Data and Analytics for a Step Ahead in the Technology Innovation CurveBy CIOAdvisor Apac | Tuesday, January 29, 2019
The world is heading toward leveraging more and more technologies like the Internet of Things (IoT) and 5G; it is essential to revise and remember the basics: data and analysis. Organizations must have a solid foundational grip of their fundamentals aka 'data' if they are going to have a strong superstructure.
Companies that have not been able to make full use of data, analytics and created a culture that seeks and valued business intelligence (BI), IoT or any of the other technology projects will not go very far either. Understanding the data is the stepping stone and the row that determine a plan of action for companies in the vast ocean of rapid innovation.
Companies which align themselves in understanding their data tend to outsmart their competitors; Amazon which mastered the data and developed predictive analytics based on the customer data performed better than its competitors and emerge as a leader in the online retail space.
It’s never late to take the right step; business leaders must take these steps if they want to rapidly develop the capabilities of their organizations for greater business impact. Firstly, they need to develop a clear vision of holistic data and analysis strategies, since low BI maturity organizations often exhibit a lack of a clear vision for enterprise-wide data and analytics strategies.
Secondly, companies should consider creating a flexible organizational structure, exploiting analytical resources and continuing training in analytics. Companies need people, skills, and critical structures to nourish and persuasive skills and advanced skills. They must anticipate the projected needs and ensure that there are designed or can be used to support the work identified in the data and analytics strategy.
They must also implement a data management program. Governance is a critical framework that describes the decision-making; companies must have the rights and authority models that data and analytics must be subjected with.
Finally, companies must curate an integrated analytics platform to support a wide range of applications, because organizations with low maturity often have primitive IT infrastructures. Their business intelligence platforms are more traditional and reporting-centered, integrated into ERP systems, or simple disparate reporting tools with limited uses.