In my first perspective on cloud computing realities, I covered some of the cost considerations associated with cloud computing and how the cloud costing model may be different enough from on-premises models that some organizations are taken by surprise. In this perspective. I’d like to focus on realities of hybrid and multi-cloud deployments.
The migration to cloud is obvious. Organizations are adopting cloud computing for all variety of applications and use cases. Managed cloud services, commonly referred to as software as a service (SaaS), offer many benefits to organizations including significantly reduced labor costs for system administration and maintenance, as many of these costs are shifted to the software vendor. SaaS also provides organizations with faster time to value as they adopt new technologies by eliminating the need to acquire and configure hardware, and it also eliminates the need to install software. In fact, we assert that by 2025, nine in 10 organizations will be using multiple cloud applications in order to minimize the costs of administration and maintenance. Yet, there are some challenges associated with cloud computing I’d like to address in a series of Analyst Perspectives:
I have written previously that the world of data and analytics will become more and more centered around real-time, streaming data. Data is created constantly and increasingly is being collected simultaneously. Technology advances now enable organizations to process and analyze information as it is being collected to respond in real time to opportunities and threats. Not all use cases require real-time analysis and response, but many do, including multiple use cases that can improve customer experiences. For example, best-in-class e-commerce interactions should provide real-time updates on inventory status to avoid stock-out or back-order situations. Customer service interactions should provide real-time recommendations that minimize the time to resolution. Location-based offers should be targeted at the customer’s current location, not their location several minutes ago. Another domain where real-time analyses are critical is internet of things (IoT) applications. Additionally, use cases like predictive maintenance require timely information to prevent equipment failures that help avoid additional costs and damage.
For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our Analytics and Data Benchmark Research about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are operating today and the results they are achieving, we can discern some of the best practices for improving the outcomes of analytics and data processes.
Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.
Topics: Sales, embedded analytics, Analytics, Business Intelligence, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, subscription management, partner management, sales engagement, revenue management, Collaborative & Conversational Computing
Many organizations invest in data governance out of concern over misuse of data or potential data breaches. These are important considerations and valid aspects of data governance programs. However, good data governance also has positive impacts on organizations. For example, I have previously written about the valuable connection between the use of data catalogs and satisfaction with an organization’s data lake. Our most recent Analytics and Data Benchmark Research demonstrates some of the beneficial links between data governance and analytics. In this Perspective, I’ll share some of the correlations identified in our research.
Ventana Research recently announced its 2022 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments in order to improve business outcomes.
I’m proud to share Ventana Research’s 2022 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that increase workforce effectiveness and organizational agility, ensuring ongoing operations during any type of disruption.
Organizations today are working with multiple applications and systems, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and other systems, where data can easily become fragmented and siloed. And as the organization increases its data sources and adds more systems and custom applications, it becomes challenging to manage the data consistently and keep data definitions up to date. This increases the need to use master data management (MDM) software that can provide a single source of truth to drive accurate analytics and business operations.
TIBCO is a large, independent cloud-computing and data analytics software company that offers integration, analytics, business intelligence and events processing software. It enables organizations to analyze streaming data in real time and provides the capability to automate analytics processes. It offers more than 200 connectors, more than 200 enterprise cloud computing and application adapters, and more than 30 non-relational structured query language databases, relational database management systems and data warehouses.