February 2021 Summaries

Developing a single customer view and effectively identifying users has gained prominence in the analytics community due to increasing user expectations for personalized digital experiences and greater control over data usage. Privacy regulations, browser tracking preventions, and ad blockers pose challenges to user identification, prompting companies to reconsider their data collection strategies. This has led to a shift from third-party to first-party data collection, particularly as browsers like Safari, Firefox, and Edge limit third-party cookie usage. User stitching is a key process in creating a single customer view, which involves capturing first-party user identifiers, understanding their relationships, and linking events across platforms. Snowplow, a behavioral data collection tool, facilitates this process by providing various identifiers and supporting enrichment of data in real-time and within data warehouses. To build an effective customer view, companies must develop a hierarchy of identifiers, create mapping tables to link user activity, and integrate these insights into their data models, ultimately enabling them to connect marketing efforts to user actions and enhance customer understanding.

Understanding and mapping the complex customer journey across various digital and offline channels is challenging but essential for creating a comprehensive single customer view, which can greatly benefit organizations, especially those with intricate customer interactions. Snowplow offers a solution by enabling flexible integration of behavioral data from diverse sources such as websites, apps, email, social media, and offline interactions like phone calls and in-store visits. This approach allows companies to start small by focusing on specific projects where cross-channel data integration can drive significant business results before expanding to create a holistic view of customer interactions. Unlike traditional analytics vendors, Snowplow provides the flexibility to capture event-level data without immediately determining user identities, allowing companies to evolve their understanding of customer behavior over time. By leveraging Snowplow’s trackers and integrations, businesses can collect and combine data from multiple engagement channels, thus enhancing their ability to deliver personalized experiences, optimize marketing efforts, and improve user engagement through a data-driven approach.

Over the last decade, the utilization and management of data have become crucial to business success, with high volumes of data flowing from various sources creating complexity in data management. Effective data management, as defined by Gartner, involves practices and tools that ensure consistent access and delivery of data across enterprise applications and processes, which is vital as 63% of businesses use data to mitigate risks. The importance of data management lies in its ability to increase data quality and accessibility, enabling accurate insights for business decisions, as mismanaged data can lead to significant losses, such as losing customers due to inaccurate information. Data management systems enhance productivity by ensuring data is reliable, interpretable, and compliant with regulations like GDPR and CCPA, thereby preventing costly data breaches. Key benefits include increased data productivity through structured governance and improved compliance by validating data formats for easier analysis. Best practices in data management emphasize the importance of data security, creating a single source of truth to ensure consistent data use across organizations, and fostering a data-driven culture. Implementing a comprehensive data management strategy aligns data goals with business objectives, employs the right tools, builds skilled teams, and ensures data accessibility, which can help firms stay competitive and manage risks effectively. Organizations like Strava have demonstrated success in democratizing data access, improving analytics, and gaining deeper insights through effective data management strategies.

Businesses are increasingly relying on cloud infrastructure, with cloud services projected to process 94% of workloads, due to their flexibility, speed, agility, and cost-effectiveness. However, major providers like AWS, Azure, and Google Cloud are not immune to outages, which can lead to significant data downtime costs, estimated at $5,600 per minute by Gartner. Notable outages in 2020 included a six-hour Azure outage due to overheating, a 14-hour AWS outage caused by server issues, and a Google Cloud outage impacting identity management. These incidents highlight the unpredictability and costliness of cloud outages, which can affect data quality, application availability, brand reputation, and revenue. To mitigate risks, businesses can adopt strategies such as multi-cloud, hybrid cloud, and multi-region architectures to ensure data redundancy and availability. Snowplow has developed an Outage Protection solution to minimize data loss during outages by rerouting traffic to backup regions, demonstrating the importance of proactive measures in safeguarding against data disruptions.