Ackroo Inc., a loyalty marketing, gift card and payments technology and services provider, is pleased to announce that they have launched Ackroo BI, Ackroo's business intelligence data services product. As a data driven MarTech company, Ackroo now offers an end to end data solution that combines an Ackroo developed DataWarehouse for ingestion of ALL sales and transaction data, a storage and data transformation tool in order to process, store and sort the ingested information, plus an integrated data presentation and visualization tool for custom dashboards and reports. Clients can also choose to use their own visualization tool and just utilize Ackroo's Enterprise DataWarehouse and leverage the Ackroo data engineering services team to support their data needs. The solution will provide Ackroo merchants a centralized and unified data set to better understand not just their loyalty and gift card data but ALL purchase data in order to make better marketing and business decisions and to truly understand ROI. For Ackroo this means even further differentiation in the marketplace and an additional revenue stream that the Company expects will have a significant impact on their organic growth in the years ahead.
The year might kick-off at an ominous note with recession indicators showing omen of an economic downstream, the IT space has never been feast to one's eye more indispensable with emerging technologies playing pivot. Presently, not a day passes without any news and message having word Artificial Intelligence, Machine Learning, and Big Data. The algorithm continually evolves, the experts gain knowledge, consisting of information about each trade; this undeniably draws exciting prospects for the future with customized good, food, and entertainment. With the best AI/ML development companies in India and the USA paring costs and more data-driven decisions, they are proving to be a simple yet efficient proposition of the time. Recently, business and startups have started observing value in actionable insights from a vast swath of raw data and information.
AI sign in the center of circuit board, Artificial intelligence connect the future, vector... [ ] illustration For many companies, when it comes to implementing AI, the typical approach is to use certain features from existing software platforms (say from Salesforce.com's Einstein). But then there are those companies that are building their own models. Yes, this can move the needle, leading to major benefits. At the same time, there are clear risks and expenses. Let's face it, you need to form a team, prepare the data, develop and test models, and then deploy the system.
Modern software applications are often comprised of distributed microservices. Consider typical Software as a Service (SaaS) applications, which are accessed through web interfaces and run on the cloud. In part due to their physically distributed nature, managing and monitoring performance in these complex systems is becoming increasingly difficult. When issues such as performance degradations arise, it can be challenging to identify and debug the root causes. At Ericsson's Global AI Accelerator, we're exploring data-science based monitoring solutions that can learn to identify and categorize anomalous system behavior, and thereby improve incident resolution times.
New Relic this week announced it has extended the capabilities within its New Relic AI module to include support for additional data sources and provide access to more advanced analytics. Integrations with Splunk, Grafana, Prometheus and Amazon CloudWatch are now provided along with support for a number of incident management platforms, including ServiceNow, OpsGenie and VictorOps. New Relic AI previously provided support for the incident management platform from PagerDuty. DevOps teams can also surface more details about anomalies, including attributes that caused spikes, related signals to investigate root cause by looking at what happened around the anomaly and view upstream and downstream dependencies. Guy Fighel, general manager and group vice president for New Relic, said these and other capabilities being added to extend the capabilities of a monitoring platform that as a software-as-a-service (SaaS) platform provides a natural focal point applying machine learning algorithms to the massive amounts of data required to inform an AI engine.
ThetaRay, a provider of Big Data and artificial intelligence (AI)-enhanced analytics tools, has joined Microsoft's (NASDAQ:MSFT) partner program, One Commercial Partner, which provides various cloud-powered solutions. ThetaRay's anti-money laundering (AML) solution for correspondent banking can be accessed through Microsoft's Azure Marketplace. A large US bank has reportedly signed an agreement to use the solution. "We are proud to join the One Commercial Partner program and offer Microsoft Azure customers access to our industry-leading AML for Correspondent Banking solution." "Global banks are increasingly de-risking or abandoning their correspondent banking relationships due to a lack of transparency and fears of money laundering and regulatory fines. Our solution provides banks with the … ability to reverse the trend and grow their business by allowing full visibility into all links of the cross-border payment chain, from originator to beneficiary."
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
There has been a lot of talk, as of late, that the cloud has become a resource to turn to when a company needs large amounts of digital storage, constantly updated SaaS (software-as-a-service), or high-performance computing capabilities. Couple this with the expansion of AI (artificial intelligence) in applications, even smaller companies can realize the full computing power available in the cloud. And while the cloud has been highly publicized as an option for everything digital, an even more powerful environment exists that hasn't gotten nearly as much coverage, at least not yet. Lookout world here comes quantum computing. A recent IDC (Intl Data Corp.) survey of IT (information technology) and business personnel responsible for quantum computing adoption has revealed that improved AI capabilities, accelerated BI (business intelligence), and increased productivity and efficiency are quickly proving to be the top expectations of organizations currently investing in cloud-based quantum computing.
Some people may even wonder what data science really means. At its core, data science seeks to comprehend the what and the why questions. This article aims to introduce all the branches of data science and explain its various phases. Below is a quick look at all the terms and techniques that I'll be reviewing in this article: Data access is the first step in any data science project. It refers to the data scientist's ability to read, write or receive the data within a database or a remote repository.
BERLIN, Nov 21 (Reuters) - German data mining software firm Celonis said on Thursday that it had raised $290 mln in a Series C funding round, putting a $2.5 billion valuation on the company that has been compared with enterprise application giant SAP . The funding round was led by Arena Holdings and investors included Ryan Smith, the founder of customer experience specialist Qualtrics that was bought by SAP for $8 billion a year ago. Celonis, based in Munich and New York, runs a cloud-based service that uses artificial intelligence to mine data and optimise business processes, serving customers including Siemens, 3M, Airbus and Vodafone. "We are in a market that shows enormous momentum," co-CEO and co-founder Bastian Nominacher told Reuters, adding that Celonis would invest the funds raised in its global sales and customer service and in enhancing its cloud platform. The funding round brings total investments into Celonis to $370 million.