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Jeremy Coughlin is the VP, Enterprise Analytics for Coast Capital Savings, where he is responsible for enabling the organization to effectively leverage data. View the full article

A fundamental requirement for any data-driven organization is to have a streamlined data delivery mechanism. With organizations collecting data at a rate like never before, devising data pipelines for adequate flow of information for analytics and Machine Learning tasks becomes crucial for businesses. As organizations gather information from multiple sources and data can come in […]View the full article

As organisations embark on becoming more digitally enabled, the road is paved with many twists and turns. Data science teams and analysts, as well as the teams they serve, know full well that the path to analytic excellence is not linear. The good news is organisations have opportunities to unlock value at each step along the way. The pattern by which companies develop strength within their data is highly repeatable, underpinned by more ways to manipulate data and unlock the benefits of automation. While automation in and of itself isn’t digital transformation, since new processes are not being launched, it frequently delivers huge value and lays the framework for organizations to make major operational improvements. With automation in place, organizations can harness more analytical approaches with modelling enhanced by AI and ML. Once these core capabilities move out of the sole domain of technical IT teams and are put into the hands of more domain experts, true transformation of business process occurs and more overall value is derived from analytics. Delivering value from the start Automation is typically one of the earliest steps in overhauling enterprise analytics. In my experience, this step won’t deliver as much value as those that follow – but it’s still significant and, beyond that, vital. Let’s take a large manufacturer automating its VAT tax recovery process as an example. While some might assume that this type of automation simply saves time, many companies are not recovering 100% of their VAT because the manual, legacy process has a cost, and if the VAT is below a given value, it might not be worth the recovery. When this process is automated, 100% VAT recovery yields become possible – the hard cash savings for the business can’t be ignored. Finance teams can automate many of the manual processes required to close their books each quarter, reducing the time it takes to close from a matter of weeks to days. Audit teams can upgrade from manual audits repeated every couple of years to continuous audits which check for issues daily and report any issues automatically and instantly. From reducing cost and risk to increasing revenue and saving time for employees (your greatest asset), automation is having a huge impact on organizations around the globe. With this lens, it’s evident that automation amounts to much more than time savings. Two varying approaches There are two very different approaches that organizations have historically taken to drive automation. The first, which has a more limited impact, is to form a centralized team and have that small team attempt to automate processes around the business. The second approach is to upscale employees to allow every worker to be capable of automating a process. This latter approach can scale at a very different pace and impact. Organizations can upskill tens of thousands of employees and automate millions of manual processes. This would be very difficult with a small team trying to perform the same automation. It can lead to substantial business benefits, including increased productivity, reduced costs and greater revenue. Historically, of course, the latter approach has also been nigh on impossible to execute – given the requirement for familiarity with coding language to use code-heaving technologies. But that was then – today, when mature low-code systems present a massive opportunity to upskill employees to automate processes simply by asking the right questions. This isn’t simply an alternative route – it should be the only route for organizations that are serious about achieving analytical excellence. Code-free platforms remove the need for departments to wait in queues for the IT teams to deliver an application that fits their needs. It puts the power of automated analytical and development capabilities into the hands of business domain experts with the specific expertise needed to get valuable insight from analytics quicker. Therefore, upskilling efforts need to be directed towards making such a broad data-literate culture possible. Providing teams with automation tools For many organisations, a common strategy for driving upskilling and capability is to focus on its new employees. With attrition and growth rates at many businesses ranging between 5 and 10%, organisations can face the challenge of replacing as much as a quarter of their entire team moving on every 18 months. Providing training and technology for inevitable new joiners to automate processes is therefore essential for every department to cut time to drive efficiencies and upskill the overall workforce base. This is already taking place within the education sector, with many schools beginning to implement automation technologies and analytic techniques in their curriculum, particularly in business schools and accounting, marketing and supply chain courses. Businesses that do not take notice and look to prioritize these skills as well will likely not only continue to suffer from the inefficiencies of manual processes but could also risk the attrition cost of failing to provide their employees with the modern tools that are being taught in the base curriculums of these degree programs. Automation is the first step towards analytics excellence, but its relevance doesn’t stop there. It’s through automation that leaders can unlock clear, traceable benefits for their organizations in terms of overhauled processes as well as setting them on the right path when it comes to upskilling and democratizing data. We've listed the best online courses and online class sites. This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro View the full article

