Conversations about analytics and data processing have shifted notably in my career. Even 15 years ago, enterprise data warehouse discussions focused on processing speed and performance. Today, the bottlenecks have moved.

Performance is still important, yes, but no longer the biggest impediment to success. Now the challenges are more about data access, movement, security and governance as enterprises struggle to get the right data to the right people in the right form -- and in time to make a measurable difference. With the proliferation of data and analytics everywhere, this is no small feat.

That’s why now, instead of “speeds and feeds,” I recommend that organizations focus on Time to Analytics (TTA). This is an emerging metric that’s more helpful to track in today’s agile business environment. Time to Analytics measures the time between when an enterprise gets data to when the right stakeholder has access to that data for analysis – both initially and ongoing.

Many enterprises today have TTAs for analytic workloads that are often measured in quarters or years. That's not remotely fast enough in today's competitive environment. And unfortunately, TTA is getting worse as big data introduces new technical complexities. How do you take big data from many sources, move it to the right place and get it to the right people, in a way that works with their preferred tools?

Long TTAs make everyone unhappy. Business and product teams are frustrated; they can’t get the data they need, or they get it too late to make an impact. Lack of resources stymies IT, as do the scarcity of big data skills and traditional waterfall development cycles. New data technologies (Cloud, Hadoop, Spark, etc.) exacerbate the issues by introducing new technical and integration complexities.

Solving these problems and speeding up this end-to-end data flow will have a big impact, which is why organizations must focus on TTA. Metrics like ROI are helpful, but notoriously hard to calculate and measure. In contrast, TTA is much more straightforward. Again, TTA is simply the span between when an organization gets data to when the right person can use it … and of course, you can define this more specifically to match your organization’s terminology, systems and processes.

TTA gives data management professionals a much more holistic measure of success and is a helpful metric for getting necessary funding for data processing tools that might otherwise sound like boring plumbing. Rather than defend technology choices, data professionals can quantify the potential acceleration in TTA and let the business decide whether it’s worth it (and they usually say yes.) When analysts and business professionals have timely access to all the data they need, the conversation becomes much more strategic.

Reducing TTA helps teams move with more agility, improve decision-making and iterate faster through data and ideas that may deliver a competitive edge. Focusing on TTA can be transformational.

Here are a few recommendations on using TTA to drive more agility in your projects:

Benchmark your organization’s current Time to Analytics.

Look at last year’s, or even last quarter’s, data projects and evaluate them from a TTA perspective. How long did it take to incorporate new data sets, build new infrastructure and deliver data to the right people and processes? What is the average cycle time now for that data type, from generation to availability for analysis?

Focus on cutting your Time to Analytics in half every year and consider the impact.

Strategize with business and IT leaders. How would decision-making or processes change with faster TTA? Where does slow TTA impede business progress the most? Imagine if TTA could follow Moore's law style of scaling, and data was instantly available to business people. How would data conversations change?

Evaluate changes to processes and systems.

Many enterprises still use waterfall-style management for data projects, often due to the complexity of the technology. Consider tools and services that will help you get more agile with data – think cloud, “as a service” options, new data platforms and more automation. What will truly impact TTA?

The emphasis on Time to Analytics will be a shift for many organizations. But the impact makes it worth it. Business people will feel the difference when they can get access to data two times faster. By improving TTA, you’ll be able to process new datasets faster, get more comprehensive analytics and, eventually, fundamentally change the nature of your organization to be truly data-driven.

(About the author: Prat Moghe is the founder of Cazena, cazena.com, a leader in the enterprise Big Data as a Service market recently named a Gartner Cool Vendor in DBMS, 2016. A successful big data entrepreneur, he has more than 18 years of experience inventing next-generation data technology products and building strong teams in the technology sector.)

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