“People often ask me where to start. I always say [to] look for something that’s critical to your business and optimize it. Don’t go off and start some brand new, glorified project,” said Dell’s Rogers. “Set goals, milestones and metrics to measure success along the way.”

While it’s important to have a plan in place that includes goals, strategies, tactics and a timeline, the definition of success tends to shift over time. Rather than embarking on large, expensive projects that take years to complete, organizations are phasing deployments to deliver stages of capabilities at a certain cost and in a certain timeframe, adopting agile methods that allow for experimentation, validation, and refinement—or both.

“Saying you’re going to drive value out of your Big Data initiative the first year may or may not be reasonable given your organization and focus,” said KPMB’s Gusher. “It may be a three- to five-year road map to value. It depends on the organization and what it’s trying to achieve.”

Prototyping can help organizations avoid time and cost overruns that may otherwise be caused by expensive, long-term endeavors that fail to meet the needs of the end customers.

“The biggest roadblock to companies getting value out of data and analytics is the inability to identify the decisions that are most critical to a company’s success,” said Ernst & Young’s Johnson. “A lot of people say ‘I don’t want to talk about advanced analytics because I’ve got to get my data straight first.’ Three years later, they’re still trying to perfect the data warehouse.”

In short, companies need to be able to do more intelligent things with their data. While having the right technology in place helps, it doesn’t guarantee that a company’s data strategy will be successful. Ultimately, though, reporting and analytics exist to improve business performance, whether that’s increasing customer satisfaction or reducing the number of equipment failures.

Although solving departmental or business unit challenges can be difficult, enterprise undertakings are more complex and are not necessary in all circumstances. Working backward from desired business outcomes is the most straightforward way to determine the data, reporting and analytics that are necessary, and at the same time, companies need to be nimble enough in their approaches to manage change effectively as circumstances, market requirements, customer demands, and business objectives shift.