The theme of this year’s Oracle NetSuite SuiteWorld was “Full Suite Ahead,” with content aimed at demonstrating to customers (and prospective buyers) the value of using more of what NetSuite has to offer. The business logic behind this concept goes beyond the obvious objective of upselling existing customers to increase the average annual recurring revenue. As is often the case with subscription businesses, customers fail to take advantage of what’s already included in their service. Ensuring that customers are achieving full value is essential to retaining them, and almost always a precondition to selling them more. For a cloud software vendor, this translates to having an effective customer success organization backed by a customer-centric product strategy and a product management organization that delivers on the strategy. All of this was on display at the event.
The pandemic years saw an exponential rise in organizational investment in digital learning, and for good reason. With in-person learning no longer an option, organizations were forced to quickly adapt to the changing world of work in order to ensure workers were prepared with the knowledge and training necessary to operate in ways they had never before seen. Beyond operational imperatives, organizations have turned to digital learning systems to find new ways to track and bolster productivity, sentiment and engagement of the workforce. This serves to protect the investments made in hiring, onboarding and upskilling or reskilling talent by mitigating risk and regrettable attrition as well as helping employees along a career path that is achievable and desirable for the employee and strategically advantageous to the employer.
Almost all organizations are investing in data science, or planning to, as they seek to encourage experimentation and exploration to identify new business challenges and opportunities as part of the drive toward creating a more data-driven culture. My colleague, David Menninger, has written about how organizations using artificial intelligence and machine learning (AI/ML) report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats, and improving the bottom line with increased sales and lower costs. One-quarter of participants (25%) in Ventana Research’s Analytics and Data Benchmark Research are already using AI/ML, while more than one-third (34%) plan to do so in the next year, and more than one-quarter (28%) plan to do so eventually. As organizations adopt data science and expand their analytics initiatives, they face no shortage of options for AI/ML capabilities. Understanding which is the most appropriate approach to take could be the difference between success and failure. The cloud providers all offer services, including general-purpose ML environments, as well as dedicated services for specific use cases, such as image detection or language translation. Software vendors also provide a range of products, both on-premises and in the cloud, including general-purpose ML platforms and specialist applications. Meanwhile, analytic data platform providers are increasingly adding ML capabilities to their offerings to provide additional value to customers and differentiate themselves from their competitors. There is no simple answer as to which is the best approach, but it is worth weighing the relative benefits and challenges. Looking at the options from the perspective of our analytic data platform expertise, the key choice is between AI/ML capabilities provided on a standalone basis or integrated into a larger data platform.
In my previous perspectives on cloud computing, I addressed some of the realities of cloud costs as well as hybrid and multi-cloud architectures. In the midst of the pandemic, my colleague, Mark Smith, authored a series of perspectives on considerations for business continuity in general, beginning with this look at some of the investments organizations must make to mitigate the risk of business disruptions. In this perspective, I’d like to address some of the realities of business continuity and cloud computing and how they impact the digital technologies of an organization. The cloud can be both advantageous and disadvantageous when it comes to providing business continuity.
Topics: Digital Technology
The starting point of an era is never precise and rarely conforms to neat calendar delineations. For example, the start of the 20th century is associated with the outbreak of war in 1914. So I expect that decades from now, the consensus will hold that what became known as the 21st century began in the year 2020, with the pandemic serving as a catalyst that accelerated already existing trends and forced changes to prevailing norms and practices. This and other disruptive events that have followed are reverberating through economic and social networks and will ultimately result in some new equilibrium, but the ructions on the way there will be sharp and ever-present. Large-scale disruptions in most aspects of doing business have forced change on organizations. In this climate, the financial planning and analysis group can play a far more important role by using technology to enhance organizational agility and improve performance.