Monthly Archives: May 2022

Unstruk Data: Empowering Enterprises to Transform Unstructured Data Files into Actionable Intelligence about Real-world Assets and Locations

The latest version of Unstruk’s flagship product is the first commercially available DataOps platform built to generate intelligence about real-world assets and locations, so that it can be utilized to automate processes, inform business decisions or integrate into other critical workflow systems.

How Automation Gets Finance Workloads Back in Balance

How can finance teams lighten the workload and take their rightful place advising on business strategies and best practices? Automation can help. Software robots are ideally suited for transaction processing, accounting operations, and compliance work. It’s time for finance teams to do more of what they were meant to do: help the entire business be better. In this eBook from UiPath we’ll take a quick look at how automation can make that happen.

Deepnote Comes Out of Beta to Make Data Science and Analytics Collaborative

Deepnote, an early-stage startup backed by Accel and Index Ventures, launched version 1.0, opening up to the general availability of collaborative data science notebooks to data teams worldwide. Deepnote is a new data science notebook that makes data insights truly interactive. Jupyter compatible with real-time collaboration and runs in the cloud.

Active Monitoring and Statistical Prediction of Indoor Radon Concentration Can Reduce the Risk of Lung Cancer

healthcare aiIn this contributed article, Insoo Park, CEO of Ecosense Inc., discusses how scientists are turning to statistical prediction methodologies for help with preventing radon-related deaths. Exposure to radon is the first leading cause of lung cancer among non-smokers According to the EPA, radon-related lung cancer deaths total 21,000 per year in the US alone.

Infographic: State of AI 2022

Peak, a leader in Decision Intelligence (DI), released a new “State of AI, 2022” report highlighting that a number of organizations are making investments in both data and AI – without connecting the two. Surveying 775 decision makers (senior managers and above) from the US, UK or India, the report further reveals that when businesses are failing to prepare their data for future AI adoption, they risk needing to retrofit their infrastructure later down the road.

The Secret to Solving the World’s Crimes Lies in Data

In this contributed article, Chris Cardwell, Product Go-To-Market Lead for Tresata, discusses how data can help tackle the global problem that is financial crime, but there are challenges within the data itself that complicate investigations further.

How Macmillan Publishers Authored Success Using IBM Cognos Analytics with Watson

Macmillan Publishers is a global publishing company (operating in over 70 countries) and one of the “Big Five” English language publishers. If you’re a reader, chances are good that you’ve read a book from Macmillan. They are the publishers of the influential Wheel of Time fantasy series, Oliver Sacks’ extraordinary The Man Who Mistook His Wife for a Hat, and Sunday Times’ business book of the year, Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World.

With such a widespread global operation, Macmillan has long invested in platforms to source deep analytical information about sales, inventory, and transportation of their titles in the market. The publisher has used IBM Cognos Analytics for more than ten years to wrangle its internal and external operational reporting needs. This usage encompasses their supply chain, inventory management, and production.

The publishing industry is a heavy user of metrics. For example, when an editor is interested in acquiring a new book, the standard operating procedure is to investigate historical sales figures of related books to judge how a similar title would do in the market. Using metrics is essential to their business operations, and data assists them in making all kinds of business decisions. Data helps decide how long to keep a book in print, when to move it to the backlist, or delist it entirely.

However, due to outdated fulfillment systems, the quality of that data was not as high as it should be, causing the analytics and reporting process to become overly complex and burdensome. In addition, transactional data was stored in a way that did not support the deep analysis required to make the best future-looking decisions for the business.

Publishing leaders need access to operational data like internal sales figures and external vendor sales data to make the best decisions. They may also need to understand how well a title performed in the market and how much physical inventory exists. The operational side of the business cannot function without access to these granular metrics.

The operational side is editorial, production, inventory management, sales and finance, and HR teams. Until recently, when a team member needed operational information about a title, they had to request a report from an analytics team member. Because a requester only needs to see information on their titles and not the business’s overall health or another editorial group’s book list, these analytics requests are specific, time-consuming, and drill down on granular data.

The Macmillan analytics team wanted to make it easier for anyone in the company to collect the answers they’re looking for themselves. So, the group kicked off an initiative to standardize reporting and analytical objectives across departments. In going through that process, the analytics team discovered that the most strategic way to implement self-service was to move their business intelligence operations to the cloud. Moving to the cloud would ensure their data remained organized, clean, and connected.

