Welcome to the Velocity monthly newsletter for July 2020 where we share our latest use cases, upcoming events, product updates, and more...
INDUSTRY VIEW POINTS
Mass adoption of AI in financial services expected within two years Why will we see mass adoption of AI in financial services within the next two years? For many reasons. It will allow financial institutions to reimagine their customer experience, offer greater personalisation, better utilise data insights, aid in financial inclusion, and help manage fraud and risk. The benefits are endless. We now need to start building our teams with deep expertise in both AI and financial services. Read the article.
How CIOs and CDOs can transition to new finance and budgeting processes that support innovation Agility can't be siloed. To be truly agile, cross-functional teams need to work together towards change and innovation. We see this often in organisations,especially with finance teams separated from the core business function. Which is crazy considering nothing can function without the finance team. Great insight here on how to implement a successful agile strategy and how business leaders can change with finance, allowing space for creative funding, and further innovation. Read the article.
Velocity Business Solutions has been awarded as Snowflake’s Emerging Systems Integrator Partner of the Year for Asia Pacific at the recent Snowflake Virtual Partner Summit 2020. This award recognises Snowflake’s top strategic partners in the irrespective ecosystem specialisation for FY20, and Velocity Business Solutions truly embodies that by playing a key role in building and innovating inside of Snowflake. Read press release.
ANALYTIC PROCESS AUTOMATION
Analytic Process Automation (APA) is the technology that allows anyone in your organization to easily share data, automate tedious and complex processes, and turn data into results. With Analytic Process Automation, anyone can unlock predictive and prescriptive insights that drive quick wins and fast ROI. Analytic Process Automation marks the maturation of data and analytics software, which used to consist of distinct markets including analytics, business intelligence, data science, and machine learning tools. APA converges three key pillars of automation and digital transformation to enable the democratization of data and analytics,the automation of business processes, and the upskilling of people for quick wins and transformative outcomes. Learn more.
What’s New in DataRobot Release 6.1?
Use Case Value Tracker. The new Use Case Value Tracker is a centralized hub for collaborating with team members around a single AI initiative, end-to-end. Use Case Value Tracker enables you to manage your machine learning projects and understand the associated value at every step. You can organize all your DataRobot assets around a given use case. For example, you can group all of the datasets, models, deployments, and apps associated with a customer acquisition initiative. You can also see metrics on the realized business value of your use case over time. This helps you understand the true ROI of your enterprise AI.
Location AI. Location AI allows your predictive models to understand spatial relationships between observations in a dataset. For example, one of the best ways to predict home prices is to look at the prices of other homes in the same neighborhood.
Profit Curve and Payoff Matrix. Profit Curve and Payoff Matrix so you can visualize the payoff from good predictions and the costs associated with bad predictions. You can set up a number of alternative payoff matrices, inspect the resulting profit curve, and even compare alternative profit curves on the same chart. Armed with these insights you can decide the most profitable option to take. DataRobot's Profit Curve and Payoff Matrices help you tune your models for business impact not just accuracy.
MLOps Challenger Models. Champion/Challenger models in MLOps Release 6.1 enable you to test and compare your production models with alternatives to determine the most appropriate and accurate as business conditions change. This allows you to continue to perform the same rigor of analysis on your production models as you did during training. You can replay predictions against challenger models to analyze accuracy and performance over the same time period. When a challenger beats out the current champion, you can instantly switch out your old model with absolutely no service interruption.
Anomaly Detection for Automated Time Series. In this release we introduce Automated Time Series Anomaly Detection. Without the need to specify a target variable, you can now train time series models in a fully unsupervised mode. DataRobot automatically selects, builds, tests, and ranks a diverse set of anomaly detection models and uses a novel Synthetic AUC error metric to help you understand which model is best for your specific use case. DataRobot's Automated Time Series Anomaly Detection also provides all of the automated insights and visualizations to help you explain every model and drill into individual anomalies to understand causal factors.
