Resources

Intelligent Process Automation: Boosting Bots with AI and Machine Learning

Data Robot

Across all sectors, companies are learning that they can transform their businesses by embracing Intelligent Process Automation, or IPA. With the pairing of AI and RPA, IPA adds a new layer of intelligent decision-making processes to automated RPA tasks. By automating repetitive work, and adding the ability to automate intelligent decision making, intelligent automation frees up your most valuable resources – your employees – to spend more time on higher value and more strategic work. But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. In our ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning.

6 Steps to Improving Your Application’s Analytics Experience

No one designs bad dashboards and reports on purpose. So why do so many applications have terrible analytics experiences? Download this ebook for secrets to creating dashboards and reports your users will love.

Nine Developer Enablement Practices to Achieve DevOps at Enterprise Scale

Datadog

In this eBook, Christian Oestreich, a senior software engineering leader with experience at multiple Fortune 500 companies, shares how a metrics-driven mindset can dramatically improve software quality and enable DevOps at enterprise scale.

Design Thinking for Product Teams: Leverage Human Insight Throughout Development

UserTesting

Product teams must increase their exposure hours with customers—seeing and hearing them. Human insights and the design thinking framework can be applied to your development cycle to help you build better products and experiences for your customers.

The Complete Guide to Distributed Tracing

LightStep

Distributed tracing is a diagnostic technique that reveals how a set of services coordinate to handle individual user requests. Distributed tracing helps enable loosely coupled work across teams for fast, independent problem-solving.

How Banks Are Winning with AI and Automated Machine Learning

Data Robot

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

4 Approaches to Data Analytics for Your Application

Selecting the right solution for your embedded analytics is crucial to the success of your application. But with so many solutions and vendors, how do you make this critical selection? Learn about the different approaches to analytics and the pros and cons for each.

Add User Tests to Your Agile Process: Reduce Risk in Shipping New Products

UserTesting

Agile has become the go-to methodology for companies that want to reduce the risk involved in shipping new products. But how do you prevent building items nobody wants? If you wait to get user feedback until after development, then you’ve waited too long.

How Banks Are Winning with AI and Automated Machine Learning

Data Robot

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

Democratizing AI for All: Transforming Your Operating Model to Support AI Adoption

Data Robot

Democratization puts AI into the hands of non-data scientists and makes artificial intelligence accessible to every area of an organization. With the emergence of enterprise AI platforms that automate and accelerate the lifecycle of an AI project, businesses can build, deploy, and manage AI applications to transform their products, services, and operations. But in order to reap the rewards that AI offers, it is essential that businesses first address how their organizations are set up, from their people to their processes. Democratizing AI through your organization requires more than just software. It may require changing your operation models and finding the right guidance to realize the full breadth of capabilities.

Humility in AI: Building Trustworthy and Ethical AI Systems

Data Robot

AI is becoming ubiquitous. More and more critical decisions are automated through machine learning models, determining the future of a business or making life-altering decisions for real people. The number of critical touch points is growing exponentially with the adoption of AI. In this ebook, we explore the concept of humility in AI systems and how it can be applied to existing solutions to ensure their trustworthiness, ethicality, and reliability in a fast-changing world.

5 Signs It's Time to Replace Your Homegrown Analytics

If you built your analytics in house, chances are your basic features are no longer enough for your end users. Is it time to move on to a more robust analytics solution with more advanced capabilities? Follow this free guide for tips on making the build to buy transition.

How to Choose the Best Embedded Analytics Solution to Modernize Your Application

If you are looking to modernize your application to improve competitiveness, then one of the quickest wins you can have is to embed sophisticated analytics that will wow your existing and prospective customers.

Build vs Buy: 10 Hidden Costs of Building Analytics with UI Components

Many teams, as a logical first step, choose to build their own analytics with the help of UI components. But eventually you’ll find that doing it yourself and at scale has hidden costs. Consider these 10 factors when deciding whether you should build analytics features with UI components.

A Guide to Better Data Quality

Without high quality data that we can rely on, we cannot trust our data or launch powerful projects like personalization. In this white paper by Snowplow, you'll learn how to identify data quality problems and discover techniques for capturing complete, accurate data.

BI Buyers Guide: Embedding Analytics in Your Software

The business intelligence market has exploded. And as the number of vendors grows, it gets harder to make sense of it all. Learn how to decide what features you need and get an evaluation framework for every technical and non-technical requirement you could imagine.

The Essential Guide to Building Analytic Applications

Embedding dashboards, reports, and analytics in an existing application presents some unique opportunities—and poses unique challenges—to software teams. Download this eBook to hear 16 product experts share insights on business intelligence, UI/UX, security, and everything that goes into building a successful application with analytics at its core.

How Top Engineering Leaders Build High-Performance Teams That Deliver Results

The primary responsibilities of Engineering Leadership - essentially, VPs of Engineering and CTOs - are building high-performance teams and delivering high-quality products on time, which together drive business results.

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