Working alongside its IBM technology partner, Sterling Technology Group, Macmillan redesigned their reporting tool and user-driven modeling. Sterling remained steadfast and proactive, offering consulting and services to ensure Macmillan completed the migration thoroughly and successfully. Above all, the Sterling team ensured that the transformation of legacy systems supported the business goals of driving cost savings and reduced administrative maintenance. Overall, the analytics group can now spend less time maintaining and optimizing servers and more time adding value to business operations.

Since they already had a trusted relationship with IBM Cognos, the Macmillan analytics team decided to keep the partnership going and upgraded to IBM Cognos Analytics with Watson. The upgraded platform improved their data warehouse processes, business intelligence models, and reporting.

“Cognos was the easy part.”

– Richard Babicz, Business Intelligence Manager & Architect at Macmillan

The next important step in the journey for the Macmillan analytics team is to grow its data culture. Without a robust data culture, seeing success in a self-service data and analytics initiative is difficult. The granular data teams need to understand is very complex. The data requires business rules and logic applied, requiring a higher skill level than most editors or sales associates have. The Macmillan analytics team uses a two-part strategy to grow their data culture: Data modules and champions.

Data modules are a data modeling feature inherent to IBM Cognos Analytics. A data module allows the analytics team to set up containers that describe rules for accessing data, and they can fine-tune them for each team’s use. The result is an easy-to-use drag and drop interface. So, users can now generate customizable reports to track orders, view the health of any title published, see its shipping history, and how the title is performing in-market.

On the other hand, champions are individual team members from across the business. A senior analyst trains them to help scale training and further self-service adoption. By using the pre-fine-tuned data within the data module, champions are their team’s primary point of contact for analytics and reporting. They are both answering questions for their team and training their team on how they can get the most out of the platform.

The goal is to empower users companywide to quickly and easily pull whatever information they need about their titles. The solution also allows users to derive more insights from financial reports like accounts payable and royalties. In addition, the finance team has automated a once manual process using Cognos. They deploy Cognos to scrape information from Amazon on chargebacks and returns so that they can have a complete picture of a title’s lifecycle.

Other benefits the analytics team has seen include a 50% reduction in administrative costs and a 100% reduction in hardware costs. They have also realized a 40% reduction in overall administrative maintenance tasks and efforts. In addition, several key users can access data via data modules and merge it with external sources using the new UI and improved search functions that facilitate content discovery.

Around one thousand users across Macmillan’s business units and teams currently consume this data. The Macmillan analytics team wants as many people using the platform as possible to uncover issues with the system. More users give them more opportunities to solve problems they aren’t yet aware of. The Macmillan analytics team predicts that the combined strategy of data modules and champions will help self-service usage grow by 20%.

If you’re on the fence about switching to the cloud, Richard Babicz, Business Intelligence Manager & Architect at Macmillan, advises creating an on-premises sandbox prototype. Then, migrate a representative subset of your content into that sandbox and see if it solves the problems you’re looking to solve. A local on-premises sandbox environment will ensure the switch to the cloud has no impact on your query modes.

The post How Macmillan Publishers Authored Success Using IBM Cognos Analytics with Watson appeared first on Journey to AI Blog.

The Value of Artificial Intelligence in Customer Data Platforms

In this special guest feature, Aditya Bhamidipaty, Founder & CEO, FirstHive, discusses how businesses today face an expanding gap between the value their customers’ data can potentially provide and the true value their CDPs can create. AI systems can help close this gap by enhancing the productivity of human workers, so long as those workers are trained how to use those systems effectively.

AI > Humans in Understanding Emotion in Business

A new report was launched by Red Box, a leading voice software specialist, around challenges and opportunities in AI. The data in the report, “Being Human: How and Why Machines are Learning the Art of Human Conversation,” revealed business leaders view AI as more effective than humans in all business use cases.

Introducing Vultr Talon with NVIDIA GPUs — Cloud Platform Breakthrough Makes Accelerated Computing Efficient and Affordable

Vultr®, a leading independent provider of cloud infrastructure, announced that Vultr Talon, powered by NVIDIA GPUs and NVIDIA AI Enterprise software, is now available in beta. A breakthrough cloud-based platform, Vultr Talon offers affordable accelerated computing by enabling GPU sharing, so multiple workloads can efficiently run on a single NVIDIA GPU.