Orchestrate all the Things

Data quality is part of data intelligence. It's a topic that lot of people are concerned about, and it makes engagement and adoption around data intelligence solutions better. With many data quality solutions with different approaches available in the market, customers need to be able to choose the one that works best for them. Plus, if you are someone like Alation, a vendor whose core business is not data quality -- if you can't beat them, join them. As Alation CEO and co-founder Satyen Sangani shared, that was the thinking behind today's announcement of the Alation Open Data Quality Initiative (ODQI) for the modern data stack. As Alation notes, the program provides customers with the freedom of choice and flexibility when choosing the best data quality and data observability vendors to fit the needs of their modern, data-driven organizations. We caught up with Sangani to discuss the ODQI and where it fits in the broader data intelligence landscape, as well as Alation's strategy and evolution. Article published on VentureBeat

Circa 2017, there was a lot of hype around autonomous driving. If one were to take that at face value, it would mean that by now autonomous driving would have been a reality already. Apparently that's not the case, and Alex Kendall claims to have known that all along. Still, that did not stop him from setting out then, and he's still working on it today. Kendall is the co-founder and CEO of Wayve, a company founded in 2017 to tackle the challenge of autonomous driving based on a deep learning approach. Today, Wayve announced a partnership with Microsoft to leverage the supercomputing infrastructure needed to support the development of AI-based models for autonomous vehicles on a global scale. We caught up with Kendall to discuss Wayve's philosophy and approach, its current status, as well as where the partnership with Microsoft fits in. Spoiler alert: this is not your typical "commercial application provider partners with cloud vendor" story. Article published on VentureBeat

There is an implicit assumption in most analytics solutions: the data being analyzed, and the insights derived, are almost exclusively quantitative. That is, they refer to numerical data, such as number of customers, sales, and the like.  But when it comes to customer feedback, perhaps the most important data is qualitative: text contained in sources such as feedback forms and surveys, tickets, chat and email messages. The problem with that data is that, while valuable, they require domain experts and a lot of time to read through and classify. Or at least, that was the case up until now. This is the problem Viable is looking to address. Article published on VentureBeat

After data privacy and GDPR, the EU wants to leave its mark on AI by regulating it with the EU AI Act. Here's what it is, what it means for the world at large, when it's expected to take effect, how it will work in practice, as well as Mozilla's recommendations for improving it, and ways for everyone be involved in the process. Article published on ZDNet

What do you get when you combine two of the most up-and-coming paradigms in data processing - streaming and graphs? A potential game-changer, and this is the bet first DARPA and now CrowdStrike Falcon Fund have taken on thatDot and its open source framework Quine. The CrowdStrike Falcon Fund is an investment vehicle managed by CrowdStrike, in partnership with Accel, that makes cross-stage private investments within cybersecurity and adjacent markets. DARPA is also known to have an interest in cybersecurity, and this is what motivated the decision to fund the development of a framework recently released by thatDot as an open source project dubbed Quine. Many solutions exist on the market both for streaming data processing as well as for graph analytics, oftentimes working in tandem. However, thatDot Founder and CEO Ryan Wright claims that Quine's technology is unique, enabling it scale to orders of magnitude beyond what any other system today is capable of. We caught up with Wright to discuss the key premises behind Quine and thatDot, as well as the practical aspects of using Quine and the next steps in its evolution. Article published on VentureBeat

Amazon just unveiled Serverless Inference, a new option for SageMaker, its fully managed machine learning (ML) service. The goal for Amazon SageMaker Serverless Inference is to serve use cases with intermittent or infrequent traffic patterns, lowering total cost of ownership (TCO) and making the service easier to use. We connected with Bratin Saha, AWS VP of Machine Learning, to discuss where Amazon SageMaker Serverless fits into the big picture of Amazon’s machine learning offering and how it affects ease of use and TCO, as well as Amazon’s philosophy and process in developing its machine learning portfolio. Article published on VentureBeat

Modern agriculture was broken long before pandemics, wars, supply chain disruptions and fertilizer shortages. Regenerative agriculture can fix that, and data can help. What could a multi-national corporation like Unilever and an environmental leader and activist like Vandana Shiva possibly have in common? Shiva has a history of actively opposing the commodification and appropriation of natural resources for the benefit of corporate interests. Unilever is at the heart of the international corporate web. Shiva, a prolific author, just published her latest book: "Agroecology and Regenerative Agriculture: Sustainable Solutions for Hunger, Poverty, and Climate Change".  Unilever, whose products need around 4 million hectares of land to grow the raw materials for, recently published a new set of regenerative agriculture principles. There has to be something about regenerative agriculture. Let's take a look at what it is and why it's important, what the data tells us about it, and how analytics and AI may help going forward. Article published on ZDNet

Whether you're genuinely interested in getting insights and solving problems using data, or just attracted by what has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor, chances are you're familiar with data science. But what about graph data science? As we've elaborated previously, graphs are a universal data structure with manifestations that span a wide spectrum: from analytics to databases, and from knowledge management to data science, machine learning and even hardware. Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points -- that's the the 30 second explanation according to Alicia Frame. Frame is the Senior Director of Product Management for Data Science at Neo4j, a leading graph database vendor. She has a PhD in computational biology, and has spent ten years as a practicing data scientist working with connected data. When she joined Neo4j about 3 years ago, she set out to build a best in class solution for dealing with connected data for data scientists. Today, the product Frame is leading at Neo4j, aptly called Graph Data Science, is celebrating its two-year anniversary with version 2.0 which brings some important advancements: new features, a native Python client, and availability as a managed service under the name AuraDS on Google Cloud. We caught up with Frame to discuss graph data science the concept, and Graph Data Science the product.

Processing data in real-time is on the rise. The streaming analytics market (which depending on definitions, may just be one segment of the streaming data market) is projected to grow from $15.4 billion in 2021 to $50.1 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 26.5% during the forecast period as per Markets and Markets. A multitude of streaming data alternatives, each with its own focus and approach, has emerged in the last few years. One of those alternatives is Apache Pulsar. In 2021, Pulsar ranked as a Top 5 Apache Software Foundation project and surpassed Apache Kafka in monthly active contributors. In another episode in the data streaming saga, StreamNative just released a report comparing Apache Pulsar to Apache Kafka in terms of performance benchmarks. We caught up with StreamNative Chief Architect & Head of Cloud Engineering Addison Higham to discuss the report's findings, as well as the bigger picture in data streaming. Article published on VentureBeat

There's no business like powering business. That may seem like a paradox, and most of us don't really think of accounting as a separate line of business. In that sense, it's a secret hiding in plain sight: accounting is big business, in and of itself. The global accounting services market is expected to reach $735.94 billion in 2025, and digital transformation has targeted it for impact. The size of the accounting software market is projected to reach $22,9 billion by 2027. This includes names from Microsoft, Oracle and SAP to Intuit and Xero, plus many others. Yet, there's something none of these names are applying, says Isaac Heller: AI in accounting. That's why Heller and Amir Boldo co-founded Trullion in 2019. Trullion seems to be on a mission to disrupt the accounting software market by "unifying the unstructured and structured worlds of accounting by reading PDF or Excel-based contracts and translating them into financial workflows, connected to the data source". In February 2022, Trullion announced that it has closed $15 million in Series A funding. Trullion has more than 100 clients, a mixture of large enterprise clients and audit firms which procure directly for their clients, and has crossed the seven figure mark in recurring revenue. We caught up with Heller to discuss Trullion's focus and its use of AI to innovate in the accounting software market. Article published on VentureBeat