Pre-processing and exploring data, building and deploying models and turning those scoring values into an actionable insight can be overwhelming. A recent survey shows that for data scientists, the many tasks they spend their time working on are very different from the tasks they actually want to prioritize. This disparity can feel wide, especially when coworkers or internal clients think you can do it all.
The expectations for those who work with data and analytics can be as large as the potential impact that can be made in organizations. The survey shows that the biggest hurdles faced in their project work include lack of support from their organization, dirty data and results not being used by business decision makers. Using AI throughout the analytics journey can help data and analytics experts in overcoming these obstacles and making work more enjoyable, productive and impactful.
The ability to use technology to help accelerate data-driven decisions has never been more important. Data scientist Ken Jee states, “I think a unique combination of skills makes data science such an integral aspect of businesses these days. At this point, every business is a technology company in some respect and every company collects volumes of data, whether they plan to use it or not. There’s so much insight to be found in data.”
Working in data and analytics should be full of potential, not pitfalls. If teams are going through endless data cleansing or facing barriers to deployment, check out these three ways to speed up analytics workload.
Drag and drop wherever you can
No keyboard? No problem. A mouse can do so much to accelerate analytics, and users can leave the programming for their favorite analytics activities. Look for ways to use drag and drop functionality and highly visual environments for data management, data visualizations or model development to get the most impact out of the minimum effort.
Use automated insights for critical context
Automated insights provide easy-to-understand information about data and are powered by natural language generation with quick drag-and-drop functionality. Using insights based on analytics rather than influenced by intuition can help users, management and others at your organization make strategic decisions. Relying on automation to get those insights faster allows users to dive deep into data and pull out what’s most important to act quickly. Just make sure it’s governable and free from bias.
Do not forget model governance and performance
Once you’re near the end, do not forget model governance and performance. If your goal is to create models and find interesting insights, that’s a journey within itself, but getting others to use those insights is a different story. Checking in on what’s going on with your models after deployment quickly validates your work’s accuracy, allowing you to influence others to make reliable data-driven decisions.
Implementing AI is easier and faster across your workload with SAS® Viya® on Microsoft Azure. It’s also easier to access and can be a shared space for DataOps, analytics and ModelOps teams.
Join a community of SAS users by visiting the Microsoft Azure Marketplace and discover the capabilities of SAS Viya.
Originally posted here. Reposted with permission.
About the author:
Briana Ullman’s focus is on business intelligence and augmented analytics at SAS. She is passionate about lowering the barriers to using analytics and helps create a live video series that brings together experts across SAS. Briana is an alum of North Carolina State University and currently lives in Denver, CO. Outside of work, you can find Briana listening to an almost-alarming number of podcasts and daydreaming about her next hike.