Enterprise data warehouses (EDWs) are often deemed the most valuable asset in the data center, serving as the backbone of the business. The ongoing insight gained from these solutions has justified the significant up-front capital investments and ongoing operational costs, but the rigidity of the traditional EDW is forcing organizations to reevaluate their approach to analytics and business intelligence.
While legacy EDW solutions were all about throwing as much computational power as possible at a relatively static data set, with the inflow of new and valuable sources of data and the emergence of all-encompassing analytics initiatives, the success of today’s EDW solutions depends more on operational and resource agility than raw horsepower. Being able to dynamically adjust to the needs of the business, integrate into operational processes, and quickly react to emerging opportunities can place an organization at a distinct competitive advantage. Today’s EDW solutions must act as a global repository of information, provide the agility to scale up or down on demand, and seamlessly integrate with other analytics tools and services used throughout a data-driven organization.
Over the last two years, Enterprise Strategy Group has conducted detailed studies quantifying the economic value of Google data analytics services. The first evaluated Google BigQuery compared to on-premises Hadoop and AWS redshift. The second focused on Google DataProc compared to DIY Spark and Hadoop approaches. I’m happy to share the next iteration of our economic analysis, extending the BigQuery study to incorporate a comparison to legacy enterprise data warehouses, both on-premises and in the cloud.
Through publicly available pricing and in-depth qualitative customer interviews, ESG was able to assert a base set of assumptions that power a dynamic model, incorporating up-front capital investments, deployment and migration costs, expected monthly cloud costs, administrative costs, and operational costs associated with legacy on-premises EDWs, cloud-based EDWs, and Google BigQuery.
The crux of the results show organizations can save up to 52% by using BigQuery over on-premises EDWs and up to 41% over cloud-based EDWs. Unlike legacy on-premises EDWs, BigQuery provides organizations with the key abilities that are essential to delivering a modern EDW solution, most notably the ability to integrate across other Google Cloud Platform services, including its market leading AI-based solutions and services. Although we did not call it out directly in the published report, ESG’s models indicate that the savings achieved by migrating an on-premises EDW solution to Google BigQuery may actually be more cost effective than simply continuing to operate an existing on-premises EDW solution. Stay tuned for more as we continue to expand our analysis throughout the year!
In its most recent 
2018 was a breakout year for AI and machine learning. Practically every vendor has or is working on an AI story, and businesses are looking for the easiest ways to embrace and benefit from the technology as soon as possible to maintain their competitive edge. There have been some amazing and eye-opening stories throughout the year that hint at the future: 
The hyperconverged market continued its meteoric rise and exploded in 2018. Virtually every vendor saw growth in their product offerings. The technology is being embraced across all verticals and use cases, and for customers, its yielding real business benefits. With the expectation that market adoption will continue to rise in 2019, here are a few things I’m expecting to impact the HCI market.