![Boosting Enterprise Adoption of Self-Service Data Analytics](/img/9a48112fd.jpg?width="600&height=400&fit=crop&q=60)
![The Tipping Point that Drives the Need for Enterprise Report Management](/img/9cff74286.jpg?width="600&height=400&fit=crop&q=60)
![Modernizing the Enterprise Data Warehouse with the Cloud](/img/f73624119.jpg?width="600&height=400&fit=crop&q=60)
Latest Research
Boosting Enterprise Adoption of Self-Service Data Analytics
New organizations, processes, and technology approaches are required to enable people throughout the organization to work with data
![How to Determine the Right Growth Metrics for Self-Service Adoption](/img/b5672a662.jpg?width="600&height=400&fit=crop&q=60)
How to Determine the Right Growth Metrics for Self-Service Adoption
Defining a set of metrics will help to keep everyone involved with the mission to drive a data culture focused on accomplishing the right goals and communicate progress.
![Technology Considerations for Enterprise Self-Service Adoption](/img/66f5d64a6.jpg?width="600&height=400&fit=crop&q=60)
Technology Considerations for Enterprise Self-Service Adoption
Technology plays a key role in driving – or deterring – enterprise adoption of self-service data analytics
![Self-Service Analytics Requires New Processes and New Governance](/img/325a932da.jpg?width="600&height=400&fit=crop&q=60)
Self-Service Analytics Requires New Processes and New Governance
New data management processes for self-service will focus on governing data, managing output data and analytics models, and managing analytics workflows
![Grow Your Data Culture: A Mindset for New Enterprise Organizations](/img/571a374cf.jpg?width="600&height=400&fit=crop&q=60)
Grow Your Data Culture: A Mindset for New Enterprise Organizations
Our research shows that each company’s unique data culture is the leading factor in driving enterprise adoption of self-service data analytics
The Tipping Point that Drives the Need for Enterprise Report Management
A report management hub is a software platform that is independent of report development tools and provides unified visibility of each reporting tool’s available reports
Modernizing the Enterprise Data Warehouse with the Cloud
Three concepts are relevant for the future of your data warehouse: what it means to be “enterprise,” what it means to be “modern,” and the impact of multi-cloud
![Accelerating Analytics with Alteryx and Snowflake](/img/ad9e8532d.png?width="600&height=400&fit=crop&q=60)
Accelerating Analytics with Alteryx and Snowflake
The strengths of Alteryx and Snowflake combine to optimize analytics while improving user experience
![Amplify Business Analytics with Your Operational Data, CDC, and a Real-Time Data Lake](/img/01eb2ccaa.png?width="600&height=400&fit=crop&q=60)
Amplify Business Analytics with Your Operational Data, CDC, and a Real-Time Data Lake
With a few clever steps in technology architecture, organizations can deliver timely data to analysts, BI teams, and data scientists without needing to hire teams of data engineers or push deadlines
![Streaming Operational Data to Cloud Data Lakes](/img/105db5ee3.jpg?width="600&height=400&fit=crop&q=60)
Streaming Operational Data to Cloud Data Lakes
This white paper clarifies the importance and value of populating a cloud-based data lake with streaming operational data, leveraging database replication and modern data integration techniques
![Reference Architecture for Alteryx on Azure](/img/10f8375df.png?width="600&height=400&fit=crop&q=60)
Reference Architecture for Alteryx on Azure
This technical white paper presents the process, platform components and architecture patterns for an analytics environment
![How Alteryx on Azure Accelerates the Modern Analytics Lifecycle](/img/36d464cd7.png?width="600&height=400&fit=crop&q=60)
How Alteryx on Azure Accelerates the Modern Analytics Lifecycle
The Modern Analytics Lifecycle with Alteryx on Azure can transform companies into data-driven organizations that empower business analysts and data scientists
![Reference Architecture for Alteryx on AWS](/img/840e62b21.png?width="600&height=400&fit=crop&q=60)
Reference Architecture for Alteryx on AWS
This paper provides a straightforward reference architecture for IT architects and senior personnel interested in deploying the self-service platform Alteryx on AWS
![Making the Modern Analytics Lifecycle a Reality with Alteryx and AWS](/img/ba7287992.png?width="600&height=400&fit=crop&q=60)
Making the Modern Analytics Lifecycle a Reality with Alteryx and AWS
The Modern Analytics Lifecycle on AWS can transform companies into data-driven organizations that empower their business analysts and data scientists
![The Six Pillars of Enterprise Self-Service Data](/img/9a347a19a.jpeg?width="600&height=400&fit=crop&q=60)
The Six Pillars of Enterprise Self-Service Data
These essential six pillars of self-service data serve the needs of three different groups within the organization: business users, IT development and the enterprise data management cult
The Platform Approach for Enterprise Self-Service Data Success
A single platform that incorporates all six pillars is the ideal approach for enterprise self-service data capabilities rather than an ecosystem of various tools and products.
Leading Business Applications for Self-Service Data
Several important initiatives clearly benefit from self-service data’s key value propositions, such as customer 360 programs, compliance and GDPR, and ERP consolidations
Achieving Enterprise Self-Service Data
A successful enterprise self-service data strategy is dependent on a comprehensive set of core capabilities deployed together to empower business users with data
![The Core Components of an Enterprise Self-Service Data Strategy](/img/bc204657b.jpg?width="600&height=400&fit=crop&q=60)
The Core Components of an Enterprise Self-Service Data Strategy
To implement an enterprise self-service data strategy, six pillars are the foundation required to fully empower users with data
![Why Data Sampling Is Essential to Data Wrangling](/img/eb95ececb.jpg?width="600&height=400&fit=crop&q=60)
Why Data Sampling Is Essential to Data Wrangling
Data sampling is essential to data wrangling and the future of working with data in every company striving to be more effective with their data assets