Exponentially growing data volumes create myriad challenges. Chief among them is the risk to data already stored in silos. If companies continue to operate in analytics environments with no central point of data access, all the new information being collected will exacerbate already fragmented views of the company’s business.

Not only will these organizations be challenged to extract the value from their existing data investments, but the stunted view could also expose them to unseen risks as new data continues to roll in.

That’s why a centrally governed semantic layer that is performant at scale against Big Data—and can readily accommodate new sources—is a growing use case for many organizations.

In this thriving data-rich universe, insurance companies have to collect huge amounts of data with the goal of performance optimization, risk mitigation, and meeting the rising expectations of consumers. Hence, data is key to remaining competitive. For ages, insurance companies have been aggregating a great deal of data, but face challenges, preventing them from making the most of the power offered by analytics strategy and data governance frameworks.

Data access and security continue to be a pillar in any organization that has decided to be data-driven. But we still rely on Excel to do even basic analyses.

Meanwhile, downloading company data to a desktop has proven repeatedly to be an enormous risk for organizations. For every data breach that happens there is an average $4 million financial cost. Are the cost and risk acceptable to your organization?

Many data professionals struggle to get results from their data; business applications require extensive training and users often lack the skills to get the best out of the myriad of tools at their disposal. In this session, we will help you solve the data literacy challenge by showing how even the most diverse set of business users can access the right information at the right time thanks to an intuitive self-service data analytics platform that scales for any data, any user and any analytics.

What if care team analysts and front-line workers could harness data and analytics to diagnose and understand critical issues like hospital readmissions? Could they do it, without relying on complicated analysis techniques, or waiting for others to prepare the analysis for them? They can with the Pyramid Decision Intelligence Platform.

How can BI developers use data science techniques to enrich existing analytic dashboards—without sacrificing ease of consumption? And how can they do it quickly, without relying on data scientists to write complex algorithms and apply them to their data sets?

With Pyramid, it’s all possible in a single, integrated platform that makes it easy for non-data scientists to enrich existing dashboards with advanced analytics.

See how a business analyst at a leading financial services organization can adapt to change outside of her control in mere minutes with some powerful tools: Analyze Structure and Change Data Source.

This organization relies on the merchant/partner dashboard and reports to manage the incentive program. The organization has adopted a new cloud strategy and has recently migrated from a Postgres on-premises database to a Snowflake cloud-based database. Somehow, the business analyst got left out of the discussion. No matter how organized things may seem, this situation happens quite often.

How would your analytics strategy adapt in such a situation?

See how a business analyst at a large telecom organization can quickly (seconds, not days) understand the details behind key payment methods (electronic check, credit card, bank transfer, other) using Pyramid’s Explain feature.

See how a business analyst at a large retail organization with brick-and-mortar and online stores can quickly build a report to show which promotions are performing best, and in which channels.

We demonstrate how a retail business manager can capture data, prepare instant analysis, and share it with her team—all in 15 minutes.