Recently companies think lots about analytics and its roles, and they find the possibilities and benefits of data-driven decision making. Companies rethink about the adaption of analytics in their various business challenges like improving customer experience and engagement, optimization of enterprise productivity, and in the field of innovation presently companies have pressure to address analytics as a holistic strategy of their operational areas but the large no of companies do not have an exact plan for operationalizing analytics.
According to SAS research, too many firms are allocating remarkable amounts and sizeable resources to establishing analytics but very sadly it does not deliver the expected value, which only serves to waste money and time. Advantage of data-driven decision making only from operationalized analytics so the company willing to do the work in this analytics is important. With the help of AI models, the company can automate analytics its works as a solution for all areas in the company to create efficiency in operation, innovation, and consumer insights and experience.
Employee time, energy, and enthusiasm are timeless factors in organizations so adjust strategies to prioritize and position analytics provide large value in all areas of business. The prime aim of operationalizing analytics is providing employees with a wide view of the world and aligning the organization ready to adapt to changes in the business. Usually, companies prioritizing analytics to create products and services for better customer experience.
Analytics can be operationalized into security practices to help the organizations be more proactive about reducing risks related to cyber. With analytics, it is possible to detect and take the right actions against adversarial threat actors like the latest ransomware attacks. Analytics also helps IT organizations create a more secure business environment and support individual workforces even in this COVID 19 period.
Most of the companies have a clear picture about their business goals in the area of data-driven decision making such as what data collected and analyzed, how to normalize data, data governance, and also awareness of potential data biases but some areas like data privacy and security standards companies struggle a lot. For many companies analytics remain somewhat supernatural. Today analytics more focus on the improvement of customer engagement and productivity so undoubtedly data analytics drive the business process to its peak and derive more revenue opportunities.
