Adobe is leaning on AI-powered data solutions to bridge the post-cookie identity gap. This fits into Adobe's broader strategy of using a mixture of automation and artificial intelligence to figure out what people are looking for and predict how brands can demonstrate value for customers in the moments that matter, said Kevin Lindsay, Adobe's director of product marketing. In practice, that means focusing on reducing churn and anticipating a customer's needs rather than just pushing to complete a transaction. Considering the rising cost of customer acquisition, convincing someone not to cancel a service can be more valuable than converting a new customer. "It's also about paying attention to signals and emotional cues, like frustration," Lindsay said, and [determining whether you're] ticking people off with a bad experience."

Effective policing relies on good data. The prevention and reduction of crime, particularly serious and organised crime, depends on law enforcement agencies being able to gain swift insights from the huge and increasing amount of information at their disposal. The problem, given the sheer volume and variety of that data, is where to look first. So much of the data available to law enforcement data analysts and senior staff is unstructured. Police forces collect data of many different types – images from CCTV, phone records, social media conversations and images, and so on.

This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any pair of autonomous robots hold non-identical, but overlapping parts of a global joint probability distribution, representing real world inference tasks (e.g., mapping, tracking). It is shown that in dynamic stochastic systems, filtering, which corresponds to marginalization of past variables, results in direct and hidden dependencies between variables not mutually monitored by the robots, which might lead to an overconfident fused estimate. The paper makes both theoretical and practical contributions by providing (i) a rigorous analysis of the origin of the dependencies and and (ii) a conservative filtering algorithm for heterogeneous data fusion in dynamic systems that can be integrated with existing fusion algorithms. This work uses factor graphs as an analysis tool and an inference engine. Each robot in the network maintains a local factor graph and communicates only relevant parts of it (a sub-graph) to its neighboring robot. We discuss the applicability to various multi-robot robotic applications and demonstrate the performance using a multi-robot multi-target tracking simulation, showing that the proposed algorithm produces conservative estimates at each robot.

By Vikram Labhe It is a truism at this point to talk of the centrality of data for organisations. According to IDC, the global datasphere will rise at a compound annual growth rate (CAGR) of 23% between 2020-2025, highlighting the importance of responding to the surge in storage demand. For businesses to leverage data insights and drive growth, they must coordinate the dependencies and execute the different tasks on their data journey in the desired order, all while ensuring minimal impact from potential errors. Whether an organisation favours extract, transform, load (ETL) or extract, load, transform (ELT) will depend on their specific needs. Orchestration is fundamental for modern data processes, but for many businesses a modern data stack makes specific orchestration tools redundant.

In the book'Bad Data Handbook' Q Ethan McCallum has rightly said, "We all say we like data, but it's not the data but the insights that we derive from it are what we care about." Yet, a data analyst gets to dedicate only 20% of her time to the art and science of generating insights out of data. The rest of her time is spent in structuring and cleaning the data. In order to minimize the time investment in data cleaning, there is a need of standardized frameworks and tools that work for the diverse data and business use cases across industries, functions, and domains. This blog aims to equip you with the knowledge you need to build and execute such standardized data quality frameworks that work for your data and use cases.

Number one, I'm looking at improved productivity of users, the technical experts, citizen developers, or business users. Secondly, if complexities are solved, it opens up for business users to carry out integration tasks almost without any support from a central IT team, or your integration specialist, such as a data engineer,

AI-powered optical character recognition lets insurers unlock vast troves of data and streamline all processes.||Insurers still struggle with PDFs, images and handwritten documents. Countless human hours are required to manually extract the data into a machine-readable format. This process is known as ETL (extract, transform and load). Insurers that can maximize their ETL capabilities have a powerful competitive advantage. 

Mailchimp is a leading marketing platform for small business. We empower millions of customers around the world to build their brands and grow their companies with a suite of marketing automation, multichannel campaign, CRM, and analytics tools. We're looking for a Software Engineer III to work with a high performing group of software engineers and analysts. This newly formed team will support the development, configuration and architecture of data sources and integrations across Mailchimp. In this position we seek someone who's excited to work with a dynamic, diverse group of engineers and managers who are all used to wearing multiple hats to get the job done.

Analyzing data is now essential to success in education, employment, and other areas of activity in the knowledge society. Even though several frameworks describe the competencies and skills needed to meet current and future challenges, no data analytics competency framework exists to describe the importance of specific skills to succeed in data analytics assignments.

The Data Engineer has been gaining popularity in the past 10 years, but what exactly do Data Engineers do? Data Engineers in my experience wear many hats, and often sit in the middle of a triangle of Business Intelligence, Software Engineering, and Data Science. One primary role of the Data Engineer is to partner with downstream teams, such a Business Intelligence and Data Science to understand the data needs of the business, and build data integrations to supply these data. The other role can be to partner with Software Engineers to consume application data; typical of new software development efforts, or "0 to 1" projects. Data Engineers are often hidden in the shadows; monitoring data quality dashboards, listening to engineering sprints, and eavesdropping in analytics meetings.