Data Strategy Best Practices

Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.

The economic model and elegance of cloud environments are motivating companies to assess their existing on-premises data warehouses and modernize their enterprise information environments. However, confusion about what is meant by “modernization” has led some to believe that “lifting and shifting” their on-premises implementation to a cloud environment is the default approach. In these cases, the results are mixed—while the system has technically been moved to a modern cloud platform, by no means is it modernized.

Fanatics, a popular sports apparel website and fan gear merchandiser, needed to ingest terabytes of data from multiple historical and streaming sources transactional, e-commerce, and back-office systems to a data lake on Amazon S3. Once ingested, the data would be analyzed to better identify, predict, and fulfill customer needs related to the products Fanatics offers in over 300 online and offline stores. To accomplish this, Fanatics chose Attunity Replicate, a software solution featuring continuous data capture (CDC) and parallel threading for streaming data in real time from multiple sources into a data lake on Amazon S3. The data can then be consumed in Apache Kafka for real-time analytics. Attunity helps Fanatics avoid the heavy lifting of manually extracting data from disparate sources and enables the organization to see results in real time.

In order to maximize the value of your data and democratize AI across the organization, citizen data scientists are emerging and implementing machine learning models into business analytics applications such as Microsofts Excel, Power Apps, Power BI, Reporting Services and more. In this webinar, attendees will learn more about how their organizations can add AI to BI, making more predictive decisions along the way.