How does one measure the success of an organization in the age of insight and business intelligence? Enterprises today run on a strong foundation built on the clarity and efficiency of the data that they possess. Highly beneficial data analytics that gleans business insights faster is the lifeblood of the data economy and every business' evolution is plainly dependent on analytics.
Every new technological innovation in any industry is dependent on the availability of infrastructure to support said innovation. For instance, Electronic Medical Records (EMR) could not have been a reality without the requisite facilitation of storage and performance-enhancing tools. Solutions implementing Software-as-a-Service (SaaS) is one such requirement for innovations in healthcare.
DataOps is a collection of methodologies that has been taking the data management domain by storm. As we know, DevOps is the natural result of applying lean principles such as broad focus and continuous improvement to application development and delivery. DataOps takes these principles and applies them to data science.
A world-renowned bank's CEO was recently quoted as saying that, while there are thousands of manual banks now, the future will see only a handful of digital banks. Along with the rest of the world's critical business sectors, an overwhelming number of banks are also shifting to virtual platforms. This migration is being orchestrated by some of the best DevOps resources that the global FinTech sector has to offer.
Discussions about the future of cloud implementation always involve the building and operating of DevOps platforms in cloud-native environments, which is also known as CloudOps. However, the proper know-how to fully grasp the long-term impact, challenges, and solutions of this technology are quite limited in the software industry.
Efficient customer communication is the basis for gaining the trust of a loyal customer base for any business. While data science-backed approaches in customer service and retention have massive benefits, attention must be paid to the quality of customer communication. Cloud-based solutions and AI must be leveraged for more promising customer communications.
An overwhelming number of leading banking organizations have been adopting conversational AI to improve customer service and end-user experience. This has been especially evident in the retail banking sector where there is no shortage of competitors. Valuable customer analytics and 24/7 support are some of the benefits that conversational AI offers.
Organizations depend heavily on customer data for predicting sales trends and coming up with strategies for successful sales campaigns. Maintaining healthy customer data is a high-priority requirement for a flawless revenue stream for any company. Customer data is arguably one of the most valuable global resources today.
Traditional financial institutions still rely on manual tracking when it comes to the vendor management component of their workflow. They make use of spreadsheets, email, and shared files on online drives instead of a dedicated vendor management platform. A common misconception in the financial industry is that investing in a Vendor Management System (VMS) is more expensive than manual processes.
Chief revenue officers of big brands often are tasked with finding the right balance between short-term revenue pursuits and long-term brand equity building. The emergence of advanced marketing analytics and Big Data is making this job much more challenging. As data is becoming more voluminous and yet more precise, what are the challenges it poses to brand equity?