The Warehouse

Today’s businesses continue to strive for data maturity and to create a culture that is data-driven and data-literate. But the perception remains that starting such a journey is expensive and time-consuming. There is a belief that a combination of high upstart costs and lengthy implementation time prevents the business from seeing any near-future value in its data and analytics investment. The reality, however, is that today’s cloud-based data platform technologies, specifically the triumvirate of Snowflake (data storage and compute), Matillion (data loading and transformation), and ThoughtSpot (data analytics and insight), enable rapid analytic ability with minimum initial inv...

The time it takes from beginning a data cloud initiative to realizing any of tangible value can be years.

After all, there is a lot to consider. First you need to ensure that there is business alignment, between the business and I.T., on what the short, mid, and long-term objectives are. Second, there is the small task of assessing your entire data estate. It is important to figure out with each vendor which modern data stack configuration, workload option, availability zone, and pricing plan is the optimal configuration for you.

Third, you need to build a migration plan, defining a strategy for each possible candidate analytical application and data set, that will drive value to the business ...

We have all heard the notion that data should be viewed as an asset, the “new oil”, a crucial business resource for the enterprise. When viewed in this context, a business’s investment in data must be assessed by the quality of its return. Measuring this return is more than just “how many cool-looking dashboards” you can now create; rather, a CTO/CDO must look beyond the technical capabilities and determine if data is having a true impact on the business culture. This means evolving a BI and Data Science community that engages employees at all levels of the organization. The path to this positive return is creating a modern data culture that permeates the enter...

The data vault has long been viewed as a model best suited for historical and archival enterprise data. Its “insert only”, business-process approach to raw, unadulterated data is ideal for low-maintenance storage of all enterprise-generated information from all systems. Use cases for data vaults have traditionally revolved around historical tracking and auditing . . . however, the perception has largely been that it is ill-suited to analytics due to its many-to-many relationships and dispersed structure. In fact data vaults are often used as a “lightly modelled stage” for traditional star-schema data warehouses.

But the data vault may be best suited for a use case that...

The two leading ETL/ELT tools for cloud data migration are Talend and Matillion, and both are well-positioned for moving and transforming data into the modern data warehouse. So if you’re moving to any type of cloud-hosted DW, whether it is a cloud-dedicated warehouse such as Snowflake, or part of a larger cloud platform such as AWS Redshift, Azure SQL Data Warehouse or Google BigQuery, which tool should you use to move your existing on-prem data?

Both Talend and Matillion can source any kind of on-prem data and land it in a cloud-hosted data environment. They can also move data to and from AWS’s cloud data-storage S3 as well as Azure’s Blob storage (which can be used to s...

Growth companies today rely on Enterprise Resource Planning (ERP) systems to manage their daily operations and collect and retain vital business data. The ERP space continues to evolve . . . cloud-hosted ERP, AI-based automation, digital transformation, etc.; eventually an organization will find itself upgrading or migrating to a more modern enterprise platform. This move will likely involve a Systems Integrator (SI) specialist to drive the effort, and an SI’s focus may need to incorporate more than just the operational systems at hand . . . the organization’s approach to data integration and analytics should be accounted for as well.

Your SI and internal migration team will init...

When planning your integrated enterprise data environment, it is impossible to understate the importance of master data management. Much has been written about MDM, and it encompasses a broad range of (mostly non-technical) disciplines that are beyond the scope of a single blog entry. Here we will provide a broad overview of the four main areas of MDM to start your journey towards enterprise data governance. We will also examine the relationship between MDM and recent developments around other enterprise management programs such as Product Information Management.

What is master data management? Quite simply it is the administrative oversight of organizational “data as an asset” to...