Welcome to today’s enlightening Q&A session on “AI for Enhanced Analytics,” where we are privileged to host Manu Swami, the esteemed Head of Technology (Markets) at Sonata Software. Manu is a distinguished figure in the field of technology, where his leadership transcends ordinary bounds, offering strategic guidance in Customer Experience, Process Automation, Cloud, Data, and […] Source View the full article

The AWS Partner Analytics Dashboard is now known as Analytics and Insights, covering more insights for your AWS Marketplace and APN businesses than ever. We’ve streamlined the interface and added several new features to help you better understand how to grow with AWS: The new Training and Certifications tab gives you self-service access to commonly requested data on your teams’ Training and Certification achievements. These insights help you better understand your teams' strengths and identify areas needing training investment to better serve customers. We expanded the Solution Provider and Distributor tab to also highlight opportunity and AWS account details for Public Sector, Partner-Originated, and Partner Growth Discounts. New ACE Co-Sell features are now integrated into the Opportunities tab to help you better understand and manage your pipeline. View the full article

Amazon QuickSight now supports predictive analytics using machine learning (ML) models created in Amazon SageMaker Canvas, without writing a single line of code. QuickSight authors can now export data to SageMaker Canvas, build ML models, and share them back to QuickSight for consumption. This allows you to build predictive dashboards for better insights. With this new capability, you can evolve your analytics from descriptive to predictive capabilities, enabling the entire organization with a forward-looking view of the business. View the full article

Google Cloud Next kicks off tomorrow, and we’ve prepared a wealth of content — keynotes, customer panels, technical breakout sessions — designed for data professionals. If you haven’t already, now is the perfect time to register, and build out your schedule. Here’s a sampling of data-focused breakout sessions: 1. ANA204What's next for data analysts and data scientists Join this session to learn how Google's Data Cloud can transform your decision making and turn data into action by operationalizing Data Analytics and AI. Google Cloud brings together Google's most advanced Data and AI technology to help you train, deploy, and manage ML faster at scale. You will learn about the latest product innovations for BigQuery and Vertex AI to bring intelligence everywhere to analyze and activate your data. You will also hear from industry leading organizations who have realized tangible value with data analytics and AI using Google Cloud. 2. DSN100What's next for data engineers Organizations are facing increased pressure to deliver new, transformative user experiences in an always-on, global economy. Learn how Google’s data cloud unifies your data across analytical and transactional systems for increased agility and simplicity. You'll also hear about the latest product innovations across Spanner, AlloyDB, Cloud SQL and BigQuery. 3. ANA101What's new in BigQuery In the new digital-first era, data analytics continues to be at the core of driving differentiation and innovation for businesses. In this session, you’ll learn how BigQuery is fueling transformations and helping organizations build data ecosystems. You’ll hear about the latest product announcements, upcoming innovations, and strategic roadmap. 4. ANA100What's new in Looker and Data Studio Business intelligence (BI) is more than dashboards and reports, and we make it easy to deliver insights to your users and customers in the places where it’ll make the most difference. In this session, we’ll discuss the future of our BI products, as well as go through recent launches and the roadmap for Looker and Google Data Studio. Hear how you can use both products — today and in the future — to get insights from your data, including self-service visualization, modeling of data, and embedded analytics. 5. ANA102So long, silos: How to simplify data analytics across cloud environments Data often ends up in distributed environments like on-premises data centers and cloud service providers, making it incredibly difficult to get 360-degree business insights. In this session, we’ll share how organizations can get a complete view of their data across environments through a single pane of glass without building huge data pipelines. You’ll learn directly from Accenture and L’Oréal about their cross-cloud analytics journeys and how they overcame challenges like data silos and duplication. 6. ANA104How Boeing overcame their on-premises implementation challenges with data & AI Learn how leading aerospace company Boeing transformed its data operations by migrating hundreds of applications across multiple business groups and aerospace products to Google Cloud. This session will explore the use of data analytics, AI, and machine learning to design a data operating system that addresses the complexity and challenges of traditional on-premises implementations to take advantage of the scalability and flexibility of the cloud. 7. ANA106How leading organizations are making open source their super power Open source is no longer a separate corner of the data infrastructure. Instead, it needs to be integrated into the rest of your data platform. Join this session to learn how Walmart uses data to drive innovation and has built one of the largest hybrid clouds in the world, leveraging the best of cloud-native and open source technologies. Hear from Anil Madan, Corporate Vice President of Data Platform at Walmart, about the key principles behind their platform architecture and his advice to others looking to undertake a similar journey. Build your data playlist today One of the coolest things about the Next ‘22 website is the ability to create your own playlist, and share it with people. To explore the full catalog of breakout sessions and labs designed for data scientists and engineers, check out the Analyze and Design tracks in the Next ‘22 Catalog. Related Article Read Article

