How an organization can move forward in developing advanced data analytics has been added to your bookmarks.
How an organization can move forward in developing advanced data analytics
Ask the pro: Steve Gold
Adopting an enterprise science approach to analytics may enable organizations to use data in new and more advantageous ways. Addressing obstacles such as guarding the status quo may be the first step.
Why is data analytics such a hot topic?
Every company is looking for an edge, regardless of industry. We’re now seeing the phenomenon of organizations trying to use data in a way they haven’t been able to use it before to gain that advantage—which is what data analytics, or the discipline of enterprise science, is all about. As one might expect, certain industries are further along in this journey. For instance, credit card organizations have become quite adept at using sophisticated algorithms to detect anomalies in buying patterns in order to prevent fraud. Leading online retailers too have become expert at using predictive analytics to recommend products based on a customer’s shopping preferences and previous purchases.
These capabilities represent massive cost-savings and/or revenue-generating opportunities—and even more, they have become part of the collective psyche. These advanced solutions have changed the game, which is why so many C-suite executives across so many industries and organizations, are pushing their organizations hard to develop advanced analytics capabilities—and fast. In almost every case, the output of these solutions greatly impacts business performance.
What prevents organizations from realizing the potential of enterprise science to improve business performance?
Despite these results, many organizations are not prepared to adopt and leverage analytics across the enterprise or take an enterprise science approach. In my view, two main obstacles are holding them back. First, they don’t have the competencies (i.e., mathematicians, statisticians, IT specialists, etc.) inside their organizations to build these capabilities and to integrate them into the enterprise. Second, cultural barriers prevent organizations from embracing the insights that enterprise science reveals. The best part of data analytics is its objectivity. When a computer leverages algorithms and massive data sets, it only deals with zeros and ones—the machine is always impartial. However, it becomes difficult in a large decentralized organization to integrate these types of solutions—and especially to use them to identify enterprise issues—since the functions and business units are naturally prone to guarding the status quo.
What is the first step to overcoming organizational inertia around accepting data analytics?
While effectively incorporating data analytics on a large scale requires a cultural shift, organizations have to start somewhere. A focused “use case” can provide that foothold. This typically involves assembling a team of internal and external resources to focus on a known problem in a defined area, such as, “How can we sell more widgets in the winter slow period?” Using analytics to produce insight into a question such as this can be very valuable, not only in a monetary sense but also because it allows people to overcome their fears about using math and data science in a new way.
Organizations are quickly discovering that these use cases can pay for themselves several times over, and once they do one, they usually want to do more. However, the next stage of evolution in enterprise science will need to go beyond addressing discrete problems. At some point executives will question the need to reinvent the wheel in each silo one step at a time. They will also recognize that a cross-functional view as well as governance and controls will be necessary if they’re going to start implementing these types of solutions across the company. This suggests a wake-up call is coming soon for shared services and GBS organizations since they are a logical place to house an enterprise science competency.