As a business community, it’s time to admit we have a problem. From transactional data and customer service calls to city traffic patterns, GPS, robotic sensors, emails, texts and social media -- at every given moment, we’re capturing as much data as we possibly can, without ever really knowing why.

We’ve begun amassing data “just in case,” creating huge, ever-growing challenges for the IT teams responsible for storing and securing all of it, and the analysts responsible for making decisions with it. Then we spend countless hours sifting through it to find answers to questions we haven’t clearly identified. We’ve become data hoarders. So how do we move past the hype of “big” data and face the larger challenge of tapping into the right data – big or small?

First, it’s important to recognize that data doesn’t create meaning. People do. And for that reason, it’s imperative that you start with a question that has meaning to you and to your business.

To illustrate how starting with a question can have tremendous impact, let’s go back to June 2000 for a moment. Before we’d ever heard the term “big data,” President Bill Clinton and Prime Minister Tony Blair stood side by side at the White House to announce the sequencing of the human genome. Today, less than 16 years later, genomics is changing the practice of medicine.

How did this happen so quickly?

In 2002, Dr. Jeffrey Trent established the Translational Genomics Research Institute (TGen), hoping to use genomic data to better understand diseases like neuroblastoma. That same year, TGen developed technology that could sequence and analyze specific markers in a human genome.

By 2009, TGen built a 1.2 teraflop, 16-node High Performance Computing cluster that processed data 12 times faster than previous systems. In 2012, TGen collaborated with clinicians at the Neuroblastoma and Medulloblastoma Translational Research Consortium (NMTRC) to identify treatments specific to each child fighting this rare disease.

By 2014, the organization was able to cut biopsy to treatment time down from one month to 21 days. By 2015, biopsy to treatment time was only a few days. Through these efforts, these organizations have been able to stop the progression of cancer in 60 percent of patients previously deemed incurable. That’s incredible.

And it all started with a question.

The question came to Dr. Giselle Sholler, chair of the NMTRC and director of the Pediatric Oncology Research Program at Helen DeVos Children’s Hospital, when she was treating her first patient, a 5-year-old boy named Tyler with critical neuroblastoma. Tyler’s 9- and 13-year-old brothers wanted to know why they couldn’t make their brother better. And it led Dr. Sholler to ask a seemingly simple question: Can we rationally understand and treat each of our patients in a way that’s specific to them as individuals?

Throughout their data journey, TGen and NMTRC never lost sight of this central question. TGen and NMTRC weren’t hoarding data; they were gathering it for a highly specific purpose – to develop specific treatments for each patient based on their personal profile.

Unfortunately, the approach taken by TGen and NMTRC represents the exception, not the norm, especially in the healthcare space. In fact, the most recently unveiled findings from the Dell Global Technology Adoption Index show that while 48 percent of healthcare organizations believe big data is relevant, they aren’t sure how to approach it.

Most of those organizations can likely find the answers they’re looking for in the form of questions. Without a question to drive your data initiatives, it’s easy to make unwise technology investments and faulty business decisions. But with the right central question, you can leverage technology in a strategic and cost-effective way.

Organizations that are successfully using all data – not just big data – to obtain insights and answers to a central question employ a framework that incorporates all of the following concepts:

● Ties data projects to specific goals

● Defines and prioritizes use cases

● Sets measurable performance metrics

And there are a few key steps they take to develop this framework and ensure success: Identify the use case.

It’s worth repeating: Successful big data initiatives start with a question. So before doing anything else, identify the use case and goal of the initiative. That will direct you to the data you need – and importantly, away from the data you don’t – and allow you to leverage analytics to turn data into better and faster insight.

Test analytical models.

Just like formulating the right question, creating the right analytical models takes time and testing. A big part of the analytical challenge is gathering and preparing data for analysis. Before you make analytics platform choices, be sure you know the goals. And look for solutions that are open and flexible and do not require expensive, proprietary skillsets.

Get executive sponsorship for the project.

Identify change agents among department heads and leaders who can model the new data approach. These leaders must participate in coming up with the initial question to ensure it’s aligned with strategic goals.

Get users to buy in.

All team members must get better at using data to make decisions, not just validate them. More importantly, employees from across the organization must share data freely for the greatest insights to be unlocked.

Learn from Dr. Sholler, Dr. Trent, and the rest of their collaborators, and start your big data project with a question worth answering. Partner and collaborate with other experts and share data across silos. Remember, data-driven initiatives are always a journey, and they’re rarely easy, but if you start with the right question and stay focused on answering it, the opportunity for is impact is limitless.

(About the author: Joanna Schloss is a business intelligence and analytics expert at Dell Systems and Information Management)

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