse, leveraging what BI pro Colin White calls the extended data warehouse. New analytic RDBMSs or technologies such as Hadoop may be a better fit for some projects and may deliver more long-term capabilities to the business. However, asking Should we get one of these Hadoop thingies? is not the right place to start, White clarified.