Systems | Development | Analytics | API | Testing

Watch a detailed product demo of the Hevo Data platform from Arun Chaudhary, Senior Sales Engineer at Hevo Data.

For the past 25+ years, Kirk Mower, Cyber Security and Data Integrity Leader at TasNetworks, has been on a mission to scale Data-Driven Decision Making in a sector marked by the vast amount and complexity of data - Australian Utilities Space. In this engaging fireside chat, Kirk talks about the significance of data in the utility sector, his role as a data leader, and his thoughts on the Modern Data Stack. Check out this delightful data talk on our new show - Mavericks of Data.

eCommerce analytics involves tracking a wide range of metrics relating to the complete journey of the customer, right from discovery to acquisition, conversion, retention and advocacy. Analytics lays a foundation framework for any eCommerce business and it helps them understand which marketing and sales initiatives are working, those that aren't, and the areas that have a great potential for growth.

The healthcare industry has always been flooded with a huge amount of complex data, coming in at a very rapid pace. The industry also faces constant challenges such as the growing demand for services, rising costs and a workforce that is struggling to meet the needs of its patients.

The 'data lakehouse' is quickly becoming popular in the data analytics community. Data lakehouse architecture combines the benefits of a data warehouse and a data lake. It aims to merge the data warehouse’s data structure and management features along with the flexibility and relatively low cost of the data lake. Watch this panel discussion to learn how the data lakehouse can address the limitations of the data lake and data warehouse architecture to deliver significant value for organizations. Explore why the data lakehouse is an ideal option for enterprise data storage initiatives.

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.