More Explainability and Trust Features. We have introduced an automated Data Quality Assessment and Data Quality Handling Report that surfaces and reports on issues such as missing values, outliers, target leakage, and more.
Other New Features and Enhancements. Release 6.1 is jam-packed with new capabilities that you’ll want to take advantage of immediately, including Spark SQL data prep and other enhancements to Paxata, Visual AI (now enabled by default) with Prediction Explanations, tight integration with popular third-party products including Snowflake, MS SQL Server, and Tableau and dozens of usability improvements including a new UI light mode theme.
Qlik named Snowflake Data Engineering Partner of the Year
Qlik has been named the Snowflake 2020 Technology Partner of the Year in the Data Engineering category at Snowflake Partner Summit. “We continue to see fast growing adoption of analytics in cloud data platforms, and are excited about being named a Snowflake Technology Partner of the Year,” says Qlik technology alliances SVP Itamar Ankorion.
“Qlik’s continued investment towards Snowflake ensures its integrations and solutions adhere to our best practices so customers have a great experience. We look forward to continued momentum through the collaboration between Qlik and Snowflake,” says Snowflake product manager Harsha Kapre. Read article.
Executive Insights Center
The Qlik Executive Insights Center keeps business leaders current on the latest trends and strategies in data integration, data analytics, and data literacy. Learn more.
Snowflake unveils the data cloud so Organizations can connect, collaborate, and deliver value with data Snowflake, the cloud data platform, today unveiled the Data Cloud – an ecosystem where thousands of Snowflake customers, partners, data providers, and data service providers can break down data silos, derive insights, and deliver value from rapidly growing data sets in secure, governed, compliant, and seamless ways. Snowflake also announced new product features for Snowflake Cloud Data platform – the technology that unites this data ecosystem and powers the Data Cloud. Snowflake has also developed a multitude of new features for Snowflake Cloud Data Platform, including:·
Snowsight – Anew analyst experience within Snowflake to execute queries and commands against Snowflake, including SQL auto-complete, collaboration, visualizations, and dashboards for a streamlined experience. (Public Preview)
Dynamic Data Masking – Create masking policies for data, so users see data with varying levels of fidelity based on their permissions. (Private Preview)
External Tokenization – Integration with third-party tokenization solutions for increased protection of sensitive data. (Private Preview)
Search Optimization Service – Dramatically improved performance for point lookup queries on large tables of data. (Public Preview)
External Functions – Use Snowflake to call external services for richer query support and to build robust data pipelines that integrate with third-party libraries or services. (Public Preview)
Java UDFs – Leverage Snowflake to run business logic developed in Java, bringing computation closer to data. (Private Preview)
Organizations – Centrally operate and manage multiple Snowflake accounts with ease, including provisioning and managing accounts across multiple clouds. (Private Preview)
Data Exchange – Create your own data exchange to seamlessly and securely share live, governed data with other business units, partners,suppliers, or customers. Eliminate data silos in your ecosystem, empowering your organization and business partners to securely access your data sets without having to move, copy, or transfer that data. (Public Preview)
Snowflake Data Marketplace – Discover and access third-party data sets that are ready to query from your Snowflake account. Snowflake Data Marketplace allows consumers to access a single, live copy of data. No copying files, no moving data, and no delays. (Public Preview)
The Velocity Coastal Rowing masters team entered their first competitive race on Saturday 13th June. The race was a part of the Victoria Rowing Club 2020 Duff & Phelps VRC Race Series. This is an eight-kilometer open water course starting and ending in Deep Water Bay.
The Velocity team performed admirably with a quick time of 52 minutes 53 seconds but were beaten into second place by the eventual winners of Ferrari 20, clocking in at 51 minutes 20 seconds.
If any Velocity customers or staff who would like to participate or join the Velocity coastal rowing team, please contact Paul Kidman at +852 9033 6916 or email@example.com.