Are you making the most of your collected data? The data you accumulate through your products and services can be a game-changer for your organization. Imagine if you can put that information to the proper use! Knowledge Graphs can allow you to make the most of your information to access, search, and utilize data for your enterprise search needs. A Knowledge Graph is a progressive way of interconnected search, an accurate query search resolution system that combines entities like people, objects, and places.

Every day, businesses and organizations are tasked with making more decisions than any human could ever hope to handle. Often, enterprises need to make complex business decisions with limited information on hand. With the help of AI-based products and AI-driven enterprise search solutions as a critical enabling technology, leaders can make better strategic and informed decisions by gaining insight from a vast amount of data in a short period. With the assistance of custom dashboards and 360-degree views of data, employees with different roles can each have a single view into all the information they need at its most appropriate level. The key message for companies is that making the right decisions and fast decisions are not either-or anymore.

Unstructured data belonging to the enterprise continues to grow, making it a challenge for customers and employees to get the information they need. Amazon Kendra is a highly accurate intelligent search service powered by machine learning (ML). It helps you easily find the content you're looking for, even when it's scattered across multiple locations and content repositories. Amazon Kendra leverages deep learning and reading comprehension to deliver precise answers. It offers natural language search for a user experience that's like interacting with a human expert.

Citibot is a technology company that builds AI-powered chat solutions for local governments such as Fort Worth, Texas; Charleston, South Carolina; and Arlington, Virginia. With Citibot, local residents can quickly get answers to city-related questions, report issues, and receive real-time alerts via text responses. To power these interactions, Citibot uses Amazon Lex, a service for building conversational interfaces for text and voice applications. Citibot built the chatbot to handle basic call queries, which allows government employees to allocate more time to higher-impact community actions.

Machine learning (ML) is gaining momentum across a number of industries and scenarios as enterprises look to drive innovation, increase efficiency, and reduce costs. Microsoft Azure Machine Learning empowers developers and data scientists with enterprise-grade capabilities to accelerate the ML lifecycle. At Microsoft Build 2020, we announced several advances to Azure Machine Learning across the following areas: ML for all skills, Enterprise grade MLOps, and responsible ML. New enhancements provide ML access for all skills. Data scientists and developers can now access an enhanced notebook editor directly inside Azure Machine Learning studio.

Commerce moved beyond brick-and-mortar stores, seized the online space, and is steadily crippling into mobile. Modern online shopping offers unparalleled flexibility of access and selection of available interfaces. You can shop away while on a bus. You can buy your next favorite gadget while relaxing on a lawn on a beautiful sunny day. That's why mobile commerce is such a fast-growing niche that caters specifically to smartphone users and their respective app ecosystems.

With all the hype around artificial intelligence (AI), cognitive search and intelligent intranets, it's hard to understand how to actually apply those new technologies to improve the workplace. But it may not be that difficult: If you get a few of the basics right, you can put an effective cognitive search system in place and set it up for ongoing growth. Machine learning (ML) is behind all this, and it is advancing incredibly quickly. Sometimes it's touted as almost magic, but in fact the underlying algorithms in machine learning have not really changed in a decade. The advances are the result of fast, cheap computing, the availability of huge sets of data, open-source software and models, and easier administration and development environments.

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More than half (54%) of global information workers are interrupted from their work a few times or more per month to spend time looking for or trying to get access to information, insights, and answers. The problem: Old keyword-based enterprise search engines of the past are obsolete. Cognitive search is the new generation of enterprise search that uses artificial intelligence (AI) to return results that are more relevant to the user or embedded in an application issuing the search query. A new generation of enterprise search solutions that employ AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Our new Forrester Wave report evaluates 9 of the leading search vendors against 23 important criteria.

Knowledge Management is a term that goes in and out of favor. Whatever name you give it though, consistently capturing and reusing intellectual assets is a critical endeavor within an organization that values information- sharing. In addition to the social and organizational aspects of KM, a short list of systems supporting KM includes search, collaboration, and distance learning. Whether called KM or not, most companies have used email and calendaring for some time and, in a limited sense, already use a KM system: basic collaboration. Today--as far as KM in practice goes--enterprise search and retrieval seems to be giving organizations the most trouble.