October 2022 Summaries

In their discussion on data reliability and digital transformation, Girish Bhat and Ravi Prasad emphasize the importance of leadership and strategy in driving transformation efforts. Key to this is ensuring data reliability and managing data pipelines. Prasad highlights Novartis' focus on organizational management, empowering data team members with creative problem-solving freedom, and fostering an innovative environment. He also stresses the need for alignment between business and technical teams in developing operational efficiency and enhancing customer engagement speed. Prasad shares insights into Novartis' approach to product development, technology innovation, and infrastructure investment while maintaining a focus on data reliability and trust-building across stakeholders. The company is looking to leverage ML and automate existing pipelines as they anticipate an exponential growth in their data pipelines due to digitization of services.

A survey of Fortune 1000 companies found that improving data quality and usability by just 10% could increase return on equity (ROE) by 16%, resulting in over $2 billion in additional revenue per year for the average company. To achieve this, enterprise data teams need a data observability solution with advanced AI/ML capabilities to automatically detect data and schema drift, anomalies, and lineage. Data observability provides full visibility across the data lifecycle, helping data teams predict, prevent, and resolve unexpected data downtime or integrity problems caused by fragmented data. A multidimensional data observability approach offers a single unified view of the entire data pipeline, enabling automatic monitoring and tracking of data lineage. Advanced AI/ML capabilities can automatically identify anomalies based on historical trends and help data engineers identify root causes of unexpected behaviors in their production environment.

Ravi Prasad, Global Head of Data and AI Strategy and Operations at Novartis, shares his insights on building a data-driven culture within an organization. He emphasizes the importance of partnering with business stakeholders to develop an enterprise data management program, which involves establishing a centralized data organization supported by functional business areas. Prasad outlines four foundational principles for fostering a data-driven culture: starting with business objectives and needs, co-creating data solutions, relying on change management and data literacy teams, and obtaining buy-in from top executives. By following these principles, data leaders can facilitate the necessary changes to achieve a data-driven organization.

Dolphin has over 20 years of experience in marketing and currently works at Acceldata as a Marketing Operations Manager. Before joining Acceldata, Dolphin worked with Feedback Loop, Sterling Talent Solutions, Astoria Bank (now Webster Bank), and Travers Tool. They appreciate the company culture at Acceldata, which is adaptive, dynamic, transparent, and embraces diversity. The team at Acceldata is cooperative, communicative, and focused on achieving goals. Dolphin's role involves marketing analysis, campaign planning, research, conversion tracking, ROI Analysis, and data analytics. They find working with data to be fulfilling as it provides definitive answers for decision-making. Salesforce has been a valuable tool in their career, allowing them to customize applications according to their needs. The most unique aspect of working at Acceldata is the shared mission among employees. Dolphin's personal mantra revolves around self-discipline and living with dignity and grace. Their favorite TV show is Game of Thrones, and they recently learned about the origin of the term "West Indies" during a vacation to Antigua.

Snowflake, a popular cloud data platform, reported an 83% increase in year-over-year revenue growth in Q2 of 2022. Its ability to automatically scale cloud infrastructure based on workloads is a significant differentiator. However, this feature can lead to unexpected costs if not monitored properly. Acceldata's guardrails help data teams implement Snowflake best practices by providing automatic resource alerts and monitoring various aspects of the platform, such as setting appropriate timeout limits, purging dormant users, and more. This helps prevent costly mistakes and ensures efficient use of computing resources in a highly scalable cloud environment like Snowflake.

Modern organizations are focusing on building innovative data products to accelerate innovation, improve efficiency and reduce risk. Data-driven dashboards, visualizations, reports, models, and other processes have become vital tools for companies across various industries. However, building new data products can be challenging due to issues like data sprawl, tech sprawl, talent shortage, and bad data. To overcome these challenges, many data teams are turning to data observability, which simplifies the complexity of managing data and related systems. Acceldata's Data Observability Cloud helps increase reliability for existing data products and provides resources to build more.