Amazon OpenSearch Service, with the availability of OpenSearch 1.3., now gives customers the ability to organize their logs, traces and visualizations in an application-centric view. Customers can also benefit from enhanced log monitoring support with live tailing of logs, the ability to see surrounding log data, and the ability to do powerful ad-hoc analysis of unformatted log data at query time. View the full article

Software development is fraught with risk that can run projects aground like a ship against a rocky beach. Whether it is misunderstood requirements, rapidly evolving marketplaces or old-fashioned bugs and schedule slippage, all can derail otherwise well-resourced and managed software development projects. Even when software development teams eventually ship their product and collect payment from […] The post 3 Ways API Analytics Can Help Application Owners appeared first on DevOps.com. View the full article

You can now run graph analytics and machine learning tasks on graph data stored in Amazon Neptune using an open-source Python integration that simplifies data science and ML workflows. With this integration, you can read and write graph data stored in Neptune using Pandas DataFrames in any Python environment, such as a local Jupyter notebook instance, Amazon SageMaker Studio, AWS Lambda, or other compute resources. From there, you can run graph algorithms, such as PageRank and Connected Components, using open-source libraries like iGraph, Network, and cuGraph. View the full article

Amazon Kinesis Data Analytics is now authorized as FedRAMP Moderate in US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon) and as FedRAMP High in AWS GovCloud (US-West) and AWS GovCloud (US-East). View the full article

AI-Powered Analytics Helps Organizations Expedite Review and Investigations. Tysons, VA, June 16, 2021 — Casepoint, a leader in cloud-based legal technology solutions, today announced that its built-in AI and advanced analytics technology, called CaseAssist, has been significantly upgraded to give users more insight and control over the analytics process with enhanced visualization capabilities and configuration templates. The enhancements […] The post Casepoint Unveils Latest Iteration of AI and Advanced Analytics Technology, CaseAssist appeared first on DevOps.com. View the full article

StarTree Cloud is Built on the Same Technology Used by LinkedIn and Uber to Democratize Data and Empower More Users with Fresh Insights Mountain View, CA, June 9, 2021 — StarTree, Inc. today announced the commercial availability of its “blazing-fast” cloud analytics-as-a-service platform, making it easier for organizations to share self-service analytics with their most important external […] The post StarTree Announces Commercial Availability of User-Facing Analytics Platform Raising the Bar for Speed, Scalability and Performance appeared first on DevOps.com. View the full article

Comprising lean, predictability, flow, DORA, TTM, quality, cost and other metrics, the offering provides deep visibility into value streams so teams can gain the awareness that drives improvement. ATLANTA – November 17, 2020 – ConnectALL, a leading provider of value stream management solutions, today announced the general availability of ConnectALL Value Stream Insights. Part of ConnectALL’s […] The post ConnectALL Announces Support for Flow Metrics and More with Value Stream Insights and Analytics appeared first on DevOps.com. View the full article

Amazon AppFlow, a fully managed integration service that enables customers to securely transfer data between AWS services and cloud applications, now allows you to import custom dimensions and metrics from Google Analytics into Amazon S3. You can specify the custom dimensions and metrics that you want to import while mapping source fields to the destination fields during flow set up and AppFlow will transfer records, including these dimensions and metrics, during flow execution. View the full article

Integrating the analytics capabilities into your application can be easier than you think Today, the ability to create and explore dashboards and reports, embedded directly into the apps your customers use every day, is no longer a luxury but a necessity to keep your product competitive and deliver value to users. Embedded analytics brings many […] The post Embedded Analytics Essentials: A Checklist for Success appeared first on DevOps.com. View the full article

With AWS IoT Analytics you can now configure notification for data sets that receive late data and refresh the results of the data sets with late data. Late data is data that arrives after an initial result is generated for the data set. You can configure late data notification for a data set by simply setting a time window within which late data is expected to arrive. AWS IoT Analytics sends late data notifications via Amazon CloudWatch Events when it receives late data for the data set. For more information, please visit the late data notification page in the AWS IoT Analytics user guide. View the full article