The Best Way to Get Started with Data Analytics
IT Data Solutions
As data analytics matures, it’s attracting a wider range of adopters. Here’s how your organization can tap into this essential business technology.
Over the past several years, enterprises of all types and sizes have transformed themselves into data-driven organizations. With the assistance of data analytics, it’s now possible to put customer and market data to work and obtain rewarding insights almost immediately.
While data analytics’ benefits are well documented, some organizations remain stubbornly on the sidelines, concerned that the cost of launching and running a data analytics project will outweigh any potential rewards. Yet as data analytics technology grows ever more powerful and accessible, such enterprises may now want to reconsider their decision.
First steps
To introduce data analytics effectively, enterprises need to develop a strategy that promotes both top-down and bottom-up initiatives, said Gonzalo Zarza, director of data and analytics for IT and software development company Globant. “On one hand, top-down or management-driven initiatives play a major role in adoption … through leading by example,” he explained. “On the other hand, a sharp selection of bottom-up projects to be executed first has proven to help break initial reluctance and skepticism arising from different teams and well-established areas in the organization.”
Begin the journey into data analytics by building a strong foundation, advised Rosaria Silipo, principal data scientist at KNIME, an open source data analytics company. “Hire key experts and choose the best software,” she suggested.
Follow up by building an inventory of existing resources and capabilities, including whatever is available in the current data warehouse, the organizational structure and from staff competence. “A useful guide for this purpose is the Analytics Maturity Model developed by INFORMS, a leading academic and professional analytics organization,” said Willem van Hoeve, a professor of operations research and head of the master of science in business analytics program at Carnegie Mellon University’s Tepper School of Business.
The experts heading the enterprise’s data analytics project should be versatile individuals with competences spanning the entire spectrum of data-related fields, including data analytics, data processing, data collection, data warehouses, data blending, data visualization, and data preparation. “It is, of course, hard to find one employee with expertise in all those disciplines,” Silipo observed. “The effort should be distributed in order to hire a group of people to cover the global expertise.”
