Enterprises of all kinds are becoming more agile out of necessity. While the movement began with the software teams in many organizations, the rapid pace of today's business environment requires companies to become more nimble. Data-related initiatives, including analytics, are no exception.
"You're leaving value on the table if you don't have enough analytical agility in your organization," said Patrick Rice, founder and CEO of data science platform provider Lumidatum, in an interview. "A lot of people have standard reporting and dashboards in place, but we're going through the second wave of needing more agility in different places that go beyond standard reports for users."
Tool choices continue to proliferate, with the general trend being that they are easier to use. The idea is to put the power of data, analytics, and insights in the hands of more people in the organization faster without requiring PhDs to use the systems. Although, even in more technical contexts, technology layers or extensions are being added to existing systems such as Hadoop so that they too are easier to set up, deploy, and use. Of course, analytical agility isn't just about technology and solutions.
"We're chipping away at the problem. A technology tool set is one thing, but if you haven't got the right mindset, the right people with the skills to use these tools, and you haven’t adapted the way you work, you're going to continue to work the old way," said Mark Marinelli, CTO of agile analytics solution provider Lavastorm, in an interview.
That's not to say organizations should attempt to reinvent themselves overnight. Becoming agile is a process that involves ideas, testing, iterating, and improvement. Rather than spending months or years building something that no longer maps to user requirements or the needs of the business, the point is to break large projects into smaller pieces, build on successes, learn from failures, and embrace an ethos of continuous improvement.
Still, many companies continue to chase the ideal of perfection at their own expense. They're attempting to build or deploy perfect systems or achieve perfectly clean data. The hope is to enable accurate data analyses and insights that can inform precise business decisions. Such a relentless focus on perfection can cause companies to spend unnecessary time and money on efforts that do not justify the incremental value they provide, however.
"I see companies fall down on this all the time. They want perfection and forget that good is good enough. They also forget that analytics are not perfect most of the time, so trying to create a perfect solution based on an imperfect signal, model, or outcome is actually compounding the imperfections. It's wasting time, money and effort," said John Lucker, global advanced analytics & modeling market leader at consulting firm Deloitte & Touche, in an interview.
Here are a few ways you can help your company achieve better ROI and greater analytical agility.