n today’s fast-paced economy, businesses need access to insights faster than before. While periodic reporting still has its place, organizations are looking for deeper and more timely insights that can help them make better decisions, cut costs, improve efficiencies, reduce risks and drive more revenue.

It’s pretty obvious from all the hype around the topic that many powerful things can be done with analytics, but it isn’t always obvious where one should begin. We asked some experts — they or their firms presented in the Data & Analytics Track at Interop ITX this month — where they thought businesses should start, and here’s what they had to say.

Prove value, not concepts

Some organizations spend too much time trying to get everything in a perfect state before using analytics, which wastes valuable time and overcomplicates what could be a simple beginning. The best way to start is to choose a project that has the potential to demonstrate value without requiring a lot of extra work or heavy investment.

“When we build something that returns value to the organization right away, people start buying in because they see the ROI and the other types of value it provides like efficiencies in the workforce, short time to solution or short time to value,” said Kirk Borne, principal data scientist at Booz Allen Hamilton. “It can be something simple.”

For example, a financial services company managed to save $1 billion simply by analyzing web clicks, Borne said.

“They already had web analytics in place. They just needed to pay attention to what the signals were telling them,” said Borne. “It doesn’t have to be a complicated model or involve complicated data to prove value.”

Analytics can start anywhere

Analytics can begin at any level in an organization, whether it’s an executive who wants an answer a strategic question or a line of business manager or staff member who needs to solve a tactical problem.

Five years ago, the Association of Schools and Programs of Public Health(ASPPH) assigned data analytics as a part-time job to its current director of data analytics and another employee. The association had been sending members periodic reports, but as the volume of data grew, it became obvious ASPPH could provide more value to its members with analytics.

“We started out small, providing a few dashboards to our members,” said Christine Plesys, director of data analytics at ASPPH. “They didn’t have to wait four months to receive a report.”

Now ASPPH is teaching its members about the best practices in data analytics and how to use the data ASPPH provides for strategic planning and internal benchmarking purposes. In addition, its data analytics staff has grown to four full-time employees

Get an executive sponsor

First efforts can be difficult to get off the ground if no one at the executive level understands the potential value of the project. To minimize that challenge, it’s wise to have an executive involved who will help the project succeed.

“One of the things some people miss is it’s not just a chief data officer or a data scientist you should be talking to,” said Booz Allen Hamilton’s Borne. “Sometimes it’s the chief financial officer or chief marketing offer because those people hold the purse strings on the kinds of investments that need to be made.”

Expect the unexpected

When people receive reports, they often have more questions that require a new report to be built. Analytics dashboards enable individuals to explore data in a more iterative fashion which needs to be considered when launching a first attempt.

“A lot of people think analytics is a new word for data warehousing or business intelligence and then they try to run their [analytics] project the same way,” said Karen Lopez, senior project manager and architect at data project and data management consultancy InfoAdvisors. “At a base level, you’re building a Q&A system because you don’t know [all of the questions] you’re going to ask.”

It’s also difficult to anticipate what unexpected circumstances might arise, especially when launching an analytics project without the help of an expert. For example, it may be difficult to get the necessary data from IT or from another department because they don’t want to share it.

People new to analytics also tend to overlook data quality. Poor data quality can cause spurious analytical results and perfect data quality is virtually unattainable. It’s wise to understand the tradeoffs between data quality and the time and expense it takes to get it into a state that’s “good enough” for the purposes it will serve. An expert can help you find that balance.

Bottom line

Too often, first attempts are derailed by overcomplicating the problem or attempting to solve a problem that is too complex to be solved well with existing team members and tools. The best first analytics project is one that can demonstrate value quickly and cost-effectively. If you succeed, it will be easier to make a case for follow-on projects. If you fail, you’ll learn a lot without wasting months or years and several million dollars.