Building a data warehouse is an expensive affair and it often takes months to build one from scratch. There is also a constant struggle to keep up with the large volumes of data that is constantly generated. On top of that, setting up a strong architectural foundation, working on repetitive and mundane data validation tasks and ensuring data accuracy is another challenge. This puts tremendous stress on data teams and data warehouses. Data warehouse automation is intended to handle this growing complexity.

Product analytics is the most critical and complex task for any product team. There are thousands of data points that have to be analyzed carefully while setting up the product analytics foundation and it enables product teams to use data to track, visualize, and analyze user engagement and behavior that can be used to improve and optimize a product experience. However, managing large data workloads can be very challenging as not all data that is collected can be directly used for analytics.

Data Warehouse Automation helps IT teams deliver better and faster results by getting rid of repetitive design, development, deployment and operational tasks within the data warehouse lifecycle. With automation, organizations can accelerate the data to the analytics journey, work more effectively with large amounts of data and save cost. Join this session with Darshan Wakchaure, Global Data & Analytics Competency Head, Tech Mahindra as he shares his insights on the key benefits of Data Warehouse Optimization and how to achieve Data Warehouse Automation at scale.

The collaboration between teams of data scientists, data engineers, data analysts, operations, and product owners can help an organization scale new heights. Each of these roles needs to be in sync in order to use data in the most efficient manner.

Business metrics guide founders and decision-makers to make the right call to push their ventures towards their goals. In the initial launch of a startup, the focus tends to be on revenue and profits. However, if a startup wants to scale up, it is important to broaden what metrics and key performance indicators (KPIs) are monitored at each stage, so they can grow the business by using data instead of just intuition.

Modernizing your data stack can help your organization tackle various demands and challenges caused by complex data problems. The session will uncover the building blocks of a modern data stack and discuss how you can maximize benefits with a minimal investment.

One of the key goals for data teams in 2022 is to drive business impact while reducing development and maintenance costs. Setting up or migrating to a modern data stack will be a key factor for data-driven businesses to achieve faster growth. Attend this session to take a look at the biggest data trends of 2022.

The Data Warehouse is the heart of any Modern Data Stack. Organization-wide data is essential in order to create a single source of truth. The question here is whether you should build a data warehouse, data lake, or a data lakehouse. Join this session to explore the options that are available and how you can evaluate multiple vendors for your use case.

Business metrics guide founders and decision-makers to make the right call to push their ventures towards their goals. In the initial launch of a startup, the focus tends to be on revenue and profits. However, if a startup wants to scale up, it is important to broaden what metrics and key performance indicators (KPIs) are monitored at each stage, so that they can grow the business by using data instead of just intuition.

Victor Jager, Head of Business Performance at Lovebox, talks about how before Hevo, most of Lovebox's data rested in disparate sources. There was no way to perform transversal analysis and gain insights into the customer's journey. Hevo has enabled Lovebox to unify eCommerce, website, product, and support data from Shopify, Google Analytics, Amplitude, Zendesk, and Google Sheets to a BigQuery Data Warehouse.