IGOR RIKALO
TECHNOLOGY
Igor Rikalo is President and Chief Operating Officer of o9, having joined the company almost 14 years ago. He has witnessed the company grow into a global player, with more than 3,000 employees and hundreds of clients around the world.
“ I joined the company when we were just a handful of people with a vision,” he says“ and getting into the execution mode over those 13 and a half years has been a really rewarding and humbling experience to where we are today.”
Igor has been a part of o9’ s global scaling, having built high-performing teams in order to develop strong solutions for o9’ s customers. The company works to drive more intelligent planning and decisionmaking, creating better financial results and more transparency throughout the supply chain.
IGOR RIKALO
TITLE: PRESIDENT AND CHIEF OPERATING OFFICER
COMPANY: o9
INDUSTRY: SOFTWARE DEVELOPMENT
Igor is a visionary leader, with demonstrated success in helping organisations grow through strategic decision-making. He puts a focus on building high performing teams to deliver strong results to customers.
Q. WHAT IS THE ROLE OF PREDICTIVE ANALYTICS WITHIN ENTERPRISE INTELLIGENCE?
» That is a key question, and a very pertinent one, especially in a world of constant change, constant volatility, constant complexity that we are facing. Some of these disruptive forces on an enterprise – from the outside of the enterprise – have been quite impactful, starting from COVID, then to a lot of economic disruption, supply chain disruptions due to other disruptions worldwide.
We are now operating in a world where a lot of these large supply chains are very long, there’ s a lot of different points of disruption that are possible, which makes the domain or the function of predictive analytics ever more so important. When we started o9, we always distilled it to the statement that we are helping companies answer three W questions:
• what is happening and why in my business
• what is going to happen
• what actions should I be taking? Predictive analytics as a domain went through a significant maturation over the last several decades from being a rather simplistic, naive kind of a forecasting, to using a lot more a statistical approach. This has transformed in the last 10 to 15 years, using some of the machine learning, advanced algorithms and actually evaluating the impact that each of those drivers can make onto the forecast to increase the accuracy of that prediction and reduce any bias.
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