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.
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: Analytics, Collaboration, Data Governance, Data Lake, Data Preparation, IOT, Data, Information Management (IM), Digital Technology, blockchain, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.
The emerging internet of things (IoT) is an extension of digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation means that virtually any appropriately designed device can generate and transmit data about its operations, which can facilitate monitoring and a range of automatic functions. To do this IoT requires a set of event-centered information and analytic processes that enable people to use that event information to make optimal decisions and take act effectively.
When it comes to managing product information, organizations know they have room for improvement; only 27 percent trust their efforts completely, and less than a fifth (19%) are very satisfied with them. Almost half (48%) say they have too many incompatible tools, while 41 percent do not have a centralized information repository and 45 percent use a manual process to create a single complete, consistent and reliable product record. All of these facts and more from our product information management (PIM) benchmark indicate that businesses need a set of integrated processes and applications to meet their responsibilities. The benchmark found that adaptability, functionality and usability top technology and vendor considerations among the core components of our Value Index methodology.
Topics: Sales Performance, Supply Chain Performance, MDM, PIM, Operational Performance, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Information Management (IM), Product Information Management