The new paradigm in AI Strategy : Cloud native solutions & platforms for accentuated ROI

Enterprises today recognize that cloud is integral to their AI transformation journey . Cloud adoption is growing exponentially, and it is a key enabler to every AI transformation. As enterprises move to the cloud for agility, scalability, and flexibility, they are adopting cloud-native, intelligent, and automated solutions and multi-cloud platforms. Over 80% of workloads are expected to be run in the cloud in 2021 and the same number are expected to move between clouds. At the same time, enterprises moving to the cloud are consolidating and modernizing their traditional on-premises data warehouses and data lakes to take advantage of cost savings and operational efficiencies of the cloud. Cloud and AI strategy go hand-in-hand. A recent study by Deloitte found that 65% of C-level respondents see cloud strategy as key driver for AI led transformation .

To support their AI in cloud initiatives, enterprises are adopting cloud data warehouses, data lakes, and lakehouses (which combine the benefits of cloud data warehouses and data lakes into one data platform). But common data management mistakes can hinder organizations on their journey to analytics modernization and ROI. This is also true for enterprises starting new data warehouses and data lakes in the cloud, whether for the first time or for business functions. The good news is that proven, cloud-native and automated cloud data management strategies for modernizing analytics in the cloud are available to accelerate your journey and finally realize the expected ROI from your investments.

The AI landscape has recently undergone tremendous changes. For several years, enterprises relied on data marts and on-premises data warehouses to power business intelligence (BI) and reporting systems. With the advent of big data, enterprises added Hadoop-based data lakes. New technologies— such as EMR, Hive, and Spark—eventually followed. These changes paved the way for faster analytics and insights. But operational complexity, maintenance, and cost considerations remained challenges. Businesses that depended on analytics coming from on-premises data warehouses and data lakes went through an arduous phase of expectations versus reality when it came to time to value and ROI. Millions of dollars were spent trying to address serious data quality and data management challenges. Data lakes very quickly became known as data swamps. Organizations that succeeded were those that decided to tackle these challenges head-on with intelligent, automated data management. Now with the promise of agility, scalability, and cost savings, organizations are turning to cloud-native data lakes, data warehouses, and lakehouses. Yet the data management and data quality issues that affected on-premises data warehouses and data lakes remain barriers to success today. According to a latest survey, a majority (64%) of enterprises surveyed believe that data quality and data management issues are the leading barriers to successfully delivering cloud data warehouses and data lakes. In the same survey, 86% responded that a systematic approach to cloud data management is important to the success of their data strategy. Enterprises can avoid these barriers by taking a cloud-native, intelligent, and automated approach to cloud data management. Those that do so will be well-positioned to realize the full possibilities of their cloud data warehouses, data lakes, and lakehouses—gaining analytics insights into past performance as well as predictions for the future.

Enterprises often struggled to accelerate time to value and maximize ROI from their on-premises data warehouses and data lakes. As AI in the cloud gets mainstream , it’s essential to avoid three common mistakes :

Using manual hand coding to hydrate and process data in cloud data warehouses, data lakes, and lakehouses: Organizations may turn to hand coding for prototyping, but in the long run hand coding is insufficient to address enterprise requirements of scale and maintainability. Hand coding falls short when it comes to data integration capabilities for building high-performance data pipelines to ingest and prepare data for analysis. Code isn’t reusable as the underlying technology changes— meaning that you have to re engineer and recode every time there’s a change to technology, platform, or processing engine. Hand coding also fails to ensure data quality and doesn’t provide metadata management to help you discover, catalog, and understand how your data moves through the organization. Over time, hand coding is more expensive, time consuming, and riskier than using an intelligent and automated solution that doesn’t require coding.

Depending on disjointed products to achieve end-to-end data management: Using multiple, non-integrated products increases complexity as well as cost. It can take up to 10 separate products to achieve the end-to-end data management you need for modern cloud data management. And stitching together disjointed products means that you must constantly deal with changing roadmaps, cost and time overruns, and–most significantly– inconsistent data governance and quality.

Relying on limited solutions from cloud vendors that only offer basic data integration or ingestion and don’t work across clouds: Although offerings from platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS) vendors are designed for the cloud, they tend to have both of the above downsides. They typically offer basic data integration and ingestion, are reliant on hand-coded development, and provide capabilities that extend only as far as their own platforms, making them inadequate for today’s multi-cloud environments. Cloud data management requirements for modern enterprises must extend beyond any single PaaS to a multi-cloud strategy and deployment model.

(This is first in the CXO insights series of AI & Cloud Strategy )

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