Sisense defines infusion as the practice of incorporating data and insights into end-user business applications. "Infusion is all about putting decision-supporting insights into a product in a way that feels native. And it's far more interesting," Scott Castle SVP of Product at Sisense says. Typically, a BI tool works by pulling data together to help end users draw their own conclusions. They aggregate data, slice and dice, figure it out, come to the insight and then, take action. Whereas, infusion speaks towards broadening perspective on what embedded analytics means to include more than just a chart to figure out.

In less than two years, the workplace has evolved quickly. Our personal space inside our homes has transformed into a makeshift office, while corporate buildings are vacant and underutilised. As vaccines continue to roll out, a hybrid work model has emerged, with staff now alternating and'taking turns' being back in the office. In the US, research done by SHRM.org highlights that 55% of the workforce favours a hybrid workforce post-pandemic. In the UK, a survey by PWC found 77% of UK employees want a mix of face-to-face and remote working.

Over the past decade, the field of artificial intelligence has made massive leaps forward. Today, those advancements are helping businesses differentiate themselves from the competition. Companies like Netflix and Amazon wouldn't be the same without their AI-based recommendation engines. Others use AI to improve everything from customer service operations to digital marketing. Up until recently though, the best AI solutions were only accessible to big, well-funded corporations.

The Sisense analytics platform is known for its augmented analytics capabilities and ease of use, and as it moves forward it will do so with a new leader in charge of its product development. Just over a year after its acquisition of Periscope Data, a purchase that added capabilities aimed at data scientists to the features geared toward business users Sisense was already know for, the New York-based vendor is focused on third-generation analytics in which AI and business intelligence embedded throughout the workflow will be prominent. Most recently, Sisense updated its analytics platform with new natural language query capabilities and introduced Knowledge Graph, a graph analytics engine the vendor developed that was trained on more than 650 billion past analytic events and informs the machine learning capabilities of the query tool. Now, to help shape its vision, Sisense has added Ashley Kramer as its first chief product officer. Kramer began her career as a software engineering manager at NASA.

Artificial intelligence (AI) is turning into a staple of all business software, regardless of whether customers know about it or not. Frequently, AI and machine learning abilities are installed inside applications and furnish clients with functionality, for example, automation or predictive capabilities. These smart applications make the procedures and tasks led by organizations and workers easier and simpler with the assistance of AI, yet it is critical to separate between devices that are AI-empowered and those that help create intelligent applications. Let's review the best AI software of 2019. Google Cloud's AI Platform makes it simple for ML developers, data scientists, and data engineers to take their machine learning projects from ideation to generation and deployment, rapidly and cost-effectively.

Machine Learning is the buzzword of the moment. In recent years, news stories raving about its possibilities have soared, Google searches for the term have quadrupled, and companies across the globe are scrambling to figure out how to capitalize on the excitement by bringing it into their product mix. While that can be a great thing, claims made by some businesses about what machine learning can do are wildly exaggerated. That makes it crucial to cut through the noise and get to grips with its potential, limitations, and what you can realistically achieve with your resources – so that any investment makes solid business sense. The pair joined forces to deliver an in-depth webinar on machine learning and business intelligence, which you can view in full here.

Machine learning continues to make serious inroads in big data markets, most notably in the automation of tedious business intelligence tasks. Among the emerging applications is discerning and highlighting patterns in enterprise data, then alerting users in real time to any anomalies. That's the premise of a new analytics platform designed to alert users to data deviations and unveiled this week by business analytics specialist Sisense. Its "Pulse" platform leverages machine learning to analyze complex data sets, and then alerts users to anomalies that can be used to track key performance indicators. New York-based Sisense said the automation of performance tracking would free users from having to monitor multiple dashboards and tasks such as manually running analyses to spot anomalies.

CHICAGO – – CHICAGO – February 28, 2017 – Narrative Science, the leader in advanced natural language generation (Advanced NLG) for the enterprise, and Sisense, the Business Intelligence (BI) company that's disrupting the industry by simplifying analytics for complex data, announced a strategic partnership today. With this partnership, Sisense is leveraging the Narratives for Business Intelligence API to power Sisense Everywhere, a program that is changing the way business users consume data by bringing it into their natural environments. They will be demonstrating their joint technology at the "Innovative BI in Action" session at the Gartner Data and Analytics Summit on March 5, 2017. Sisense Everywhere empowers everyday users to further connect with their data by engaging in two-way conversations on their platform of choice. The Sisense platform integrates Advanced NLG from Narrative Science to automatically transform users' data and visualizations into Intelligent Narratives.

When I was a young boy I saw the classic movie "2001 A Space Odyssey" with HAL, the voice interactive computer system that bordered on AI, and that sparked in me, a lifelong interest and career in IT. Today we are seeing devices that are starting to provide the beginnings of that same functionality like the Amazon Echo, Dot or Google Home. It's one thing to sit in your living room and call out to the air "Alexa, how old is Matt Damon" or "Alexa, play the Logical Song by Supertramp", it's another when your 6 year old is having a conversation with Alexa and orders a bunch of things from Amazon and it is quite another when you are trying to find ways to use it in the office to make your company more productive. The difference between Alexa and HAL is pretty dramatic, but at the core of them both, and AI in general, is Machine Learning. As Guy Levy-Yurista, Sisense Head of Product, described in this recent blog post "Sisense employs machine learning as a core element of its In-Chip data processing algorithms….We call it query recycling – breaking queries into smaller blocks that are later reassembled to answer future queries: if user A asks a completely new question such as'what was our average deal size last year?' and user B later asks'what is our year-over-year growth in sales?', This isn't OLAP, it is a learning algorithm that grows smarter and more efficient over time and as more unique queries accumulate. It learns to identify the reusable chunks within each query, and to use these as a knowledge base for future reference".

If something happened in your business, like a drop in daily transactions or a shift in customer demographics, how long would it take you to find out about it? Here's another question: Once you discover the incongruity in your data, how long would it take you to decide on how to resolve it, if necessary? We have finally reached the pinnacle where traditional marketing and data collection tapers, only to have discovered a mountain of potential insight far off in the distance. We now have the power to recognize, analyze, and solve complex business problems but to get to the next climb requires constructing a form of technology never explored before. Taking that next step requires combining dynamic algorithms with widely available hardware and other technical elements that can continue to grow and expand as it is used.