Organizations are dealing with exponentially increasing data that ranges broadly from customer-generated information, financial transactions, edge-generated data and even operational IT server logs. A combination of complex data lake and data warehouse capabilities are required to leverage this data. Our research shows that nearly three-quarters of organizations deploy both data lakes and data warehouses but are using a variety of approaches which can be cumbersome. A single platform that can provide both capabilities will help address organizations’ requirements.
Businesses are transforming their organizations, building a data culture and deploying sophisticated analytics more broadly than ever. However, the process of using data and analytics is not always easy. The necessary tools are often separate, but our research shows organizations prefer an integrated environment. In our Data Preparation Benchmark Research, we found that 41% of participants use Analytics and Business Intelligence tools for data preparation.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Information Management, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data. Big data architectures have attempted to solve the problem with large pools of cost-effective storage, but in doing so have often created on-premises management and administration challenges. These challenges of acquiring, installing and maintaining large clusters of computing resources gave rise to cloud-based implementations as an alternative. Public cloud is becoming the new center for data as organizations migrate from static on-premises IT architectures to global, dynamic and multi-cloud architectures.
Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads running independently, data spread across multiple data centers, data governance, etc. In our ongoing benchmark research project, we are researching the ways in which organizations work with big data and the challenges they face.
Although historically there has been a hard divide between what are colloquially called “Inside and Field Sales,” changes over the last 10 years have narrowed the distinction. The pandemic has only accelerated the path to unifying sales activities commonly performed to engage buyers and customers. Characterized by a very disciplined and controlled endeavor, inside sales teams have been heavier users of technology. This has enabled more productive engagement including emails and calls, as well as provided techniques such as gamification to set competitive internal dynamics that help motivate sales professionals.
Topics: Sales, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Sales Performance Management (SPM), natural language processing, AI and Machine Learning, intelligent sales, sales enablement