Exec Tips: How to Build a Big Data Analytics Team
Showalter says that hospital analytics teams facing a huge challenge: the need to evolve skill-sets to keep up with the speed of data growth and introduction of new technologies.
“The skills your team is going to need in three years are not the skills they have today,” says Showalter. “They’re going to need to be trained.”
Owing to the fact that few people really master analytics, training will be complex. Sriram Vishwanath, professor of engineering and computer science at the University of Texas, Austin says if anyone claims they can do healthcare Big Data to take it “with a truck load of salt.”
“Healthcare big data is hard,” he says.
Many leading healthcare systems are building specialist teams for hospital analytics.
Geisinger Health Care System chief data officer Nicholas Marko, MD, listed the following necessary skills for such a team:
Modelling and simulations;
Algorithmic research and development;
Data-centric collaboration and real-time analytics.
Danyal Ibrahim, MD, chief data and analytics officer at Saint Francis Hospital and Medical Centre has observed that future analytics skills are changing swiftly.
“SQL skills are more data science, even data warehousing, architecture and design – those can live partly in the analytic team rather than pure IT,” he says.
Analytics Teams Core Competencies
Advocate Health Care focuses on three core competencies; Technical, analytical, and deployment.
What this means is on the technical front, employees who work to gather data together with tools that include Hadoop that generate appropriate data models. For analytics, the objective is to ensure leveraging of technical aspects by including input of data scientists and statisticians. For the deployment function experts with a clinical background focus on ensuring that tools and procedures the technical and analytical teams develop make it into implementation.
Danyal Ibrahim, MD, chief data and analytics officer at Saint Francis Hospital and Medical Centre built his team with the following in mind:
Break down of corporate and people silos;
Redesign the team for the future direction;
Develop a strong data platform;
Design and adopt meaningful metrics;
Harness data presentation capabilities to make it appealing and actionable.
Showalter said to avoid purchasing tools the team could not use but outsource instead.
Coordinating, managing, extracting value for the analytics team and thinking about the strategy demands one person fill a role for making ultimate decisions.
The title matters less than a leader who can speak relate to both executives and data scientists.
Engaging with clinicians is also important for such a role as “they’re the compass heading in the right direction.”
Retaining data scientist talent is challenging right now so a leader must know how to attract and employees and make them stay.
Source: Health IT Care News
Image Credit: Pixabay
Published on : Fri, 17 Jun 2016