Real-world indicators are important for improving natural language processing (NLP) tasks such as life events for mental health analysis and risky behaviour for online safety, yet labelling such information in NLP training datasets is often costly and/or difficult given the dynamic nature of such events. This paper compares several LLM-based data enrichment methods and introduces a novel Confidence-Aware Fine-Grained Debate (CFD) framework in which multiple LLM agents simulate human annotators and exchange fine-grained evidence to reach consensus. We describe two new expert-annotated datasets, a mental health Reddit wellbeing dataset and an online safety Facebook sharenting risk dataset. Our CFD framework achieves the most robust data enrichment performance compared to a range of baselines and we show that this type of data enrichment consistently improves downstream tasks. Enriched features incorporated via debate transcripts yield the largest gains, outperforming the non-enriched baseline by 10.1% for the online safety task.

Artificial intelligence (AI) creates abundant opportunities for a wide range of intelligent, automated business operations. Two vital capabilities--metadata extraction and data enrichment--rank among the most valuable, commonly used functions for businesses seeking to harness immediate value from organizational data and content. AI-driven techniques for rapidly sorting, filtering, categorizing, and adding context to massive volumes of data can help deliver a distinct business advantage. By combining accessible, cloud-based AI services and customizable, specialized AI tools and training, businesses can shape data and content services to better meet their objectives. Despite the accelerating, never-ending spiral of accumulating content, most businesses aren't gaining the insights they need nor seeing visible operational benefits, as asserted in a Software Development Times article.

Editor's Note: See Pramod's talk "Transaction Data Enrichment and Alternative Data: An Opportunity for Business Growth and Risk Mitigation" at ODSC West 2019. In a recent financial wellness survey of American adults, 58% of respondents said they did not have the financial freedom to enjoy life. Less than half said they were confident in their ability to absorb an unforeseen financial emergency. Needless to say, Americans have room to grow when it comes to financial wellness. By enriching a consumer's transaction data, banks can demystify data, making it easier for consumers to understand their own spending and saving patterns.

As you have probably heard or read, IBM's Marketing Cloud recently published that "90% of world's data today has been created in the last two years alone." Growing daily at 2.5 quintillion bytes of data daily, this number will only explode over the next few years. This may seem impressive, but much of it is simply raw data. Nonetheless, you may point out that with all this data we are advancing technology, improving outcomes, enriching lives and making better decisions. However, how vastly improved these outcomes could be if all this data was enriched?

Inzata, the full-service data analytics platform powered by artificial intelligence, announces the general availability of its AI-Powered Data Enrichment Services, launching with 38 new One-Click Enrichments for all subscription users. Inzata's data enrichment services make any raw data more useful and accurate, and therefore more valuable. "Data enrichment" is the process of adding curated, high-quality 3rd party or external data to a company's traditional business data, efficiently and securely. The resulting enriched data is more accurate, more detailed, and yields far superior analytical insights. Digital marketers and data scientists alike have long known that enriching data with high-quality third party and external data is the fastest way to yield exponentially superior insights from even the most ordinary data.

As digital trends evolve every year, marketers should always be aware of the changes in order to easily adapt with emerging technologies and stay ahead in the market. This will help them gain a competitive edge and become able to develop new ways to grow their businesses, generate leads and improve the relationship with their existing customers. Based on the digital marketing trends article that we developed last year, 2018 has been the year of rising augmented reality, video content, voice search and influencer marketing... And now that 2019 is around the corner, you may ask yourself "what will the next year have for me?" To help you determine where the trajectory is heading over in 2019, we've listed out 10 digital marketing trends that you can take advantage of to improve your marketing strategy and meet a desired outcome. Voice search is undoubtedly rising in popularity.

We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency. Under consideration for acceptance in TPLP.

As you have probably heard or read, IBM's Marketing Cloud recently published that "90% of world's data today has been created in the last two years alone." Growing daily at 2.5 quintillion bytes of data daily, this number will only explode over the next few years. This may seem impressive, but much of it is simply raw data. Nonetheless, you may point out that with all this data we are advancing technology, improving outcomes, enriching lives and making better decisions. However, how vastly improved these outcomes could be if all this data was enriched?

Check out the Data science and machine learning sessions at Strata Data in New York, September 25-28, 2017, for more on current trends and practical use cases in applied data science. As we enter the latter part of 2017, it's time to take a look at the common challenges faced by companies interested in using data science and machine learning (ML). Let's assume your organization is already collecting data at a scale that justifies the use of analytic tools, and that you've managed to identify and prioritize use cases where data science can be transformative (including improvements to decision-making or business operations, increasing revenue, etc.). Data gathering and identifying interesting problems are non-trivial, but assuming you've gotten a healthy start on these tasks, what challenges remain? Data science is a large topic, so I'll offer a disclaimer: this post is mainly about the use of supervised machine learning today, and it draws from a series of conversations over the last few months.

Recent research shows that over 66% of employees are dependent on search in their daily work. Forty-one percent are frustrated with their existing search application. Many enterprise search platforms offer task-based search, providing a simple search, analyze, decide and start over approach that provides no context between searches by an individual. Attivio provides a different approach. Attivio Cognitive Search and Insights takes search beyond purely indexing data by incorporating innovative technologies such as machine learning, natural language processing, and content analytics to derive better insights and knowledge.