The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products and services that are available from data and analytics vendors. Data platform providers, both operational and analytic, have had to adapt to changing customer demand. The initial response — making existing products available for deployment on cloud infrastructure — only scratched the surface in terms of responding to emerging expectations. We now see the next generation of products, designed specifically to deliver innovation by taking advantage of cloud-native architecture, being brought to market both by emerging startups, and established vendors, including InterSystems.
Topics: business intelligence, Cloud Computing, Data Management, Data, natural language processing, data operations, AI & Machine Learning, Analytics & Data, analytic data platforms, Operational Data Platforms
There is always space for innovation in the data platforms sector, and new vendors continue to emerge at regular intervals with new approaches designed to serve specialist data storage and processing requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established vendors, especially for the most demanding operational or analytic data platform requirements. It is never easy, however, for developers of new data platform products to gain significant market traction, given the dominance of the established relational database vendors and cloud providers. Targeting requirements that are not well-served by general purpose data platforms can help new vendors get a toe in the door of customer accounts. The challenge to gaining further market traction is for new vendors to avoid having products become pigeon-holed as only being suitable for a niche set of requirements. This is precisely the problem facing the various distributed SQL database providers.
Topics: Cloud Computing, Data, Operational Data Platforms
Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors, including Monte Carlo Data, have emerged in recent years with the goal of increasing the productivity of data teams and improving organizations’ trust in data using automation and artificial intelligence and machine learning (AI/ML).
Topics: business intelligence, Cloud Computing, Data Management, Data, data operations
One of the most significant considerations when choosing an analytic data platform is performance. As organizations compete to benefit most from being data-driven, the lower the time to insight the better. As data practitioners have learnt over time, however, lowering time to insight is about more than just high-performance queries. There are opportunities to improve time to insight throughout the analytics life cycle, which starts with data ingestion and integration, includes data preparation and data management, as well as data storage and processing, and ends with data visualization and analysis. Vendors focused on delivering the highest levels of analytic performance, such as SQream, understand that lowering time to insight relies on accelerating every aspect of that life cycle.
Topics: business intelligence, Data, data operations, AI & Machine Learning, analytic data platforms
Organizations are increasingly utilizing cloud object storage as the foundation for analytic initiatives. There are multiple advantages to this approach, not least of which is enabling organizations to keep higher volumes of data relatively inexpensively, increasing the amount of data queried in analytics initiatives. I assert that by 2024, 6 in ten organizations will use cloud-based technology as the primary analytics data platform, making it easier to adopt and scale operations as necessary.
Topics: Teradata, Data Governance, Data Management, Data, analytic data platforms, operational data plaftforms, Object storage, vantage platforms